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 1-15 are rejected under 35 U.S.C. 101 because the claims are directed to a mental process without significantly more.
To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is evaluate according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.05. The instant claims are evaluated according to such analysis.
Step 1: Independent claims 1, 10, and 14 recite a method, device, and system for an ambulatory ECG device.
STEP 2A, PRONG 1:
Claims 1, 10, and 14 recite a mental process including the steps such as:
determining, by a processor of the ambulatory ECG device, whether the obtained ECG signal comprises ECG beat locations.
pre-processing, by the processor, the obtained ECG signal with a de-trending analysis to generate a pre-processed ECG signal if the obtained ECG signal is determined to comprise ECG beat locations, or pre-processing, by the processor, the obtained ECG signal with a bandpass filter to generate a pre-processed ECG signal if the obtained ECG signal is determined to not comprise ECG beat locations;
approximating, by the processor, the pre-processed ECG signal to generate an approximated ECG signal;
quantizing, by the processor, the approximated ECG signal to generate a quantized ECG signal.
generating, by a compression algorithm of the processor, a compressed ECG signal from the quantized ECG signal.
Transmitting, by the ambulatory ECG device, the compressed ECG signal.
Reconstructing the compressed ECG signal to generate a reconstructed approximated ECG signal.
Transforming, by a trained neural network, the reconstructed approximated ECG signal to generate a decompressed ECG signal.
The steps above, under broadest reasonable interpretation, can be performed by mental process steps because they can be practically performed in the human mind. For instance, following the collection of ECG data by the ambulatory device, the ECG data can be evaluated to determine one or more beat locations, and following the identification of one or more beat locations, the ECG signal can be approximated, quantized, and compressed by and compression algorithm, where the compressed signal is later transmitted for further evaluation to a remote server.
STEP 2A, Prong 2:
Claims 1, 10, and 14 include the additional elements of:
A processor.
One or more ECG leads configured to obtain an ECG signal from a subject.
A communication interface configured to transmit the compressed ECG signal.
Reciting a processor used to carry out a process merely serves as a tool used to carry out the method. See MPEP 2106.05(f).
One or more leads used to collect ECG data merely adds insignificant extra-solution activity (pre-solution activity). See MPEP 2106.05(g).
A communication interface configured to transmit the compressed ECG signal merely adds insignificant extra-solution activity (pre-solution activity). See MPEP 2106.05(g).
Therefore, the additional element, alone or in combination is not integrated into a practical application, and is therefore directed to an abstract idea.
STEP B:
Claims 1, 10, and 14 include the additional elements of:
A processor. This merely serves as a tool used to carry out the method. See MPEP 2106.05(f).
One or more ECG leads configured to obtain an ECG signal from a subject. This is merely adds insignificant extra-solution activity (pre-solution activity) to the judicial exception. See MPEP 2106.04 (d) and 2106.05(g).
A communication interface configured to transmit the compressed ECG signal. This is merely adds insignificant extra-solution activity to the judicial exception. See MPEP 2106.04 (d) and 2106.05(g).
For transmitting the compressed ECG signal, this step is considered insignificant extra-solution activity, and has been found to be well-understood, routine, and conventional activity in the field. The following evidence below provides support:
See MPEP 2106.05(d) i. (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
These additional elements in claims 1, 10, and 14 identified by the courts, either alone or in combination, have found not to be enough to qualify as “significantly more” than the abstract idea itself when recited in a claim with a judicial exception.
Dependent claims:
Claims 2-9, 11-13, and 15 further define the mental process and abstract idea, therefore failing to amount to “significantly more” than the abstract ideas either alone or in combination as previously stated for the independent claims.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1, 10, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over BRITO, M. et al., "ECG Compression through segment matching and progressive error encoding", 2007 Annual International Conference of The IEEE Engineering in Medicine and Biology Society (2007-08-22-26), pages 135-138 to Brito et al. (hereinafter “Brito”) in view of US 11,083,371 B1 to Szabados et al. (hereinafter “Szaba”).
Regarding claim 1, Brito teaches:
A method for compressing and transmitting an ECG signal device (see col. 1, pg. 135, para. 2 – “it is often necessary to acquire an ECG 24/7 in order to diagnose some types of arrhythmias, which generates considerable amounts of data. From this perspective, the existence of a compression algorithm
becomes an absolute necessity.” And col. 2, pg. 135, para 2 – “The most promising methods of compression, however, are the ones that explore the similarity between the various segments of the ECG……In this work, further work will be done exploring the
similarity between heart cycles in an ECG and a solution will be presented…….The paper will be organized in the following sections: in section 2, the proposed method will be presented, followed by experimental results and discussion in section 3. Section 4 presents some conclusions about the proposed algorithm.”, and co. 2, pg. 137 – “In this case, the progressive error encoding will perform better than the Bezier encoding method, and that output will be transmitted.”),
comprising:
obtaining, by the ECG device, an ECG signal from a subject (“While in some circumstances it will be enough to acquire an ECG for five minutes to be quickly examined by the physician, it is often necessary to acquire an ECG 24/7 in order to diagnose some types of arrhythmias, which generates considerable amounts of data. From this perspective, the existence of a compression algorithm becomes an absolute necessity.”);
determining, by a processor of the (“….A simple algorithm is used in order to switch between our approach and another one which isn't dependent on signal regularity. The following steps are performed: A) Read enough samples to fill the buffer window B) Compute peak vector R with Pan-Tomkins algorithm C) If no peaks detected/ too many peaks detected)..”
pre-processing, by the processor, the obtained ECG signal with a de-trending analysis to generate a pre-processed ECG signal if the obtained ECG signal is determined to comprise ECG beat locations or pre-processing, by the processor, the obtained ECG signal with a bandpass filter to generate a pre-processed ECG signal if the obtained ECG signal is determined to not comprise ECG beat locations (see cols. 1-2, pg. 136 – “3) Normalization……-Segment amplitude: The objective is to i) compensate for baseline wander effects...”);
approximating, by the processor, the pre-processed ECG signal to generate an approximated ECG signal (see col. 2, pg. 136 – “Once the segment is normalized, a best match for S in D must be determined…5) Approximation with Bezier Curves.. The objective of the Bezier Curve [7] decomposition is to achieve acceptable compression ratios while keeping reconstruction error very low. This is important for the error encoding stage of the algorithm, since it is assumed that D contains very accurate approximations of previously encountered patterns.”);
quantizing, by the processor, the approximated ECG signal to generate a quantized ECG signal (see col. 1, pg. 137 – “6) Progressive Error Encoding…….. This progressive error encoding scheme is inspired on the SPIHT algorithm, in the sense that the signal is progressively encoded…… A) Initialize quantizer Q= Qinit, level L = 1…..”);
generating, by a compression algorithm of the processor, a compressed ECG signal from the quantized ECG signal (see col. 1, pg. 138 – “Finally, a 1024 sample window of dataset 103 is displayed for visual inspection. Figure 3 presents several segments compressed with progressive error strategy, and Figure 4 presents the respective reconstruction error.”);
transmitting, by the ECG device, the compressed ECG signal (co. 2, pg. 137 – “In this case, the progressive error encoding will perform better than the Bezier encoding method, and that output will be transmitted.”).
However, Brito does not explicitly disclose wherein the method is an ambulatory method.
However, Szaba teaches embodiments for processing data (through compressing and decompressing) of an executable file on a monitor to reduce the dimensionality of the transmitted data over a wireless network (see abstract, fig. 25, and col. 39, lines 9-21). The method (see title) teaches wherein the method is an ambulatory method for compressing and transmitting an ECG signal by an ambulatory electrocardiogram (ECG) device (see col. 1, lines 32-43 and 65-67, col. 2, lines 1-2, and col. 35, lines 7-28).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Brito with the teachings of Felix to arrive at the claimed invention. Such combination would have led to a reasonable expectation for success, since the prior art of Szaba also teaches utilizing real-time ECG monitoring, data compression, data transmission, and decompression of the ECG data for accelerated and accurate evaluation, diagnosis, and treatment of the patient.
Regarding claim 10, Brito teaches:
A method for compressing and transmitting an ECG signal device (see col. 1, pg. 135, para. 2 – “it is often necessary to acquire an ECG 24/7 in order to diagnose some types of arrhythmias, which generates considerable amounts of data. From this perspective, the existence of a compression algorithm
becomes an absolute necessity.” And col. 2, pg. 135, para 2 – “The most promising methods of compression, however, are the ones that explore the similarity between the various segments of the ECG……In this work, further work will be done exploring the
similarity between heart cycles in an ECG and a solution will be presented…….The paper will be organized in the following sections: in section 2, the proposed method will be presented, followed by experimental results and discussion in section 3. Section 4 presents some conclusions about the proposed algorithm.”, and co. 2, pg. 137 – “In this case, the progressive error encoding will perform better than the Bezier encoding method, and that output will be transmitted.”),
comprising:
obtaining, by the ECG device, an ECG signal from a subject (“While in some circumstances it will be enough to acquire an ECG for five minutes to be quickly examined by the physician, it is often necessary to acquire an ECG 24/7 in order to diagnose some types of arrhythmias, which generates considerable amounts of data. From this perspective, the existence of a compression algorithm becomes an absolute necessity.”);
determining, by a processor of the ECG device, whether the obtained ECG signal comprises ECG beat locations (“….A simple algorithm is used in order to switch between our approach and another one which isn't dependent on signal regularity. The following steps are performed: A) Read enough samples to fill the buffer window B) Compute peak vector R with Pan-Tomkins algorithm C) If no peaks detected/ too many peaks detected)..”
pre-processing, by the processor, the obtained ECG signal with a de-trending analysis to generate a pre-processed ECG signal if the obtained ECG signal is determined to comprise ECG beat locations or pre-processing, by the processor, the obtained ECG signal with a bandpass filter to generate a pre-processed ECG signal if the obtained ECG signal is determined to not comprise ECG beat locations (see cols. 1-2, pg. 136 – “3) Normalization……-Segment amplitude: The objective is to i) compensate for baseline wander effects...”);
approximating, by the processor, the pre-processed ECG signal to generate an approximated ECG signal (see col. 2, pg. 136 – “Once the segment is normalized, a best match for S in D must be determined…5) Approximation with Bezier Curves.. The objective of the Bezier Curve [7] decomposition is to achieve acceptable compression ratios while keeping reconstruction error very low. This is important for the error encoding stage of the algorithm, since it is assumed that D contains very accurate approximations of previously encountered patterns.”);
quantizing, by the processor, the approximated ECG signal to generate a quantized ECG signal (see col. 1, pg. 137 – “6) Progressive Error Encoding…….. This progressive error encoding scheme is inspired on the SPIHT algorithm, in the sense that the signal is progressively encoded…… A) Initialize quantizer Q= Qinit, level L = 1…..”);
generating, by a compression algorithm of the processor, a compressed ECG signal from the quantized ECG signal (see col. 1, pg. 138 – “Finally, a 1024 sample window of dataset 103 is displayed for visual inspection. Figure 3 presents several segments compressed with progressive error strategy, and Figure 4 presents the respective reconstruction error.”);
transmitting, by the ECG device, the compressed ECG signal (co. 2, pg. 137 – “In this case, the progressive error encoding will perform better than the Bezier encoding method, and that output will be transmitted.”).
However, Brito does not explicitly discloses:
An ambulatory electrocardiogram (ECG) device, comprising:
one or more ECG leads configured to obtain an ECG signal from a subject;
and a communications interface configured to transmit the compressed ECG signal.
However, Szaba teaches The system teaches a processor and an ambulatory electrocardiogram (ECG) device (see title, col. 2, lines 13-24 and lines 34-44), comprising one or more ECG leads configured to obtain an ECG signal from a subject (see col. 16, lines 45-58), and
a communications interface/transmitter configured to transmit the compressed ECG signal (see col. 3, lines 21-36 and col. 14, lines 10-18).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Brito with the teachings of Szaba to arrive at the claimed invention. Such combination would have led to a reasonable expectation for success, since the prior art of Szaba also teaches utilizing real-time ECG monitoring, data compression and data transmission of the ECG data for accelerated and accurate evaluation, diagnosis, and treatment of the patient.
Regarding claim 14, Brito teaches:
obtaining, by the ECG device, an ECG signal from a subject (“While in some circumstances it will be enough to acquire an ECG for five minutes to be quickly examined by the physician, it is often necessary to acquire an ECG 24/7 in order to diagnose some types of arrhythmias, which generates considerable amounts of data. From this perspective, the existence of a compression algorithm becomes an absolute necessity.”);
determining, by a processor of the ECG device, whether the obtained ECG signal comprises ECG beat locations (“….A simple algorithm is used in order to switch between our approach and another one which isn't dependent on signal regularity. The following steps are performed: A) Read enough samples to fill the buffer window B) Compute peak vector R with Pan-Tomkins algorithm C) If no peaks detected/ too many peaks detected)..”
pre-processing, by the processor, the obtained ECG signal with a de-trending analysis to generate a pre-processed ECG signal if the obtained ECG signal is determined to comprise ECG beat locations or pre-processing, by the processor, the obtained ECG signal with a bandpass filter to generate a pre-processed ECG signal if the obtained ECG signal is determined to not comprise ECG beat locations (see cols. 1-2, pg. 136 – “3) Normalization……-Segment amplitude: The objective is to i) compensate for baseline wander effects...”);
approximating, by the processor, the pre-processed ECG signal to generate an approximated ECG signal (see col. 2, pg. 136 – “Once the segment is normalized, a best match for S in D must be determined…5) Approximation with Bezier Curves.. The objective of the Bezier Curve [7] decomposition is to achieve acceptable compression ratios while keeping reconstruction error very low. This is important for the error encoding stage of the algorithm, since it is assumed that D contains very accurate approximations of previously encountered patterns.”);
quantizing, by the processor, the approximated ECG signal to generate a quantized ECG signal (see col. 1, pg. 137 – “6) Progressive Error Encoding…….. This progressive error encoding scheme is inspired on the SPIHT algorithm, in the sense that the signal is progressively encoded…… A) Initialize quantizer Q= Qinit, level L = 1…..”);
generating, by a compression algorithm of the processor, a compressed ECG signal from the quantized ECG signal (see col. 1, pg. 138 – “Finally, a 1024 sample window of dataset 103 is displayed for visual inspection. Figure 3 presents several segments compressed with progressive error strategy, and Figure 4 presents the respective reconstruction error.”),
but does not explicitly teach an electrocardiogram (ECG) system, comprising:
an ambulatory ECG device comprising:
one or more ECG leads configured to obtain an ECG signal from a subject;
a processor configured to:
a communications interface configured to transmit the compressed ECG signal;
and
a remote server comprising:
a communications interface configured to receive the transmitted compressed ECG signal , and a processor configured to:
reconstruct the compressed ECG signal to generate a reconstructed approximated ECG signal;
and
transform, by a trained neural network, the reconstructed approximated ECG signal to generate a decompressed ECG signal.
However, Szaba teaches a system containing a processor and an ambulatory electrocardiogram (ECG) device (see title and col. 2, lines 13-24 and lines 34-44), comprising one or more ECG leads configured to obtain an ECG signal from a subject (see col. 16, lines 45-58), and
a communications interface/transmitter configured to transmit the compressed ECG signal (see col. 3, lines 21-36 and col. 14, lines 10-18),
and
a remote server (see cols. 38-39, lines 65-67 and 1-8) comprising:
a communications interface/transmitter configured to receive the transmitted compressed ECG signal (see col. 3, lines 21-36 and col. 14, lines 10-18), and
a processor configured to:
reconstruct the compressed ECG signal to generate a reconstructed approximated ECG signal (see fig. 25, col. 39, lines 9-21);
and
transform, by a trained neural network, the reconstructed approximated ECG signal to generate a decompressed ECG signal (see figs. 25 and 27, col. 39, lines 9-21 and col. 42, lines 9-23).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Brito with the teachings of Felix to arrive at the claimed invention. Such combination would have led to a reasonable expectation for success, since the prior art of Szaba also teaches utilizing real-time ECG monitoring, data compression, data transmission, and decompression of the ECG data for accelerated and accurate evaluation, diagnosis, and treatment of the patient.
Regarding claim 2, Brito as modified teaches:
The method of claim 1, further comprising:
receiving, the transmitted compressed ECG signal,
reconstructing, by a reconstruction algorithm, the compressed ECG signal to generate a reconstructed approximated ECG signal;
transforming, by a trained neural network, the reconstructed approximated ECG signal to generate a decompressed ECG signal (See Szaba - fig. 25, col. 2, lines 52-54, and col. 40, lines 13-23). In some embodiments, the data can be compressed through the first subset of layers, and following compression, in some embodiments, the data can be both reconstructed in the second subset of neural network layers and decompressed to properly encode the data.
Regarding claims 3 and 15, Brito as modified teaches:
The method of claim 2 and the system of claim 14, further comprising the step of displaying at least a portion of the decompressed ECG signal (See Szaba - col. 39, lines 9-21), and wherein the remote server further comprises a user interface configured to display at least a portion of the decompressed ECG signal (See Szaba - fig. 10, co. 16, lines 25-44, col. 37, lines 64-67, and col. 38, lines 1-8).
Regarding claims 4, Brito as modified teaches:
The method of claim 2, wherein the transmitted compressed ECG signal is received, reconstructed, and analyzed by a server remote from the ambulatory ECG device (See Szaba - abstract, fig. 25, col. 2, lines 52-54, and col. 40, lines 13-23). In some embodiments, the data can be compressed through the first subset of layers, and following compression, in some embodiments, the data can be both reconstructed in the second subset of neural network layers and decompressed to properly encode the data.
Claims 5, 7-9, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Brito in view of Szaba, and further in view of 03-07267 ECG Codec Algorithm Detailed Design Document. Braemar, pgs. 1-26, (2020) to Lee.
Regarding claims 5 and 11, Brito as modified teaches:
The method of claim 1 and the device of claim 10, wherein approximating the pre-processed ECG signal to generate an approximated ECG signal comprises a wavelet transform (see col. 26, lines 4-18), but does not explicitly disclose wherein approximating the pre-processed ECG signal to generate an approximated ECG signal also comprises a selection of largest wavelet coefficients and a tolerance determination.
However, Lee teaches wherein approximating the pre-processed ECG signal to generate an approximated ECG signal also comprises a selection of largest wavelet coefficients and a tolerance determination (see pg. 5 of 30, first sentence ‘The approximation step can be broken down into three main components… wavelet transform, selection of largest coefficients and determine tolerance.”).
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 modified teachings of Brito with the teachings of Lee to arrive at the claimed invention. Such modification would improve the system by increasing the precision and accuracy of the compressed signal, allowing for more accurate determination of cardiac abnormalities following transmission and decompression of the ECG signal.
Regarding claims 7, Brito as modified teaches:
The method of claim 1, wherein quantizing the approximated ECG signal to generate a quantized ECG signal comprises quantizing wavelet coefficients of the ECG signal using a quantization parameter (A) ( See Lee – pages 6-7).
Regarding claims 8 and 13, Brito as modified teaches:
The method of claim 1 and the device of claim 10, wherein generating a compressed ECG signal from the quantized ECG signal comprises saving the compressed ECG signal as an HDF5 file (See Lee – page 7).
Regarding claim 9, Brito as modified teaches:
The method of claim 1, wherein the compressed ECG signal further comprises encoded metadata (See Lee – bottom of page 7).
Claims 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Brito in view of Szaba and Lee, and further in view of US 2015/0061891 A1 to Oleson et al. (hereinafter “Oleson”).
Regarding claims 6 and 12, Brito as modified teaches:
The method of claim 5 and the device of claim 11, but does not explicitly disclose wherein a one-second windowed ECG signal around each beat in at least a portion of the obtained ECG signal is utilized to determine the tolerance.
However, Oleson teaches a method and device for monitoring biometric data for an individual that includes sensing the biometric parameter via a sensor affixed to the individual (see abstract). The system (fig. 1) teaches wherein, a windowed ECG signal around each beat in at least a portion of the obtained ECG signal is utilized to determine the tolerance (see para [0050]).
Therefore, to a person of ordinary skill in the art, it would have been obvious to try to analyze a one-second window from the ECG signal using the techniques of Oleson (See MPEP 2143). Doing so may allow for properly identifying and evaluating an ECG signal with a limited amount of noise, allowing for more accurate determination of cardiac the actual cardiac signal and it’s cardiac abnormalities following processing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Felix et al, (US 2016/0150989 A1) teaches a method for efficiently encoding and compressing ECG data optimized for use in an ambulatory ECG monitor device (see abstract).
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/K.J.W./Examiner, Art Unit 3792
/NIKETA PATEL/Supervisory Patent Examiner, Art Unit 3792
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