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 § 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 (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 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.
Claims 1, and 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ng et al. (U.S. Patent 6,496,705).
Regarding claim 1, Ng et al. disclose a device comprising:
a first transceiver (“a low power RF antenna 50,” see col. 8:17-21 and figure 4, and other alternate/equivalent counterparts in other embodiments);
a storage medium (“a memory 60 storing code for execution by the microcontroller/DSP,” see col. 8:21-28 and figure 4) storing a plurality of modules; and
a processor (“a microcontroller/Digital Signal Processor (DSP) 58, and a memory 60 storing code for execution by the microcontroller/DSP,” see col. 8:21-28 and figure 4) coupled to the storage medium and the first transceiver and accessing and executing the plurality of modules,
wherein the plurality of modules comprise:
a communication module transmitting a first wireless signal through the first transceiver and receiving a first reflected signal corresponding to the first wireless signal;
a pre-processing module (the “modulator/demodulator,” “power amp,” “filters and an antenna switch” of “transceiver 54,” see col. 8:19-23 and figure 4) pre-processing the first reflected signal to generate a first processed signal;
a wireless signal encoder (the encoder portion of “encoder/decoder 56,” see col. 8:21-29 and figure 4) capturing a first embedding from the first reflected signal and the first processed signal;
a decoder (the decoder portion of “encoder/decoder 56,” see col. 8:21-29 and figure 4) generating an estimated electrocardiogram signal according to the first embedding; and
a computing module (comprising at least one of: 1) “demultiplexer 62,” 2) “buffer 64,” 3) “digital to analog filter bank 66,” 4) “amplifiers 68,” and 5) “OEM (original equipment manufacturer) standard ECG monitor interface 70,” see col. 8:35-50 and figure 4) outputting the estimated electrocardiogram signal.
Regarding claim 10, Ng et al. disclose a method comprising:
transmitting a first wireless signal (start data acquisition signal, see col. 9:5-16 and figures 1, and 3-5) and receiving a first reflected signal corresponding to the first wireless signal (receiving at antenna 50 the data/reflected data sent from antenna 44, see figures 1, and 3-4);
pre-processing the first reflected signal to generate a first processed signal (the signal coming out of the “modulator/demodulator,” “power amp,” “filters and an antenna switch” of “transceiver 54,” see col. 8:19-23 and figure 4);
capturing a first embedding from the first reflected signal and the first processed signal (the signal coming out of the decoder portion of “encoder/decoder 56,” see col. 8:21-29 and figure 4);
generating an estimated electrocardiogram signal according to the first embedding (the signal coming out of at least one of: 1) “demultiplexer 62,” 2) “buffer 64,” 3) “digital to analog filter bank 66,” 4) “amplifiers 68,” and 5) “OEM (original equipment manufacturer) standard ECG monitor interface 70,” see col. 8:35-50 and figure 4); and
outputting the estimated electrocardiogram signal (showing the ECG data on “ECG Monitor 14,” see figure 4).
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 2-7 are rejected under 35 U.S.C. 103 as being unpatentable over Ng et al. (U.S. Patent 6,496,705) as applied to claim 1 above, and further in view of Schram (U.S. Patent Application Publication 2021/0401349).
Regarding claim 2, Ng et al. disclose the claimed invention including:
a second transceiver (“low power built-in RF antenna 44,” see col. 7:49-56 and figure 3) coupled to the processor and detecting a first electrocardiogram signal,
wherein the plurality of modules further comprise:
an electrocardiogram signal encoder (the encoder portion of “encoder/decoder 40,” see col. 7:49-56 and figure 3) capturing a second embedding from the first electrocardiogram signal,
wherein the communication module transmits a second wireless signal through the first transceiver and receives a second reflected signal corresponding to the second wireless signal (see col. 8:58 through col. 9:16 and figure 5),
the pre-processing module pre-processes the second reflected signal to generate a second processed signal (see col. 8:58 through col. 9:16 and figure 5), and
the computing module trains the wireless signal encoder according to the second reflected signal (see col. 8:58 through col. 9:16 and figure 5).
Yet Ng et al. fail to recite the computing module trains the wireless signal encoder according to the second reflected signal the second processed signal based on a first machine learning algorithm, wherein a first loss function of the first machine learning algorithm is associated with the second embedding.
Like Ng et al., Schram discloses a cardiac device with wireless communication for handling and processing ECG data of the patient and teach training a machine learning module (see abstract) using a loss function in order to predict QT intervals for ECG data of the patient(s) (see [0069]).
Therefore, at the time of the of invention it would have been obvious to one of ordinary skill in the art to modify the invention of Ng et al., as taught by Schram, to provide trained machine learning using a loss function in order to predict QT intervals for ECG data of the patient(s).
Regarding claim 3, Ng et al. disclose the claimed invention including:
the communication module transmits a third wireless signal through the first transceiver and receives a third reflected signal corresponding to the third wireless signal (see col. 8:58 through col. 9:16 and figure 5),
the communication module detects a second electrocardiogram signal through the second transceiver (see col. 8:58 through col. 9:16 and figure 5).
Yet Ng et al. fail to recite the computing module trains the pre-processing module according to the third reflected signal based on a second machine learning algorithm, wherein a second loss function of the second machine learning algorithm is associated with the second electrocardiogram signal.
Like Ng et al., Schram discloses a cardiac device with wireless communication for handling and processing ECG data of the patient and teach training a machine learning module (see abstract) using a loss function in order to predict QT intervals for ECG data of the patient(s) (see [0069]).
Therefore, at the time of the of invention it would have been obvious to one of ordinary skill in the art to modify the invention of Ng et al., as taught by Schram, to provide trained machine learning using a loss function in order to predict QT intervals for ECG data of the patient(s).
Here the combination provides for the trained machine learning with loss function(s) applied to different ECG data and so the second loss function is simply the loss function applied to different ECG data.
Regarding claim 4, Ng et al. further disclose the computing module detects a time when a waveform appears in the second electrocardiogram signal (see for example col. 2:41-50, col. 3:36-44, col. 4:17-25).
However, Ng et al. fail to disclose that label data used to train the pre-processing module comprises the time.
Again, like Ng et al., Schram discloses a cardiac device with wireless communication for handling and processing ECG data of the patient and teaches training a machine learning module (see abstract) using a loss function using “root mean error squared QT interval average” (in other words, a time interval) in order to output “a probability vector for the output class of a given QT value, whose expectation value is taken to as the predicted QT interval,” (see [0069]).
In short, Schram teaches using time (a time value, label data) when training the machine learning in order to predict the QT interval.
Therefore, at the time of the of invention it would have been obvious to one of ordinary skill in the art to modify the invention of Ng et al., as taught by Schram, to use time (a time value, label data) when training the machine learning in order to predict the QT interval.
Regarding claim 5, Ng et al. fail to disclose the waveform comprises at least one
of a P wave, a Q wave, a R wave, a S wave, and a T wave.
The waveforms of a P wave, a Q wave, a R wave, a S wave, and a T wave are extremely well known when dealing with devices, systems, and/or methods for measuring ECG data of a patient.
As an example, and like Ng et al., Schram discloses a cardiac device with wireless communication for handling and processing ECG data of the patient and teaches measuring ECG waveforms that include “a P wave, a QRS complex, a T wave, and a U wave” (see [0024]-[0027]) to provide a known and workable method of measuring ECG signals in order to predict QT intervals with a machine learning algorithm.
Therefore, at the time of the of invention it would have been obvious to one of ordinary skill in the art to modify the invention of Ng et al., as taught by Schram, to measure ECG waveforms that include “a P wave, a QRS complex, a T wave, and a U wave” to provide a known and workable method of measuring ECG signals in order to predict QT intervals with a machine learning algorithm.
Regarding claims 6 and 7, Ng et al. further disclose:
a second transceiver (“low power built-in RF antenna 44,” see col. 7:49-56 and figure 3, and col. 8:58 through col. 9:16 and figure 5) coupled to the processor and detecting a third electrocardiogram signal,
wherein the plurality of modules further comprise:
an electrocardiogram signal encoder (the encoder portion of “encoder/decoder 40,” see col. 7:49-56 and figure 3) capturing a third embedding from the third electrocardiogram signal, and
wherein the computing module updates the electrocardiogram signal encoder and the decoder according to the third electrocardiogram signal (see col. 8:58 through col. 9:16 and figure 5),.
However, Ng et al. fail to disclose:
1) the third embedding based on a third machine learning algorithm, wherein a third loss function of the third machine learning algorithm is associated with the third electrocardiogram signal, and
2) the third loss function is associated with a spectrum of the third electrocardiogram signal.
Like Ng et al., Schram discloses a cardiac device with wireless communication for handling and processing ECG data of the patient and teach training a machine learning module (see abstract) using a loss function in order to predict QT intervals for ECG data of the patient(s) (see [0069]).
This combination provides a third machine learning (the machine learning with the loss function of Schram) that processes a third embedded/embedding signal to yield the final third electrocardiogram signal.
Additionally, this third loss function is inherently associated with a spectrum of the third electrocardiogram signal since the third loss function is associated with the spectrum of the third ECG signal.
Therefore, at the time of the of invention it would have been obvious to one of ordinary skill in the art to modify the invention of Ng et al., as taught by Schram, to provide trained machine learning using a loss function in order to predict QT intervals for ECG data of the patient(s).
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ng et al. (U.S. Patent 6,496,705) in view of Schram (U.S. Patent Application Publication 2021/0401349) as applied to claim 2 above, and further in view of Soon-Shiong et al. (U.S. Patent Application Publication 2022/0022798).
Regarding claim 8, Ng et al. in view of Schram show but fail to disclose at least one of the pre-processing module, the wireless signal encoder, the decoder, and the electrocardiogram signal encoder is a transformer model.
This recitation is interpretated to mean the machine learning is in the form of a transformer/transformation model/algorithm that can be implemented into or at the stage of the at least one of the pre-processing module, the wireless signal encoder, the decoder, and the electrocardiogram signal encoder.
See [0035] of the present application’s U.S. Patent Application Publication 2024/0389951 for support for this interpretation wherein it is disclosed “The computing module 122 may train the wireless signal encoder 13 according to the reflected signal R2 and the processed signal P2 based on a machine learning algorithm (e.g., a transformer algorithm).”
Like both Ng et al. and Schram, Soon-Shiong et al. disclose a device and method for analyzing ECG signals and teach using machine learning models in the form of “Bidirectional Encoder Representations from Transformers (BERT) model” in order to provide a known and workable manner of providing significantly improved ECG diagnostic using large data sets and self-learning (see 0034]).
Therefore, at the time of the of invention it would have been obvious to one of ordinary skill in the art to modify the invention of Ng et al. in view of Schram, as taught by Soon-Shiong et al. disclose, to use machine learning models in the form of “Bidirectional Encoder Representations from Transformers (BERT) model” in order to provide a known and workable manner of providing significantly improved ECG diagnostic using large data sets and self-learning.
Claims 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ng et al. (U.S. Patent 6,496,705) in view of Schram (U.S. Patent Application Publication 2021/0401349) as applied to claim 2 above, and further in view of Brooks et al. (U.S. Patent Application Publication 2008/0234594).
Regarding claim 9, Ng et al. in view of Schram show but fail to disclose the first wireless signal comprises a frequency modulated continuous wave signal carried by millimeter waves.
Like both Ng et al. and Schram, Brooks et al. disclose a wireless device and method for analyzing ECG signals and teach providing wireless signal communication in the form of continuous waveforms (see [0088]) in millimeter wave range (see [0051] and [0056]) in order to provide a known and workable manner of wireless communication for an ECD device and method.
This combination provide wireless communication of signal (at least some of which are continuous ECG waveform signals) in the millimeter wave range.
Therefore, at the time of the of invention it would have been obvious to one of ordinary skill in the art to modify the invention of Ng et al. in view of Schram, as taught by Soon-Shiong et al. disclose, to provide wireless signal communication in the form of continuous waveforms in millimeter wave range in order to provide a known and workable manner of wireless communication for an ECD device and method.
Conclusion
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/AARON F ROANE/Primary Examiner, Art Unit 3792