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 .
Claims 21-22 are newly added.
Claims 1, 3-9 and 11-22 are presented for examination.
The claims and only the claims form the metes and bounds of the invention. “Office personnel are to give claims their broadest reasonable interpretation in light of the supporting disclosure. In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed. Cir. 1997). Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969)” (MPEP p 2100-8, c 2, I 45-48; p 2100-9, c 1, l 1-4). The Examiner has full latitude to interpret each claim in the broadest reasonable sense. The Examiner will reference prior art using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning.
Response to Arguments
Applicant’s remarks/amendment was filed on 15 December 2025.
Applicant’s arguments have been considered but they are not persuasive. However, the Examiner welcomes any suggestion(s) Applicants may have on moving prosecution forward. The Examiner’s contact information is in the Conclusion of this office action.
Applicant argues:
Before compression, Govari's data is presented on a local display immediately so that a physician can select a high-resolution portion to be compressed. However, Govari is silent to any preconditioning (e.g., data manipulation of the ECG signals) before compression. As such, Govari does not disclose the claimed first and second encoding processes, which result in preconditioned time-series data that is then compressed to generate compressed packages of the time series data.
In response, the Examiner submits:
It is not true that “before compression, Govari's data is presented on a local display immediately so that a physician can select a high-resolution portion to be compressed”.
Govari teaches that “electrodes 22 acquire and/or inject signals from and/or to the tissue of heart 23, such as of intra-cardiac ECG” (Govari: at least ¶0034). The signals are encoded before they can be presented on a display.
Govari teaches carry out a second encoding process on the first set of encoded ECG data to generate preconditioned ECG data (Govari: at least ¶0010; “select the one or more time-intervals based on one or more indications that an arrhythmia was recorded during the time-intervals”; ¶0045 further discloses “selects (e.g., on display 26, using interactive means such as a pointing and/or time-interval selection tool) time-intervals 52 (i.e., a portion) of the ECG traces to be compressed at high resolution by processor 28, at a selection of compression in high resolution step 72” and “selects another portion (e.g., a remaining portion) of the data to be compressed at low resolution by processor 28”).
Therefore, it is not also true that “Govari is silent to any preconditioning (e.g., data manipulation of the ECG signals) before compression”.
Applicant further argues:
“Applicant respectfully submits that it is unclear what the Office asserts to be Govari's alleged first and second encoding processes”.
In response, the Examiner submits:
Contrary to Applicant’s allegation above, Govari does teach the recited limitaitons of carrying out a first encoding process that converts the time-series data to a first set of encoded time-series data (Govari: at least ¶0034; “during the mapping procedure, electrodes 22 acquire and/or inject signals from and/or to the tissue of heart 23, such as of intra-cardiac ECG. The respective locations of mapping-electrodes 22 (i.e., where the intra-cardiac ECG signals are measured) are tracked as well, as described below. A processor 28 receives these signals via an electrical interface 35, and processes the information contained in these signals to construct displayable medical data, such as an electro-anatomical map 31 and present ECG traces 40 on a local display 26”; note: signals are encoded and become waveform such as the one shown in Fig. 2); and carrying out a second encoding process that converts the first set of encoded time-series data to preconditioned time-series data (Govari: at least ¶0027 “selection of one or more time-intervals of interest within the time period” and “compress a first portion of the data, which is within the selected time intervals, at a first resolution, compress a second portion of the data, which is outside the selected time intervals, at a second resolution”; ¶0029 further discloses “EP data comprises hundreds of ECG channels that were sampled at 32 bit/sec (bps) each”; ¶0045 further discloses “selectively compressing ECG data” and “… selects (e.g., on display 26, using interactive means such as a pointing and/or time-interval selection tool) time-intervals 52 (i.e., a portion) of the ECG traces to be compressed at high resolution by processor 28, at a selection of compression in high resolution step 72” and “… selects another portion (e.g., a remaining portion) of the data to be compressed at low resolution by processor 28, at a selection of compression in low resolution step 74”).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-9 and 11-14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “carry out a first encoding process on electrocardiogram (ECG) data to generate a first set of encoded ECG data, wherein the first encoding process comprises reducing bias in the ECG data”.
Applicant’s original specification teaches the following:
[0004] “In Example 3, the method of Example 1, wherein the generating the preconditioned ECG data includes reducing bias in the ECG data, wherein the compressing the preconditioned ECG data includes using bit packing.”
[0021] “In Example 18, the system of Example 16, wherein the generating the preconditioned ECG data includes reducing bias in the ECG data, wherein the compressing the preconditioned ECG data includes using bit packing.”
[0072] “A first preconditioning step can be referred to as a first encoding step, and the first preconditioning step can include reducing bias in the ECG data (block 204A in FIG. 4).”
Applicant’s specification teaches, for example, “generating preconditioned ECG data includes (or comprising) reducing bias in ECG data”. However, Applicant’s specification does not appear to teach the feature of “carry out a first encoding process on electrocardiogram (ECG) data to generate a first set of encoded ECG data, wherein the first encoding process comprises reducing bias in the ECG data” recited in amended claim 1.
Claims 3-9 and 11-14 depend from claim 1, and are rejected for the same reason(s) under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ).
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 15 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PGPUB 2020/0051683 by Govari et al. (“Govari”).
As to Claim 15, Govari teaches a method comprising: receiving one or more electronic files that contain strips of time-series data (Govari: at least ¶0008; “received data includes one or more electrocardiogram (ECG) traces”; ¶0027 further discloses “receive (a) data, comprising a medical parameter acquired as a function of time”; ¶0029 further discloses “EP data comprises hundreds of ECG channels that were sampled at 32 bit/sec (bps) each”);
carrying out a first encoding process that converts the time-series data to a first set of encoded time-series data (Govari: at least ¶0034; “during the mapping procedure, electrodes 22 acquire and/or inject signals from and/or to the tissue of heart 23, such as of intra-cardiac ECG. The respective locations of mapping-electrodes 22 (i.e., where the intra-cardiac ECG signals are measured) are tracked as well, as described below. A processor 28 receives these signals via an electrical interface 35, and processes the information contained in these signals to construct displayable medical data, such as an electro-anatomical map 31 and present ECG traces 40 on a local display 26”; note: signals are encoded and become waveform such as the one shown in Fig. 2); carrying out a second encoding process that converts the first set of encoded time-series data to preconditioned time-series data (Govari: at least ¶0027 “selection of one or more time-intervals of interest within the time period” and “compress a first portion of the data, which is within the selected time intervals, at a first resolution, compress a second portion of the data, which is outside the selected time intervals, at a second resolution”; ¶0029 further discloses “EP data comprises hundreds of ECG channels that were sampled at 32 bit/sec (bps) each”; ¶0045 further discloses “selectively compressing ECG data” and “… selects (e.g., on display 26, using interactive means such as a pointing and/or time-interval selection tool) time-intervals 52 (i.e., a portion) of the ECG traces to be compressed at high resolution by processor 28, at a selection of compression in high resolution step 72” and “… selects another portion (e.g., a remaining portion) of the data to be compressed at low resolution by processor 28, at a selection of compression in low resolution step 74”); compressing the preconditioned time-series data to generate compressed packages of the time series data (Govari: at least ¶0027; “selection of one or more time-intervals of interest within the time period” and “compress a first portion of the data, which is within the selected time intervals, at a first resolution, compress a second portion of the data, which is outside the selected time intervals, at a second resolution”; ¶0029 further discloses “EP data comprises hundreds of ECG channels that were sampled at 32 bit/sec (bps) each”; ¶0045 further discloses “selectively compressing ECG data” and “… selects (e.g., on display 26, using interactive means such as a pointing and/or time-interval selection tool) time-intervals 52 (i.e., a portion) of the ECG traces to be compressed at high resolution by processor 28, at a selection of compression in high resolution step 72” and “… selects another portion (e.g., a remaining portion) of the data to be compressed at low resolution by processor 28, at a selection of compression in low resolution step 74”); and
transmitting the compressed packages of the time-series data to a remote computing system (Govari: at least ¶0043; “Compressed data may be uploaded, for example, with link 56 supporting an upload rate of 10 megabit/sec. In an embodiment, cloud server 60 is bidirectionally connected to network 100 via a NIC 68 and a link 86. Cloud server 60 receives the selectively compressed ECG traces and performs additional steps of data manipulation, saving, and archiving”).
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.
Claims 1, 3-6, 11-13, 16-17, 19 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB 2020/0051683 by Govari et al. (“Govari”) in view of US PGPUB 2010/0074482 by Toth et al. (“Toth”).
As to Claim 1, Govari teaches a system comprising: a server comprising: one or more processors (Govari: at least Fig. 3 show processors), and computer-readable media having computer-executable instructions embodied thereon, the instructions configured to be executed by the one or more processors to cause the server (Govari: at least ¶0038; “general-purpose computer with software programmed to carry out the functions described herein”) to:
carry out a first encoding process on electrocardiogram (ECG) data to generate a first set of encoded ECG data (Govari: at least ¶0034; “electrodes 22 acquire and/or inject signals from and/or to the tissue of heart 23, such as of intra-cardiac ECG”; note: signals are encoded and become waveform such as the one shown in Fig. 2);
carry out a second encoding process on the first set of encoded ECG data to generate preconditioned ECG data (Govari: at least ¶0010; “select the one or more time-intervals based on one or more indications that an arrhythmia was recorded during the time-intervals”; ¶0045 further discloses “selects (e.g., on display 26, using interactive means such as a pointing and/or time-interval selection tool) time-intervals 52 (i.e., a portion) of the ECG traces to be compressed at high resolution by processor 28, at a selection of compression in high resolution step 72” and “selects another portion (e.g., a remaining portion) of the data to be compressed at low resolution by processor 28”);
compress the preconditioned ECG data to generate compressed packages of the ECG data (Govari: at least ¶0027; “selection of one or more time-intervals of interest within the time period” and “compress a first portion of the data, which is within the selected time intervals, at a first resolution, compress a second portion of the data, which is outside the selected time intervals, at a second resolution”; ¶0029 further discloses “EP data comprises hundreds of ECG channels that were sampled at 32 bit/sec (bps) each”; ¶0045 further discloses “selectively compressing ECG data” and “… selects (e.g., on display 26, using interactive means such as a pointing and/or time-interval selection tool) time-intervals 52 (i.e., a portion) of the ECG traces to be compressed at high resolution by processor 28, at a selection of compression in high resolution step 72” and “… selects another portion (e.g., a remaining portion) of the data to be compressed at low resolution by processor 28, at a selection of compression in low resolution step 74”); and
transmit the compressed packages of the ECG data to a remote computing system (Govari: at least ¶0043; “Compressed data may be uploaded, for example, with link 56 supporting an upload rate of 10 megabit/sec. In an embodiment, cloud server 60 is bidirectionally connected to network 100 via a NIC 68 and a link 86. Cloud server 60 receives the selectively compressed ECG traces and performs additional steps of data manipulation, saving, and archiving”).
Govari does not explicitly disclose, but Toth discloses wherein the first encoding process comprises reducing bias in the ECG data (Toth: at least ¶¶0030 & 0040; “at least one lowest value coefficient is removed from consideration. It may be desirable to remove the lowest value components in order to focus on more prevalent signal features” and “at least one lowest frequency coefficient is removed from consideration. Since wavelet transforms are scaled, the number of entries in each row of the two-dimensional matrix representing the wavelet transform may vary").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Toth’s feature of wherein the first encoding process comprises reducing bias in the ECG data (Toth: at least ¶¶0030 & 0040) with Govari’s system.
The suggestion/motivation for doing so would have been to detect “signal features from a biological (bio) signal in the presence of noise” (Toth: at least ¶0001).
As to Claim 3, Govari and Toth teach the system of claim 1, wherein the compress the preconditioned ECG data includes using bit packing (Govari: at least ¶0026; “the smart, the selective compression techniques described herein may compress the data by selectively reducing the quantization of the data (e.g., the number of bits used to represent each data value), reducing the sampling rate of the data, or in any other suitable way. The compression may utilize any suitable compression tools. In the present context, any such compression scheme is characterized by a certain resolution”).
As to Claim 4, Govari and Toth teach the system of claim 3, wherein, after the reducing the bias, the ECG data contains initial negative values and initial positive values (Toth: at least ¶0032; “derivative signal 14 may include spikes in both the positive and negative directions, respectively indicating sharp upwards and downwards transitions in the reconstructed signal 12”).
As to Claim 5, Govari and Toth teach the system of claim 4, wherein the second encoding process comprises converting the initial negative values to subsequent positive values (Toth: at least ¶¶0033 & 0042; “rectification converts negative values into positive values and may be used to simplify signal analysis. FIG. 5 shows a rectified signal 16, which represents the derivative signal 14 after being rectified” and “rectified so that negative values are converted into positive values”).
As to Claim 6, Govari and Toth teach the system of claim 5, wherein the subsequent positive values are interleaved with the initial positive values to generate the preconditioned ECG data (Toth: at least ¶0033; “FIG. 5 shows a rectified signal 16, which represents the derivative signal 14 after being rectified”).
As to Claim 11, Govari and Toth teach the system of claim 1, wherein the instructions are configured to be executed by the one or more processors to cause the server to: transmit executable code to the remote computing system, wherein the executable code is configured to be executed by the remote computing system to decompress the compressed packages of the ECG data (Govari: at least ¶0043; “decompressed by remote processor 58 that furthermore presents the decompressed data on a remote display 69. A medical expert may view the ECG traces on remote display 69 and provide diagnosis from a distant location”).
As to Claim 12, Govari and Toth teach the system of claim 11, further comprising: the remote computing system comprising a set of its own one or more processors arranged to execute the executable code to cause the remote computing system to: decompress the compressed packages of the ECG data into the preconditioned ECG data, and decode the preconditioned ECG data to reconstruct the strips of the ECG data (Govari: at least ¶0043; “decompressed by remote processor 58 that furthermore presents the decompressed data on a remote display 69. A medical expert may view the ECG traces on remote display 69 and provide diagnosis from a distant location”).
As to Claim 13, Govari and Toth teach the system of claim 12, wherein the remote computing system includes a user interface, wherein the user interface is configured to display the strips ECG data after being decompressed and decoded (Govari: at least ¶0043; “decompressed by remote processor 58 that furthermore presents the decompressed data on a remote display 69. A medical expert may view the ECG traces on remote display 69 and provide diagnosis from a distant location”).
As to Claim 16, Govari teaches the method of claim 15.
Govari does not explicitly disclose, but Toth discloses wherein the first set of encoded time-series data includes initial negative values and initial positive values (Toth: at least ¶0032; “derivative signal 14 may include spikes in both the positive and negative directions, respectively indicating sharp upwards and downwards transitions in the reconstructed signal 12”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Toth’s feature of wherein the first set of encoded time-series data includes initial negative values and initial positive values (Toth: at least ¶0032) with Govari’s method.
The suggestion/motivation for doing so would have been to detect “signal features from a biological (bio) signal in the presence of noise” (Toth: at least ¶0001).
As to Claim 17, Govari and Toth teach the method of claim 16, wherein the second set of encoded time-series data only includes positive values (Toth: at least ¶0033; “rectification converts negative values into positive values and may be used to simplify signal analysis. FIG. 5 shows a rectified signal 16, which represents the derivative signal 14 after being rectified”).
As to Claim 19, Govari and Toth teach the method of claim 17, wherein the positive values include subsequent positive values converted from the initial negative values (Toth: at least ¶0033; “rectification converts negative values into positive values and may be used to simplify signal analysis. FIG. 5 shows a rectified signal 16, which represents the derivative signal 14 after being rectified”), wherein the subsequent positive values are interleaved with the initial positive values to generate the preconditioned time-series data (Toth: at least ¶0033; “FIG. 5 shows a rectified signal 16, which represents the derivative signal 14 after being rectified”).
As to Claim 21, Govari teaches the method of claim 15.
Govari does not explicitly disclose, but Toth discloses wherein the first encoding process comprises reducing bias in the time-series data (Toth: at least ¶¶0030 & 0040; “at least one lowest value coefficient is removed from consideration. It may be desirable to remove the lowest value components in order to focus on more prevalent signal features” and “at least one lowest frequency coefficient is removed from consideration. Since wavelet transforms are scaled, the number of entries in each row of the two-dimensional matrix representing the wavelet transform may vary").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Toth’s feature of wherein the first encoding process comprises reducing bias in the time-series data (Toth: at least ¶¶0030 & 0040) with Govari’s method.
The suggestion/motivation for doing so would have been to detect “signal features from a biological (bio) signal in the presence of noise” (Toth: at least ¶0001).
Claims 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB 2020/0051683 by Govari et al. (“Govari”) in view of US PGPUB 2010/0074482 by Toth et al. (“Toth”), and further in view of US PGPUB 2012/0014575 by Wang.
As to Claim 7, Govari and Toth teach the system of claim 3, wherein the reducing the bias in the ECG data includes applying a differential encoding algorithm to the ECG data (Toth: at least ¶0032; “derivative signal 14 may include spikes in both the positive and negative directions, respectively indicating sharp upwards and downwards transitions in the reconstructed signal 12”).
Govari and Toth do not explicitly disclose, but Wang discloses wherein the differential encoding algorithm comprises calculating a difference between amplitudes of adjacent data points (Wang: at least ¶0040; “compressed ECG data, produced by encode module 610, as well as the mean R-peak amplitude, R-peak amplitude difference values, mean period, and period difference values may be provided as output from system 600 for subsequent storage and/or transmission”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wang’s feature of wherein the differential encoding algorithm comprises calculating a difference between amplitudes of adjacent data points (Wang: at least ¶0040) with the system disclosed by Govari and Toth.
The suggestion/motivation for doing so would have been to implement “hybrid 2D ECG data compression” (Wang: at least ¶0039).
As to Claim 20, Govari teaches the method of claim 15.
Govari does not explicitly disclose, but Toth discloses wherein the first encoding process applies a differential encoding algorithm to the time-series data (Toth: at least ¶0032; “derivative signal 14 may include spikes in both the positive and negative directions, respectively indicating sharp upwards and downwards transitions in the reconstructed signal 12”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Toth’s feature of wherein the first encoding process applies a differential encoding algorithm to the time-series data (Toth: at least ¶003) with Govari’s method.
The suggestion/motivation for doing so would have been to detect “signal features from a biological (bio) signal in the presence of noise” (Toth: at least ¶0001).
Govari and Toth do not explicitly disclose, but Wang discloses wherein the differential encoding algorithm comprises calculating a difference between amplitudes of adjacent data points (Wang: at least ¶0040; “compressed ECG data, produced by encode module 610, as well as the mean R-peak amplitude, R-peak amplitude difference values, mean period, and period difference values may be provided as output from system 600 for subsequent storage and/or transmission”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wang’s feature of wherein the differential encoding algorithm comprises calculating a difference between amplitudes of adjacent data points (Wang: at least ¶0040) with the method disclosed by Govari and Toth.
The suggestion/motivation for doing so would have been to implement “hybrid 2D ECG data compression” (Wang: at least ¶0039).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB 2020/0051683 by Govari et al. (“Govari”) in view of US PGPUB 2010/0074482 by Toth et al. (“Toth”), and further in view of US PGPUB 2012/0179055 by Tamil et al. (“Tamil”).
As to Claim 8, Govari and Toth teach the system of claim 1.
Govari and Toth do not explicitly disclose, but Tamil discloses wherein the server comprises a machine learning mode, wherein the instructions are configured to be executed by the one or more processors to cause the server to: before the strips of the ECG data are processed to generate the compressed packages of the ECG data, input the strips of the ECG data into the machine learning model to generate metadata, the metadata including cardiac event classifications (Tamil: at least ¶¶0041 & 0045; “the system is designed to classify six major arrhythmia most commonly observed by the cardiologists” and “we train six classifiers, each for identifying one arrhythmia type and using all the heart beats in our databank for training and testing”; ¶0046 further discloses “Training Data Sets and are passed into the feature extraction module where each beat is represented with the 203 features”) and beat classifications (Tamil: at least ¶0040; “the heart beat classification system of the invention is implemented by the Support Vector Machine approach. It is a supervised learning framework which performs classification by constructing an N dimensional hyper-plane that optimally separates the data into two categories”; ¶0046 further discloses “six classifiers are trained, one for identifying a particular type of beat using the one-versus-all scheme, resulting in six binary beat classifiers. After training, the test data set from the remaining fold (i.e., the fold not used for training) is given to the classification system. The classification is a two stage process. During the first stage, the training data is generated with the features selected. In the present invention, the ECG beats are divided into five sets of equal number of beats”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Tamil’s features of wherein the server comprises a machine learning mode, wherein the instructions are configured to be executed by the one or more processors to cause the server to: before the strips of the ECG data are processed to generate the compressed packages of the ECG data, input the strips of the ECG data into the machine learning model to generate metadata, the metadata including cardiac event classifications (Tamil: at least ¶¶0041 & 0045-0046) and beat classifications (Tamil: at least ¶¶0040, 0046) with the system disclosed by Govari and Toth.
The suggestion/motivation for doing so would have been to allow for “heart performance characterization and abnormality detection” and “analyze and characterize cardiac electrophysiological signals which help in the diagnosis of myocardial ischemia in advance of a heart attack” (Tamil: at least Abstract).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB 2020/0051683 by Govari et al. (“Govari”) in view of US PGPUB 2010/0074482 by Toth et al. (“Toth”), and further in view of US PGPUB 2016/0287122 by Heneghan.
As to Claim 9, Govari and Toth teach the system of claim 1, wherein the compressed packages of the ECG data include a collection of separate blocks of compressed strips of ECG data (Govari: at least ¶0027; “selection of one or more time-intervals of interest within the time period” and “compress a first portion of the data, which is within the selected time intervals, at a first resolution, compress a second portion of the data, which is outside the selected time intervals, at a second resolution”; ¶0029 further discloses “EP data comprises hundreds of ECG channels that were sampled at 32 bit/sec (bps) each” and “the compressed first and second portions of the data”; ¶0045 further discloses “selectively compressing ECG data” and “… selects (e.g., on display 26, using interactive means such as a pointing and/or time-interval selection tool) time-intervals 52 (i.e., a portion) of the ECG traces to be compressed at high resolution by processor 28, at a selection of compression in high resolution step 72” and “… selects another portion (e.g., a remaining portion) of the data to be compressed at low resolution by processor 28, at a selection of compression in low resolution step 74”).
Govari and Toth do not explicitly disclose, but Heneghan discloses wherein each of the compressed strips of the ECG represent multiple minutes of ECG data (Heneghan: at least ¶¶0297, 0303; “ECG signal may be split into time segments. A time segment may be as long as the entire record of the ECG signal or as short as the length of a representative hypopnea-hyperpnoea sequence. Time segments may be of equal length. In one example, each time segment may have a time length of 30 minutes. In another example, each time segment may have a time length of 10 minutes” and “the feature extraction component 840 may extract or derive one or more features from an ECG time segment. Each feature may act as a compressed representation of the ECG time segment”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Heneghan’s feature of wherein each of the compressed strips of the ECG represent multiple minutes of ECG data (Heneghan: at least ¶¶0297, 0303) with the system disclosed by Govari and Toth.
The suggestion/motivation for doing so would have been to derive features from ECG signal and analyze the feature to determine an occurrence of periodic breathing (Heneghan: at least ¶0069).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB 2020/0051683 by Govari et al. (“Govari”) in view of US PGPUB 2010/0074482 by Toth et al. (“Toth”), and further in view of US PGPUB 2002/0078241 by Vidal et al. (“Vidal”).
As to Claim 14, Govari and Toth teach the system of claim 12.
Govari and Toth do not explicitly disclose, but Vidal discloses wherein the remote computing system is configured to operate a browser, wherein the browser is configured to execute the executable code received from the server (Vidal: at least ¶¶0036-0037; “browser 22 sends a request to download media file 5, as indicated at block 32. This request is preferably intercepted by media decompress program 24” and “decompress program 24, which decompresses the file to reconstruct the original media file 5, as indicated at block 42. To complete the process, decompress program 24 sends the reconstructed media file 5 to web browser 22, as indicated at block 44”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Vidal’s feature of wherein the remote computing system is configured to operate a browser, wherein the browser is configured to execute the executable code received from the server (Vidal: at least ¶¶0036-0037) with the system disclosed by Govari and Toth.
The suggestion/motivation for doing so would have been to perform decompression using a plugin in a web browser (Vidal: at least ¶0059; “decompression program 24 resides on the user's web browser, i.e., as an "add-on", or "plug-in", for example, an ActiveX.TM. control or Netscape.TM. plugin”) such as Internet Explorer and Netscape Navigator (Vidal: at least ¶0050; “browser platform known in the art, for example, Microsoft Windows.TM. platforms, and Internet Explorer.TM. and Netscape Navigator.TM. browsers”).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB 2020/0051683 by Govari et al. (“Govari”) in view of US PGPUB 2010/0074482 by Toth et al. (“Toth”), and further in view of US PGPUB 2024/0188876 by Ghose.
As to Claim 18, Govari and Toth teach the method of claim 17.
Govari and Toth do not explicitly disclose, but Ghose discloses wherein the positive values are compressed using bit packing (Ghose: at least ¶0027; “the ECG waveform is composed of five individual positive and negative deflections along the x-axis. Each individual waveform represents conduction of electrical impulses through different parts of the heart”; ¶0054 further discloses “existing arrhythmia detectors require the use of all of the digitized samples for a complete ECG waveform or require a compressed version of the ECG waveform”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ghose’s feature of wherein the positive values are compressed using bit packing (Ghose: at least ¶¶0027, 0054) with the method disclosed by Govari and Toth.
The suggestion/motivation for doing so would have been to allow for detection of arrhythmia conditions (Ghose: at least ¶¶0053-0054; “existing arrhythmia detectors require the use of all of the digitized samples for a complete ECG waveform or require a compressed version of the ECG waveform”).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB 2020/0051683 by Govari et al. (“Govari”) in view of US PGPUB 2018/0116513 by Bhogu.
As to Claim 22, Govari teaches the method of claim 15.
Govari does not explicitly disclose, but Bhogu discloses wherein the compressing the preconditioned time-series data is a lossless compression process (Bhogu: at least ¶0062; “compression module 108 uses a lossless compression method to ensure the subtle changes in ECG signals are preserved in the original form and do not introduce any artifacts that may distort clinical diagnosis”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bhogu’s feature of wherein the compressing the preconditioned time-series data is a lossless compression process (Bhogu: at least ¶0062) with Govari’s.
The suggestion/motivation for doing so would have been to “ensure the subtle changes in ECG signals are preserved in the original form and do not introduce any artifacts that may distort clinical diagnosis” (Bhogu: at least ¶0062).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/H .W./
Examiner, AU 2168
05 January 2026
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168