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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 31-42, 44-50 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without reciting significantly more. Independent claim 31 recites “identify from the library a stored representation of electrical activation based on similarity to the patient representation of electrical activation” falls under the grouping of Mental Processes because a physician can visually compare the patients electrical activation such as electrocardiogram with previously stored ECG’s, which is routinely done in clinical settings to track changes in the heart’s electrical activity over time. The limitations “access a patient representation of electrical activation of a patient’s heart; access a library of stored representations of electrical activation of hearts that are each associated with a source location of an arrhythmia; output an indication of the source location of the arrhythmia associated with the identified stored representation of electrical activation” are merely data gathering steps and data output steps, which are insignificant extra solution activities. The recitation of a processor to perform the steps is merely a generic computer upon which the abstract idea is implemented. The claim does not recite additional limitations that would integrate the abstract idea into a practical application nor does it provide an inventive concept.
Dependent claim 32 recites “wherein the arrhythmia is an atrial fibrillation or a ventricular fibrillation” which is a condition that can be mentally determined by a physician by visually comparing and analyzing the ECG of a patient.
Dependent claim 33 recites “wherein the patient representation of electrical activation and the stored representations of electrical active are an electrocardiogram or a vectorcardiogram” is merely a reference to the electrical activation which is data that is gathered, which is an insignificant extra solution activity as noted above.
Dependent claim 34 recites “wherein the stored representations of electrical activation are derived from simulated electrical activations of computational models” which is merely a data gathering step, which is an insignificant extra solution activity. Moreover, the simulation appears to be performed on a generic computer. The claim is described at a high level of generality which is indicative that an inventive concept is not present.
Dependent claim 35 recites “wherein a computational model includes a mapping of electrical activation of a heart” merely describes a computational model that is implemented on a generic computer, and is described at a high level of generality which is indicative that an inventive concept is not present. Alternatively, the computational model appears to describe a mathematical model for mapping data, which falls under the grouping of Mathematical concepts.
Dependent claim 36 recites “wherein computational model includes a three-dimensional mesh in the shape of a heart” which is merely a model for describing the heart and implemented on a generic computer, and is described at a high level of generality which is indicative that an inventive concept is not present. Alternatively, the computational model appears to describe a mathematical representation of the heart, which falls under the grouping of Mathematical concepts.
Dependent claim 37 recites “wherein the computational models have varying geometries and source location of arrhythmias” which is merely a model for describing the heart and the location of abnormalities, the computation is implemented on a generic computer, and furthermore is described at a high level of generality which is indicative that an inventive concept may not be present.
Dependent claim 38 recites “wherein a first computational model includes one or more source locations and wherein the instructions further generate, based on that computational model, second computational model with one or more of the source locations removed” which appears to be a mathematical operation performed on a generic computer. This limitation is described at a high level of generality which is indicative that an inventive concept is not present.
Dependent claim 39 recites “wherein the instructions further determine a change in the arrhythmia between the first computational model and the second computational model” which appears to be a mathematical operation performed on a generic computer. This limitation is described at a high level of generality which is indicative that an inventive concept is not present.
Dependent claim 40 recites “wherein the instructions further display a representation of a heart with the source location indicated” which is merely a data output step, which is an insignificant extra-solution activity.
Dependent claim 41 recites “wherein the identification is based on correlation factor between patient representation of electrical activation and the stored representation of electrical activation” which falls under the grouping of Mental Processes because a physician can visually compare the patient ECG with stored ECG and determine a correlation, which is routinely done in clinical settings to track changes in the heart’s electrical activity over time.
With regard to claim 42, see discussion of claim 31.
With regard to claim 45, see discussion of claim 33.
With regard to claim 47, see discussion of claim 34.
With regard to claim 49, see discussion of claim 31.
With regard to claim 50, see discussion of claim 34.
With regard to claim 48, the claim recites “wherein the identifying includes applying a machine learning algorithm to identify a source location, the machine learning algorithm being trained using the stored representations of electrical activation and the associated source locations”. The claim does not provide any details about how machine learning algorithm operates or how the identification is made, nor does it provide any details on how the machine learning algorithm is trained. Under the broadest reasonable interpretation, the “identification” encompasses mental observation or evaluation that are practically performed in the human mind. Moreover, using the trained machine learning algorithm provide nothing more than mere instructions to implement an abstract idea on a generic computer. The training and identification are recited at a high level of generality which is indicative that an inventive concept may not be present.
With regard to claim 44, the claim recites “wherein source locations are identified for multiple cycles of the patient representation of electrical activation” which falls under the grouping of Mental Processes because a physician can visually evaluate and analyze the ECG’s to identify source locations by comparing with known or previously stored ECG’s that have known source locations. The claim further recites “wherein the graphical representation further indicates, for one or more areas of the heart, a coding that is based on a percent of source locations that are within that area” where the graphical representation, as noted above, is a data output step to display a graphical representation and additional information, which is merely an insignificant extra-solution activity.
With regard to claim 46, the claim recites “wherein the patient representation of electrical activation is a patient electrocardiogram and the stored representations of electrical activation are stored vectorcardiograms and further comprising converting the patient electrocardiogram to a patient vectorcardiogram” which falls under the grouping of Mathematical concepts because the conversion is performed through well known mathematical transformation.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 31-37, 40-42, 45-47, 49-50 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by D11 or, in the alternative, under 35 U.S.C. 103 as obvious over D2. 2
With regard to claim 31, D1 teach one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems and one or more processors for controlling the one or more computing systems to execute one or more of the computer-executable instructions (see D1 abstract: Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram inherently performed on a computer); access a patient representation of electrical activation of a patient’s heart (abstract, § 3.3 ¶ 1); access a library of stored representations of electrical activation of hearts that are each associated with a source location of an see abstract, § 2.4: database of simulated representations of activation sequences compared with measured representation to find the best match, wherein the simulations are associated with stimulus sites or source location; and output an indication of the source location of the arrhythmia associated with the identified stored representation of electrical activation (see fig. 4-5).
D1 does not explicitly teach fibrillation (or arrhythmia) source or fibrillation activation sequence, but teach simulating activation sequence comprising all possible parameter combinations at various source or stimulus sites and therefore anticipates the claim.
Alternatively, D2 teach simulating electrical activation with one or more fibrillation sources (abstract, fig. 1: different patterns of atrial fibrillation or arrhythmia induced at different source locations).
Based on the combined teachings, one skilled in the art would have found it obvious to incorporate known teachings of inducing atrial fibrillation as taught by D2 in to the configuration of D1 yielding predictable results. Specifically, the activation sequence and stimulus location in D1 may be substituted with the source and activation pattern disclosed by D2 in order to evaluate heart condition related to fibrillation.
With regard to claim 32, D2 teach wherein the arrhythmia is an atrial fibrillation or a ventricular fibrillation (see abstract, fig. 1: different patterns of atrial fibrillation or arrhythmia induced at different source locations).
With regard to claim 33, D1 teach wherein the patient representation of electrical activation and the stored representations of electrical active are an electrocardiogram or a vectorcardiogram (§§ 2.2, 3.2: vectocardiograms (VCG) determined for simulations and generated representation).
With regard to claim 34, D1 teach wherein the stored representations of electrical activation are derived from simulated electrical activations of computational models (see abstract, § 2.4: database of simulated representations of activation sequences compared with measured representation to find the best match, wherein the simulations are associated with stimulus sites or source location; see § 2.4 ¶ 1: stimulus sites sampled at 118 locations including RV endocardium; fig. 1-C2).
With regard to claim 35, D1 teach wherein a computational model includes a mapping of electrical activation of a heart (see abstract, § 2.4: database of simulated representations of activation sequences).
With regard to claim 36, D1 teach wherein computational model includes a three-dimensional mesh in the shape of a heart (abstract, § 2.3.1: finite element meshes of heart; §§ 2.4, 3.2-3.3, fig. 1).
With regard to claim 37, D1 teach wherein the computational models have varying geometries and source location of arrhythmias (abstract, § 2.4: plurality of simulations at different stimulus locations; see abstract: patient specific computational models having varying heart geometries).
With regard to claim 40, D1 teach wherein the instructions further display a representation of a heart with the source location indicated (see figs. 4-5).
With regard to claim 41, D1 teach wherein the identification is based on correlation factor between patient representation of electrical activation and the stored representation of electrical activation (§ 2.4 ¶ 1, §§ 3.2-3.3: comparing with the simulated representations to find the best match).
With regard to claim 42, see discussion of claim 31.
With regard to claim 45, see discussion of claim 33.
With regard to claim 46, D1 teach wherein the patient representation of electrical activation is a patient electrocardiogram and the stored representations of electrical activation are stored vectorcardiograms and further comprising converting the patient electrocardiogram to a patient vectorcardiogram (§§ 2.2, 3.2: vectocardiograms (VCG) determined for simulations and generated representation, patient VCG derived from ECG).
With regard to claim 47, D1 teach wherein the stored representations of electrical activation are derived from simulated electrical activations of computational models (§§ 2.2, 3.2: vectocardiograms (VCG) determined for simulations and generated representation).
With regard to claim 49, see discussion of claim 31.
With regard to claim 50, see discussion of claim 34.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 43-44 are rejected under 35 U.S.C. 103 as being unpatentable over D1 and D2.
With regard to claim 43, D1 fails to explicitly teach performing an ablation procedure on the patient targeting the source location of the arrhythmia, however Examiner takes Official Notice to the fact that performing ablation procedure is extremely well known in the art before the effective filing date and that one skilled in the art would have found it obvious to perform ablation procedure after determining the source location yielding predictable results. The motivation would have been to treat a patient with arrhythmia.
With regard to claim 44, D1 teaches wherein source locations are identified for multiple cycles of the patient representation of electrical activation and wherein the graphical representation further indicates, for one or more areas of the heart, a coding that is based on a see abstract, fig. 2-3: source locations implicitly determined for plurality of cycles; fig. 5: plot of source locations within plurality of regions).
D1 does not explicitly teach displaying a percent value, but does teach displaying a graphical representation of the source locations within plurality of regions (see fig. 5). One skilled in the art would have found it obvious to provide an alternative numerical percent value indicating the percentage of source locations within an area. The motivation would have been to help the physician quickly visualize and determine areas of priority with significant source locations, for example.
Claim 48 is rejected under 35 U.S.C. 103 as being unpatentable over D1 and D2 and further in view of D3.3
With regard to claim 48, D1 fails to explicitly teach wherein the identifying includes applying a machine learning algorithm to identify a source location, the machine learning algorithm being trained using the stored representations of electrical activation and the associated source locations, however D3 teach the missing features (see abstract, fig. 1: training a machine algorithm to recognize cardiac electrical activity).
One skilled in the art before the effective filing date would have found it obvious to combine the teachings to arrive at the claimed invention. In particular, it would have been obvious to incorporate known teachings of applying machine learning algorithm to analyze cardiogram to detect arrhythmia or abnormality as taught by D3 into the configuration of D1 yielding predictable results. The motivation for incorporating machine learning algorithm would have been to improve detection of abnormalities.
Claims 38-39 are 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.
Conclusion
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/AVINASH YENTRAPATI/Primary Examiner, Art Unit 2672
1 Villongco, Christopher T., et al. "Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram in bundle branch block." Progress in biophysics and molecular biology 115.2-3 (2014): 305-313.
2 Tobón, Catalina, et al. "Dominant frequency and organization index maps in a realistic three-dimensional computational model of atrial fibrillation." Europace 14.suppl_5 (2012): v25-v32.
3 Carrault, Guy, et al. "Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms." Artificial intelligence in medicine 28.3 (2003): 231-263.