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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites the limitation "implements the steps of" in line 2. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret as “implements steps of” and suggests amending.
Claims 11-12 are rejected based on their dependency on claim 10.
Claim 13 recites the limitation "implements the step of" in line 2. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret as “implements a step of” and suggests amending.
Claim 14 is rejected based on its dependency on claim 10.
Claim 15 recites the limitation "implements the step of" in line 2. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret as “implements a step of” and suggests amending.
Claim 16 recites the limitation "implements the step of" in line 2. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret as “implements a step of” and suggests amending.
Claim 17 recites the limitation "implements the step of" in line 2. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret as “implements a step of” and suggests amending.
Claim 18 is rejected based on its dependency on claim 17.
Claim 19 recites the limitation "implements the steps of" in line 4. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret as “implements steps of” and suggests amending.
Claim 19 recites the limitation "An electronic device comprising the system of claim 1 and/or a readable storage medium" in lines 1-2. This limitation is unclear. By stating an “OR” limitation, if one were to select the system of claim 1 OR a readable storage medium, that's a difference between a dependent claim or an independent one. Examiner will interpret as “An electronic device comprising the system of claim 1 and a readable storage medium” and suggests amending.
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.
Claim 10 is rejected under 35 U.S.C. 101 because
Claim 10 is directed towards a computer readable medium, however, under broadest reasonable interpretation the computer readable medium includes transitory media. While the specification gives example, it does not exclude the transitory media. Therefore, the United States Patent and Trademark Office is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO, see In re Zletz, 893 F.2d 319 (Fed. Cir. 1989).
The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particular when the specification is unclear. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter). Thus, claim 10 is rejected as being directed to non-statutory subject matter. Examiner suggests amending to include “non-transitory” language.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 6, 9-12, 15-16, and 19 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by EP 3821801 Xia et al., hereinafter “Xia”.
Regarding claim 1, Xia discloses a system for predicting an origin position of a ventricular arrhythmia (VA) (Abstract and Figure 3), comprising: an acquisition module (Figure 1, element 110) for acquiring a body-surface electrocardiogram (ECG) to be subjected to prediction (Para 41); and a prediction module (Figure 1, element 130) for performing stage-wise prediction on the body-surface ECG using at least two stages of pre-trained prediction models (Figure 4 and Para 91-92, Figure 1, element 131 and Para 62), thereby obtaining prediction results corresponding to the origin position of the VA (Para 103 and Figure 5, element S360).
Regarding claim 2, Xia discloses the prediction module comprises: a first prediction sub-module for performing a first-stage prediction process on the body-surface ECG using a pre-trained first prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module), thereby obtaining a first- stage prediction result corresponding to the origin position of the VA (Figure 4, element S230 and Para 91); a second prediction sub-module for performing, based on the first-stage prediction, a second-stage prediction process on the body-surface ECG using a pre-trained second prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83), thereby obtaining a second-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230); and a third prediction sub-module for performing, based on the second-stage prediction, a third-stage prediction process on the body-surface ECG using a pre-trained third prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83), thereby obtaining a third-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230).
Regarding claim 3, Xia discloses the prediction module further comprises: a fourth prediction sub-module for performing, based on the third-stage prediction, a fourth-stage prediction process on the body-surface ECG using a pre-trained fourth prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83), thereby obtaining a fourth-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230).
Regarding claim 6, Xia discloses a first pre-treatment module for determining whether a sampling frequency of the body-surface ECG is equal to a predetermined frequency and, if not, resampling the body-surface ECG (Para 43).
Regarding claim 9, Xia discloses a report generation module for displaying the prediction results corresponding to the origin position of the VA (Para 110 and Figure 6, element 230) on a predefined three-dimensional (3D) heart model, thereby generating a 3D prediction report (Para 106).
Regarding claim 10, Xia discloses a readable storage medium , storing therein a computer program, wherein the computer program, when executed by a processor (Para 30-31), implements steps of: acquiring a body-surface electrocardiogram (ECG) to be subjected to prediction (Para 41); and performing stage-wise prediction on the body-surface ECG using at least two stages of pre-trained prediction models (Figure 4 and Para 91-92, Figure 1, element 131 and Para 62), thereby obtaining prediction results corresponding to the origin position of the VA (Para 103 and Figure 5, element S360).
Regarding claim 11, Xia discloses performing the stage-wise prediction on the body-surface ECG using the at least two stages of pre-trained prediction models (Figure 4 and Para 91-92, Figure 1, element 131 and Para 62) and thereby obtaining the prediction results corresponding to the origin position of the VA (Para 103 and Figure 5, element S360) comprises: performing a first-stage prediction process on the body-surface ECG using a pre- trained first prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module), thereby obtaining a first-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230 and Para 91); performing, based on the first-stage prediction, a second-stage prediction process on the body-surface ECG using a pre-trained second prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83), thereby obtaining a second-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230); and performing, based on the second-stage prediction, a third-stage prediction process on the body-surface ECG using a pre-trained third prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83), thereby obtaining a third-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230).
Regarding claim 12, Xia discloses performing the stage-wise prediction on the body-surface ECG using the at least two stages of pre-trained prediction models and thereby obtaining the prediction results corresponding to the origin position of the VA further comprises: performing, based on the third-stage prediction, a fourth-stage prediction process on the body-surface ECG using a pre-trained fourth prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83), thereby obtaining a fourth-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230).
Regarding claim 15, Xia discloses the computer program, when executed by the processor, further implements step of: displaying the prediction results corresponding to the origin position of the VA (Para 110 and Figure 6, element 230) on a predefined three-dimensional (3D) heart model, thereby generating a 3D prediction report (Para 106).
Regarding claim 16, Xia discloses the computer program, when executed by the processor, further implements step of: determining whether a sampling frequency of the body-surface ECG is equal to a predetermined frequency and, if not, resampling the body-surface ECG (Para 43).
Regarding claim 19, Xia discloses an electronic device comprising the system of claim 1 and a readable storage medium, the readable storage medium storing therein a computer program, wherein the computer program, when executed by a processor (Para 30-31), implements steps of: acquiring a body-surface electrocardiogram (ECG) to be subjected to prediction (Para 41); and performing stage-wise prediction on the body-surface ECG using at least two stages of pre-trained prediction models (Figure 4 and Para 91-92, Figure 1, element 131 and Para 62), thereby obtaining prediction results corresponding to the origin position of the VA (Para 103 and Figure 5, element S360).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4-5 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over EP 3821801 Xia et al., hereinafter “Xia”, in view of US 2019/0090774 Yang et al., hereinafter “Yang”.
Regarding claim 4, Xia discloses a QRS complex extraction module for determining locations of all QRS complexes the first prediction sub-module is configured to perform (Para 8) by detecting the body-surface ECG using a pre- trained first machine learning model (Para 8 and 69), the first-stage prediction process on the body-surface ECG using the pre-trained first prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module), thereby obtaining the first-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230 and Para 91); the second prediction sub-module is configured to perform, based on the locations of the QRS complexes and the first-stage prediction, the second- stage prediction process on the body-surface ECG using the pre-trained second prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83), thereby obtaining the second-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230); the third prediction sub-module is configured to perform, based on the locations of the QRS complexes and the second-stage prediction, the third- stage prediction process on the body-surface ECG using the pre-trained third prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83), thereby obtaining the third-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230); and the fourth prediction sub-module is configured to perform, based on the locations of the QRS complexes and the third-stage prediction, the fourth- stage prediction process on the body-surface ECG using the pre-trained fourth prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83), thereby obtaining the fourth-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230).
Xia does not disclose a QRS complex extraction module for determining locations of all QRS complexes and locations of QRS complexes during premature ventricular contractions (PVCs) or ventricular tachycardias (VTs); wherein: the first prediction sub-module is configured to perform, based on the locations of the QRS complexes during the PVCs or VTs, the first-stage prediction process on the body-surface ECG using the pre-trained first prediction model, thereby obtaining the first-stage prediction result corresponding to the origin position of the VA.
However, Yang discloses a system/method of localizing an arrhythmia (Abstract) and teaches a QRS complex extraction module for determining locations of all QRS complexes and locations of QRS complexes during premature ventricular contractions (PVCs) or ventricular tachycardias (VTs) (Para 45); wherein: the first prediction sub-module is configured to perform, based on the locations of the QRS complexes during the PVCs or VTs, the first-stage prediction process on the body-surface ECG using the pre-trained first prediction model (Para 57), thereby obtaining the first-stage prediction result corresponding to the origin position of the VA (Para 57).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed determining locations of all QRS complexes during premature ventricular contractions (PVCs) or ventricular tachycardias (VTs) as taught by Yang, in the invention of Xia, in order to help guide ablation procedure by providing potential source locations throughout the ventricles (Yang; Para 57).
Regarding claim 5, Xia discloses the first prediction sub-module comprises: a first extraction unit for extracting a first target ECG portion of a first target length from the body-surface ECG according to the locations of the QRS complexes during the PVCs or VTs (Para 42-43 and Figure 1, element 110 and Figure 3); and a first prediction unit (Figure 1, element 122) for performing the first-stage prediction process on the first target ECG portion using the pre-trained first prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module), thereby obtaining the first-stage prediction result corresponding to the origin position of the VA (Figure 4, element S230 and Para 91) and/or the second prediction sub-module comprises: a second extraction unit for extracting a second target ECG portion of a second target length from the body-surface ECG according to the locations of the QRS complexes during the PVCs or VTs; and a second prediction unit for performing, based on the first-stage prediction, the second-stage prediction process on the second target ECG portion using the pre-trained second prediction model, thereby obtaining the second-stage prediction result corresponding to the origin position of the VA, and/or the third prediction sub-module comprises: a third extraction unit for extracting a third target ECG portion of a third target length from the body-surface ECG according to the locations of the QRS complexes during the PVCs or VTs; and a third prediction unit for performing, based on the second-stage prediction, the third-stage prediction process on the third target ECG portion using the pre-trained third prediction model, thereby obtaining the third-stage prediction result corresponding to the origin position of the VA, and/or the fourth prediction sub-module comprises: a fourth extraction unit for extracting a fourth target ECG portion of a fourth target length from the body-surface ECG according to the locations of the QRS complexes during the PVCs or VTs; and a fourth prediction unit for performing, based on the third-stage prediction, the fourth-stage prediction process on the fourth target ECG portion using the pre-trained fourth prediction model, thereby obtaining the fourth-stage prediction result corresponding to the origin position of the VA (The rest of the claim is full of “OR” limitations so none of the rest of the subunits are required by prior art).
Regarding claim 13, Xia discloses the computer program, when executed by the processor (Para 30-31), further implements a step of: determining locations of all QRS complexes (Para 8) by detecting the body-surface ECG using a pre-trained first machine learning model (Para 8 and 69), performing the first-stage prediction process on the body-surface ECG using the pre-trained first prediction model comprises: performing, based on the locations of the QRS complexes, the first-stage prediction process on the body-surface ECG using the pre-trained first prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module); performing the second-stage prediction process on the body-surface ECG using the pre-trained second prediction model comprises: performing, based on the locations of the QRS complexes and the first-stage prediction, the second-stage prediction process on the body-surface ECG using the pre-trained second prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83); performing the third-stage prediction process on the body-surface ECG based on the second-stage prediction using the pre-trained third prediction model comprises: performing, based on the locations of the QRS complexes and the second-stage prediction, the third-stage prediction process on the body-surface ECG using the pre-trained third prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83); and performing the fourth-stage prediction process on the body-surface ECG based on the third-stage prediction using the pre-trained fourth prediction model comprises: performing, based on the locations of the QRS complexes and the third-stage prediction, the fourth-stage prediction process on the body-surface ECG using the pre-trained fourth prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module, see claim 17 and Para 83).
Xia does not disclose determining locations of all QRS complexes and locations of QRS complexes during premature ventricular contractions (PVCs) or ventricular tachycardias (VTs); wherein: the first prediction sub-module is configured to perform, based on the locations of the QRS complexes during the PVCs or VTs, the first-stage prediction process on the body-surface ECG using the pre-trained first prediction model.
However, Yang discloses a system/method of localizing an arrhythmia (Abstract) and teaches determining locations of all QRS complexes and locations of QRS complexes during premature ventricular contractions (PVCs) or ventricular tachycardias (VTs) (Para 45); wherein: the first prediction sub-module is configured to perform, based on the locations of the QRS complexes during the PVCs or VTs, the first-stage prediction process on the body-surface ECG using the pre-trained first prediction model (Para 57).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed determining locations of all QRS complexes during premature ventricular contractions (PVCs) or ventricular tachycardias (VTs) as taught by Yang, in the invention of Xia, in order to help guide ablation procedure by providing potential source locations throughout the ventricles (Yang; Para 57).
Regarding claim 14, Xia discloses performing the first-stage prediction process on the body-surface ECG using the pre-trained first prediction model based on the locations of the QRS complexes during the PVCs or VTs (Para 42-43 and Figure 1, element 110 and Figure 3) comprises: extracting a first target ECG portion of a first target length from the body-surface ECG according to the locations of the QRS complexes during the PVCs or VTs; and performing the first-stage prediction process on the first target ECG portion using the pre-trained first prediction model (Para 91 and Figure 4; a submodule within a model under BRI could mean a plurality of different things. Under BRI each weighing function is a sub-module), and/or performing the second-stage prediction process on the body-surface ECG using the pre-trained second prediction model based on the locations of the QRS complexes during the PVCs or VTs and the first-stage prediction comprises: extracting a second target ECG portion of a second target length from the body- surface ECG according to the locations of the QRS complexes during the PVCs or VTs; and performing, based on the first-stage prediction, the second-stage prediction process on the second target ECG portion using the pre-trained second prediction model, thereby obtaining the second-stage prediction result corresponding to the origin position of the VA, and/or performing the third-stage prediction process on the body-surface ECG using the pre-trained third prediction model based on the locations of the QRS complexes during the PVCs or VTs and the second-stage prediction comprises: extracting a third target ECG portion of a third target length from the body-surface ECG according to the locations of the QRS complexes during the PVCs or VTs; and performing, based on the second-stage prediction, the third-stage prediction process on the third target ECG portion using the pre-trained third prediction model, thereby obtaining the third-stage prediction result corresponding to the origin position of the VA, and/or performing the fourth-stage prediction process on the body-surface ECG using the pre-trained fourth prediction model based on the locations of the QRS complexes during the PVCs or VTs and the third-stage prediction comprises: extracting a fourth target ECG portion of a fourth target length from the body- surface ECG according to the locations of the QRS complexes during the PVCs or VTs; and performing, based on the third-stage prediction, the fourth-stage prediction process on the fourth target ECG portion using the pre-trained fourth prediction model, thereby obtaining the fourth-stage prediction result corresponding to the origin position of the VA (The rest of the claim is full of “OR” limitations so none of the rest of the subunits are required by prior art).
Claim(s) 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over EP 3821801 Xia et al., hereinafter “Xia”, in view of US 2004/0230129 Haefner, hereinafter “Haefner”.
Regarding claim 7, Xia discloses all the limitations of claim 1.
Xia does not disclose a second pre-treatment module for denoising the body-surface ECG.
However, Haefner discloses an arrhythmia discrimination device/method (Abstract) and teaches a second pre-treatment module for denoising the body-surface ECG (Para 49).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed noise reduction as taught by Haefner, in the invention of Xia, in order to improve the SNR of ECG (Haefner; Para 49).
Regarding claim 17, Xia discloses the computer program, when executed by the processor (Para 30-31).
Xia does not disclose denoising the body-surface ECG.
However, Haefner discloses an arrhythmia discrimination device/method (Abstract) and teaches denoising the body-surface ECG (Para 49).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed noise reduction as taught by Haefner, in the invention of Xia, in order to improve the SNR of ECG (Haefner; Para 49).
Allowable Subject Matter
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 18 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Reason for Allowance
The following is an examiner’s statement of reasons for allowance:
Examiner has found a primary reference EP 3821801 Xia et al. that discloses a system and method for predicting an origin position of a ventricular arrhythmia. The reference discloses an acquisition module that acquires ECG data and uses a prediction model to obtain results regarding a predicted location. Examiner has also found references like US 2004/0230129 Haefner that disclose denoising a signal. However, no references were found to disclose removing high- frequency noise by hierarchical time-frequency resolution and remove low-frequency noise by non-linear fitting. For this reason, claims 8 and 18 are objected to as allowable.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYA ZIAD BAKKAR whose telephone number is (313)446-6659. The examiner can normally be reached on 7:30 am - 5:00 pm M-Th.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carl Layno can be reached on (571) 272-4949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AYA ZIAD BAKKAR/
Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796