Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 recites the limitation "second feature extractor" in line 3. There is insufficient antecedent basis for this limitation in the claim. Claim 5 depends on claim 1, and claim 1 doesn’t mention a “first feature extractor” so it is not understood how it there can be a “second feature extractor.”
Claim 7 recites the limitation "similar feature extraction unit" in line 3. There is insufficient antecedent basis for this limitation in the claim. Claim 7 depends on claim 1, and claim 1 doesn’t mention a “similar feature extraction unit.”
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 – 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a system with structural components (claims 1 - 9) and method (claim 10). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claims 1,
Step 2A, Prong One
The claim recites in part:
extracts the user's unique biosignal feature, extracts a similar feature by comparing the user's extracted unique biosignal feature and the user's biosignal feature for contrastive learning, and classifies sleep stages based on the extracted similar feature.
For example, a can doctor can compare a patient’s current biosignal to a previously observed patterns, identify the closest match, and determine the patient’s sleep stage based on that comparison.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a user terminal that measures a user's biosignal and preprocesses the user's measured biosignal;
a classification server that receives the user's preprocessed biosignal from the user terminal,
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites a user terminal and a classification server which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of “user's biosignal includes at least one of the user'selectroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), electromyography (EMG), respiratory effort signals, pulse, oxygen saturation (SpO2), and blood flow” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
a user terminal that measures a user's biosignal and preprocesses the user's measured biosignal;
a classification server that receives the user's preprocessed biosignal from the user terminal,
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites a user terminal and a classification server which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of “user's biosignal includes at least one of the user's electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), electromyography (EMG), respiratory effort signals, pulse, oxygen saturation (SpO2), and blood flow” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 2,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a wherein the user terminal comprise:
a biosignal measurement unit that measures the user's biosignal;
a first communication unit that transmits the user's biosignal measurement data, which has been noise-removed and preprocessed by the preprocessing unit, to the classification server.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
a preprocessing unit that removes noise and preprocesses the user's measured biosignal (hereinafter referred to as the user's biosignal measurement data)
which is recited at a high-level of generality with no detail of the preprocessing and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites biosignal measurement unit, a preprocessing unit, and a first communication unit which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
a wherein the user terminal comprise:
a biosignal measurement unit that measures the user's biosignal;
a first communication unit that transmits the user's biosignal measurement data, which has been noise-removed and preprocessed by the preprocessing unit, to the classification server.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites:
a preprocessing unit that removes noise and preprocesses the user's measured biosignal (hereinafter referred to as the user's biosignal measurement data)
which is recited at a high-level of generality with no detail of the preprocessing and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites biosignal measurement unit, a preprocessing unit, and a first communication unit which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 3,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the classification server comprises:
a second communication unit that receives the user's biosignal measurement data that has been noise-removed and preprocessed (hereinafter referred to as the user's preprocessed biosignal measurement data) from the first communication unit;
a memory unit that stores the user's preprocessed biosignal measurement data;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
a first feature extraction unit that extracts the user's unique biosignal feature from the user's preprocessed biosignal measurement data.
which is recited at a high-level of generality with no detail of the extraction and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites a second communication unit, a memory unit, and a first feature extraction unit which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
the classification server comprises:
a second communication unit that receives the user's biosignal measurement data that has been noise-removed and preprocessed (hereinafter referred to as the user's preprocessed biosignal measurement data) from the first communication unit;
a memory unit that stores the user's preprocessed biosignal measurement data;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites:
a first feature extraction unit that extracts the user's unique biosignal feature from the user's preprocessed biosignal measurement data.
which is recited at a high-level of generality with no detail of the extraction and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites a second communication unit, a memory unit, and a first feature extraction unit which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 4,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the classification server comprises a contrastive learning execution unit that performs contrastive learning on the user's biosignal measurement data for contrastive learning wherein the user's biosignal measurement data for contrastive learning has been noise-removed and processed and includes a plurality of users' biosignal measurement data.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites a contrastive learning execution unit which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the classification server comprises a contrastive learning execution unit that performs contrastive learning on the user's biosignal measurement data for contrastive learning wherein the user's biosignal measurement data for contrastive learning has been noise-removed and processed and includes a plurality of users' biosignal measurement data.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites a contrastive learning execution unit which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 5,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the classification server comprises a second feature extraction unit that extracts the user' unique biosignal feature for contrastive learning from the user's biosignal measurement data for contrastive learning pre-stored in the memory unit.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites a second feature extraction unit which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the classification server comprises a second feature extraction unit that extracts the user' unique biosignal feature for contrastive learning from the user's biosignal measurement data for contrastive learning pre-stored in the memory unit.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
claim further recites a second feature extraction unit which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 6,
Step 2A, Prong One
The claim recites in part:
wherein the classification server comprises a similar feature extraction unit that extracts a similar feature (hereinafter referred to as a mutually invariant biosignal feature between users) by comparing the user's unique biosignal feature extracted by the first feature extraction unit and the user' unique biosignal feature for contrastive learning extracted by the second feature extraction unit.
For example, a can doctor can compare a patient’s current biosignal to a previously observed patterns, identify the closest match, and determine similarities based on that comparison.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim further recites a similar feature extraction unit which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
The claim further recites a similar feature extraction unit which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 7,
Step 2A, Prong One
The claim recites in part:
wherein the classification server comprises a sleep stage classification unit that classifies the user's sleep stages based on the user's unique biosignal feature extracted by the first feature extraction unit and the mutually invariant biosignal feature between users extracted by the similar feature extraction unit.
For example, a can doctor can compare a patient’s current biosignal to a previously observed patterns, identify the closest match, and determine the patient’s sleep stage based on that comparison.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim further recites a sleep stage classification unit which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
The claim further recites a sleep stage classification unit which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 8,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the user terminal receives the user's sleep stage classification result from the classification server.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the user terminal receives the user's sleep stage classification result from the classification server.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 9,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 8, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the user terminal comprises a display unit that outputs the user's sleep stage classification result received from the classification server
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the user terminal comprises a display unit that outputs the user's sleep stage classification result received from the classification server
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 10 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1 - 5 and 8 - 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shusterman (US 2011/0004110) in view of Cheng et al (US 2021/0374570).
As to claim 1, Shusterman figures 9 - 11 shows and teaches an automatic sleep stage classification system for reducing the variation in performance between users using a learning method (paragraph [0019]…this multi-level structure also ensures adaptability of the system, in which the system processes all available data to learn the individual patient's pattern of normal range and abnormal variations. The adaptability is achieved by collecting and processing serial data at the higher scales and then, using this information at the lower scale to individually tailor (edit, adjust) the diagnostic and processing criteria (thresholds)), the system comprising:
a user terminal that measures a user's biosignal and preprocesses the user's
measured biosignal (paragraph [0136]…the system may receive physiological or health data (for example, ECG data) from a recorded data source for analysis, but preferably receives the data real-time, on-line. As used herein, patient means an animal, and most likely a human. The medical device further includes an analysis unit or module 40 which, in turn, consists of processing, compression, storage, and comparison units (FIG. 10). The processing unit 41 can be a typical computer or personal computer of the type available from many vendors such as IBM or Hewlett-Packard. The processing unit 41 is programmed to detect a plurality of characteristic points such as the onset, peak and offset of P-, Q-, R-, S-, T-, U-waves, and computes the characteristic parameters or primary elements which include amplitudes of the said waves and ST-segment, duration of PQ-, QRS-, and QT-intervals. The processing unit 41 has a programmable microprocessor that can be programmed to modify or change the set of primary elements or to adjust their search criteria)(Examiner’s Note: “Acquisition Unit 20 “ reads on ”a user terminal” ; “physiological or health data” reads on “biosignal” ; “Processing Unit 41” reads on “preprocesses”); and
a classification server that receives the user's preprocessed biosignal from the
user terminal, extracts the user's unique biosignal feature, extracts a similar feature by
comparing the user's extracted unique biosignal feature and the user's biosignal feature
for learning, and classifies sleep stages based on the extracted similar feature (paragraph [0037]… Compression unit 42 compresses the ECG waveform into a few weighted basis vectors and their coefficients using principal component analysis, wavelet decomposition, or other orthogonal mathematical transformation. Storage unit 43 stores the compressed waveforms and the computed primary elements into memory. Comparative unit 44 compares the newly acquired waveforms and newly computed primary elements with the waveforms and primary elements previously stored in the storage unit 43. The analysis unit 40 has means for adjusting the thresholds for each indicator, whereas the default values correspond to normal ECG. An output unit 60 includes a screen or a set of indicators for displaying the ECG waveforms and the computed primary elements in comparison with the previously stored primary elements or in comparison with the default reference values. The results of comparison can be represented both qualitatively and quantitatively in the dynamic and static modes. Abnormal readings may be further classified into moderately abnormal and severely abnormal. To make the indicators understandable to a lay person, the degree of abnormality may be color-coded: green color corresponds to a normal value, yellow corresponds to a moderate abnormality, and red corresponds to a severe abnormality. In the dynamic mode, the quantitative representation shows the differences between the newly acquired and stored primary elements and waveforms, whereas the qualitative representation includes indication of each parameter as being changed (C) or unchanged (U))(Examiner’s Note: “Compression Unit 42, Storage Unit 43, Comparative Unit 44” reads on “classification server” ; “Compression unit 42 compresses the ECG waveform into a few weighted basis vectors and their coefficients using principal component analysis” reads on “extracts the user's unique biosignal feature” ; “Comparative unit 44 compares the newly acquired waveforms and newly computed primary elements with the waveforms and primary elements previously stored in the storage unit 43” reads on “extracts a similar feature by comparing the user's extracted unique biosignal feature and the user's biosignal feature for learning” ; “The results of comparison can be represented both qualitatively and quantitatively in the dynamic and static modes. Abnormal readings may be further classified into moderately abnormal and severely abnormal” reads on “classifies sleep stages based on the extracted similar feature”);
wherein the user's biosignal includes at least one of the user's electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), electromyography (EMG), respiratory effort signals, pulse, oxygen saturation (SpO2), and blood flow (paragraph [0136]…the system may receive physiological or health data (for example, ECG data) from a recorded data source for analysis, but preferably receives the data real-time, on-line. As used herein, patient means an animal, and most likely a human).
Shusterman fails to explicitly show/teach that the learning is contrastive learning.
However, Cheng et al teaches contrastive learning (paragraph [0034]… 0034] The system 100 may include a machine-learning model 110. The machine-learning model 110 may implement contrastive learning for training of the machine-learning model 110 to facilitate classification of the data 102 received at the input 104 of the system 100. For example, the machine-learning model 110 may implement subject-dependent, self-supervised learning to facilitate classification of the data 102. The training of the machine-learning model 110 via contrastive learning may reduce a number of downstream tasks for classifying the data 102).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made, for Shusterman’s learning to be contrastive learning, as in Cheng et al, for the purpose of reducing a number of downstream tasks for classifying the data.
As to claim 2, modified Shusterman figures 9 - 11 shows and teaches an automatic sleep stage classification system for reducing the variation in performance between users using a contrastive learning method, wherein the user terminal comprises:
a biosignal measurement unit that measures the user's biosignal (paragraph [0136]…the system may receive physiological or health data (for example, ECG data) from a recorded data source for analysis, but preferably receives the data real-time, on-line. As used herein, patient means an animal, and most likely a human)(Examiner’s Note: “Acquisition Unit 20 “ reads on ” a biosignal measurement unit” ; “physiological or health data” reads on “biosignal”);
a preprocessing unit that removes noise and preprocesses the user's measured
biosignal (hereinafter referred to as the user's biosignal measurement data))(paragraph [0037]… Scale I includes means for adjustment of individual thresholds and criteria for rejection of noisy data. A detector of noise and error rejects the noisy data if the primary elements exceed physiologic range); and
a first communication unit that transmits the user's biosignal measurement data,
which has been noise-removed and preprocessed by the preprocessing unit, to the
classification server (paragraph [0137]… Communication unit 100 transmits the information between the device 10 and external higher-level processing device 110. The communication unit 100 may be a modem or a wireless transmitter/receiver. Electrocardiographic signals and recorded values of primary elements and indexes are transmitted from the device 10 to higher level devices for more detailed processing and storage. The higher-level device 110 preferably transmits back to device 10 a set of primary elements and their search criteria to be used in device 10)(Examiner’s Note: “The communication unit 100 may be a modem or a wireless transmitter/receiver. Electrocardiographic signals and recorded values of primary elements and indexes are transmitted from the device 10 to higher level devices for more detailed processing and storage” reads on “a first communication unit that transmits the user's biosignal measurement data”).
As to claim 3, modified Shusterman figures 9 - 11 shows and teaches an automatic sleep stage classification system for reducing the variation in performance between users using a contrastive learning method, wherein the user terminal comprises:
a second communication unit that receives the user's biosignal measurement data that has been noise-removed and preprocessed (hereinafter referred to as the
user's preprocessed biosignal measurement data) from the first communication unit (paragraph [0137]… Communication unit 100 transmits the information between the device 10 and external higher-level processing device 110. The communication unit 100 may be a modem or a wireless transmitter/receiver. Electrocardiographic signals and recorded values of primary elements and indexes are transmitted from the device 10 to higher level devices for more detailed processing and storage. The higher-level device 110 preferably transmits back to device 10 a set of primary elements and their search criteria to be used in device 10) (Examiner’s Note: “external higher-level processing device 110” reads on “a second communication unit”) ;
a memory unit that stores the user's preprocessed biosignal measurement data (paragraph [0037]… Storage unit 43 stores the compressed waveforms and the computed primary elements into memory. Comparative unit 44 compares the newly acquired waveforms and newly computed primary elements with the waveforms and primary elements previously stored in the storage unit 43)(Examiner’s Note: “Storage unit 43 stores the compressed waveforms and the computed primary elements into memory” reads on “a memory unit that stores the user's preprocessed biosignal measurement data”); and
a first feature extraction unit that extracts the user's unique biosignal feature from
the user's preprocessed biosignal measurement data (paragraph [0037]… An output unit 60 includes a screen or a set of indicators for displaying the ECG waveforms and the computed primary elements in comparison with the previously stored primary elements or in comparison with the default reference values. The results of comparison can be represented both qualitatively and quantitatively in the dynamic and static modes. Abnormal readings may be further classified into moderately abnormal and severely abnormal. To make the indicators understandable to a lay person, the degree of abnormality may be color-coded: green color corresponds to a normal value, yellow corresponds to a moderate abnormality, and red corresponds to a severe abnormality. In the dynamic mode, the quantitative representation shows the differences between the newly acquired and stored primary elements and waveforms, whereas the qualitative representation includes indication of each parameter as being changed (C) or unchanged (U))(Examiner’s Note: “output unit 60 includes a screen or a set of indicators for displaying the ECG waveforms and the computed primary elements” reads on “a first feature extraction unit that extracts the user's unique biosignal feature from the user's preprocessed biosignal measurement data”).
As to claim 4, Shusterman figures 9 - 11 shows and teaches an automatic sleep stage classification system for reducing the variation in performance between users using a contrastive learning method, wherein the classification server
(paragraph [0037]… Compression unit 42 compresses the ECG waveform into a few weighted basis vectors and their coefficients using principal component analysis, wavelet decomposition, or other orthogonal mathematical transformation. Storage unit 43 stores the compressed waveforms and the computed primary elements into memory. Comparative unit 44 compares the newly acquired waveforms and newly computed primary elements with the waveforms and primary elements previously stored in the storage unit 43.)(Examiner’s Note: “Compression Unit 42, Storage Unit 43, Comparative Unit 44” reads on “classification server”); and
wherein the user's biosignal measurement data for contrastive learning has been noise-removed and processed and includes a plurality of users' biosignal measurement data.
(paragraph [0037]… Scale I includes means for adjustment of individual thresholds and criteria for rejection of noisy data. A detector of noise and error rejects the noisy data if the primary elements exceed physiologic range).
Shusterman fails to explilcty show/teach wherein the classification server comprises a contrastive learning execution unit that performs contrastive learning on the user's biosignal measurement data for contrastive learning pre-stored in the memory unit.
However, Cheng et al teaches a classification server comprises a contrastive learning execution unit that performs contrastive learning on the user's biosignal measurement data for contrastive learning pre-stored in the memory unit (paragraph [0034]…system 100 may include a machine-learning model 110. The machine-learning model 110 may implement contrastive learning for training of the machine-learning model 110 to facilitate classification of the data 102 received at the input 104 of the system 100. For example, the machine-learning model 110 may implement subject-dependent, self-supervised learning to facilitate classification of the data 102. The training of the machine-learning model 110 via contrastive learning may reduce a number of downstream tasks for classifying the data 102 ; paragraph [0042]… techniques presented herein may facilitate training a general model that may be used to process data from new subjects. In some instances, the trained model may serve as an initialized model that may then be further trained (e.g., to learn features that are specific to a given user). For example, the initialized model may be stored in data storage on a new device, and the new device may then further train the model based on signals collected from a user).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made, for Shusterman’s classification server to comprise a contrastive learning execution unit that performs contrastive learning on the user's biosignal measurement data for contrastive learning pre-stored in the memory unit, as in Cheng et al, for the purpose of reducing a number of downstream tasks for classifying the data.
As to claim 5, modified Shusterman figures 9 - 11 shows and teaches an automatic sleep stage classification system for reducing the variation in performance between users using a contrastive learning method,
wherein the classification server comprises a second feature extraction unit that extracts the user' unique biosignal feature for contrastive learning from the user's biosignal measurement data for contrastive learning pre-stored in the memory unit.
(paragraph [0037]… An output unit 60 includes a screen or a set of indicators for displaying the ECG waveforms and the computed primary elements in comparison with the previously stored primary elements or in comparison with the default reference values. The results of comparison can be represented both qualitatively and quantitatively in the dynamic and static modes. Abnormal readings may be further classified into moderately abnormal and severely abnormal. To make the indicators understandable to a lay person, the degree of abnormality may be color-coded: green color corresponds to a normal value, yellow corresponds to a moderate abnormality, and red corresponds to a severe abnormality. In the dynamic mode, the quantitative representation shows the differences between the newly acquired and stored primary elements and waveforms, whereas the qualitative representation includes indication of each parameter as being changed (C) or unchanged (U))(Examiner’s Note: “output unit 60 includes a screen or a set of indicators for displaying the ECG waveforms and the computed primary elements” reads on “a second feature extraction unit that extracts the user' unique biosignal feature for contrastive learning from the user's biosignal measurement data for contrastive learning pre-stored in the memory unit”).
As to claim 8, modified Shusterman figures 9 - 11 shows and teaches an automatic sleep stage classification system for reducing the variation in performance between users using a contrastive learning method, wherein the user terminal receives the user's sleep stage classification result from the classification server (paragraph [0117]…an extreme deviation from an individual's historical PBF during psychological stress can be used for biofeedback or serve as an indicator of a high stress level and the need for stress management or medication. ; paragraph [0137… an output unit 60 includes a screen or a set of indicators for displaying the ECG waveforms and the computed primary elements in comparison with the previously stored primary elements or in comparison with the default reference values. The results of comparison can be represented both qualitatively and quantitatively in the dynamic and static modes. Abnormal readings may be further classified into moderately abnormal and severely abnormal).
As to claim 9, modified Shusterman figures 9 - 11 shows and teaches an automatic sleep stage classification system for reducing the variation in performance between users using a contrastive learning method, wherein the user terminal comprises a display unit that outputs the user's sleep stage classification result received from the classification server (paragraph [0137… an output unit 60 includes a screen or a set of indicators for displaying the ECG waveforms and the computed primary elements in comparison with the previously stored primary elements or in comparison with the default reference values. The results of comparison can be represented both qualitatively and quantitatively in the dynamic and static modes. Abnormal readings may be further classified into moderately abnormal and severely abnormal).
Claim 10 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons.
Conclusion
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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