DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 2/19/2024 Claims 1-12 and 16-23 are pending in this Office Action.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1,11 and 19 are objected to because of the following informalities: Claims 1,11 and 19 recite the term "and/or", which is selective language, the examiner suggests using either the "and" term or the "or" term, otherwise the claims should be worded in a clearer fashion to claim both terms. For the purpose of this examination the examiner is selecting the "or" term from this selective language. Appropriate correction is required.
Claim 18 recites “wherein the instructions further cause the biological sensor to one or more of filter or normalize the biological sensor data generated by the at least one measuring unit” which appears to be grammatically incorrect because “cause the biological to one or more of filter” appears to be missing a verb.
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 1-12 and 16-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 11 and 19:
Step 1: Statutory Category ?: Yes. claim 1 recites a method (i.e., a “process”) , claim 11 recites a biological sensor (i.e., a “machine”) and claim 19 recites a computer computer readable medium (i.e., an article of manufacture) which are statutory categories.
Claim 1:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
Claim 1 recites one or more limitations that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper:
“classifying a quality of the at least one signal by using at least one trained trainable model”. Except for the “using one or more trainable model” there is nothing in the claim that prevents the step from being performed in the human mind.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 1 recites additional elements of “providing biological sensor data obtained by at least one biological sensor, wherein the biological sensor data comprises at least one signal” which is simply data gathering step and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)).
Claim 1 further recites additional elements of “wherein the trainable model is trained on historical biological sensor data based on a supervised and/or semi-supervised deep learning architecture, wherein the trainable model is trained by optimizing one loss function in terms of classification or two loss functions in terms of signal reconstruction and classification” . The trainable model amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f))
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, data gathering is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 1 therefore is ineligible.
Claim 2 recites additional element of “wherein the at least one biological sensor is at least one portable photoplethysmogram device and the biological sensor data comprises at least one photoplethysmogram obtained by the at least one portable photoplethysmogram device” which is simply data gathering step and therefore are insignificant extra-solution activities (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 2 therefore is ineligible.
Claim 3 recites additional element of “wherein the quality of the at least one signal is used as a quality indicator for heart rate variability data, wherein the quality is used for distinguishing between acceptable and non-acceptable heart rate variability data” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 3 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 3 is not patent eligible.
Claim 4 recites additional element of “wherein classifying the quality of the at least one signal comprises discriminating between noisy and clean signals” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 4 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 4 is not patent eligible.
Claim 5 recites additional element of “wherein the trainable model comprises at least one deep neural network selected from the group consisting of a Convolutional Neural Network (CNN), a recurrent neural network (RNN), and a Long short-term memory (LSTM)”. The trainable model amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f)). Even when considered in combination, the additional elements do not provide an inventive concept, claim 5 therefore is ineligible.
Claim 6 recites additional element of “training the trainable model on the at least one training dataset comprising the historical biological sensor data, based on the supervised and/or semi-supervised deep learning architecture. The trainable model amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f)). Even when considered in combination, the additional elements do not provide an inventive concept, claim 6 therefore is ineligible.
Claim 7 recites additional element of “wherein the historical biological sensor data comprises manually labeled historical biological sensor data; and wherein training the trainable model comprises training the trainable model on the at least one dataset comprising the manually labeled historical biological sensor data based on the supervised deep learning architecture”. The trainable model amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f)). Even when considered in combination, the additional elements do not provide an inventive concept, claim 7 therefore is ineligible.
Claim 8 recites additional element of “wherein the historical biological sensor data comprises manually labeled historical biological sensor data and unlabeled historical biological sensor data; and wherein training the trainable model comprises training the trainable model on the at least one dataset comprising the manually labeled and the unlabeled historical biological sensor data is used for training the trainable model (119) based on the semi-supervised deep learning architecture” which is simply data gathering step and therefore are insignificant extra-solution activities (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 8 therefore is ineligible.
Claim 9 recites additional element of “wherein for the unlabeled historical biological sensor data, the trainable model is trained by optimizing the loss function in terms of signal reconstruction and by disregarding the loss function in terms of classification” The trainable model amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f)). Even when considered in combination, the additional elements do not provide an inventive concept, claim 9 therefore is ineligible.
Claim 10 recites additional element of “preprocessing the biological sensor data by one or more of filtering or normalizing the biological sensor data” which is simply data gathering step and therefore are insignificant extra-solution activities (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 10 therefore is ineligible.
Claim 11:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
Claim 11 recites one or more limitations that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper:
“classify a quality of the at least one signal by using at least one trained trainable model”. Except for the “using one or more trainable model” there is nothing in the claim that prevents the step from being performed in the human mind.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 11 recites additional elements of “generate biological sensor data comprises at least one signal” which is simply data gathering step and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)). The additional elements of “measuring unit” and “processing unit” amount to no more than mere instructions to apply the exception using generic computer components.
Claim 11 further recites additional elements of “wherein the trainable model is trained on historical biological sensor data based on a supervised and/or semi-supervised deep learning architecture, wherein the trainable model is trained by optimizing one loss function in terms of classification or two loss functions in terms of signal reconstruction and classification” . The trainable model amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f))
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 11 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, data gathering is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). The “measuring unit” and “processing unit” are at best equivalent of adding the word “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 11 therefore is ineligible.
Claim 12 recites additional element of “wherein the instructions further cause the biological sensor to classify a quality of the photoplethysmogram by using the trained with the trainable model” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. The additional element of “at least one illumination source; and at least one photodetector, wherein the at least one illumination source and the at least one photodetector are configured for to provide at least one photoplethysmogram” which is simply data gathering step and therefore are insignificant extra-solution activities (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. . Even when considered in combination, the additional elements do not provide an inventive concept, claim 12 therefore is ineligible.
Claim 16 recites additional element of “wherein to classify the quality of the at least one signal comprises discriminating between noisy and clean signals” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 16 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 16 is not patent eligible.
Claim 17 recites additional element of “wherein the trainable model is trained with labeled historical biological sensor data and unlabeled historical biological sensor data based on the semi-supervised deep learning architecture” . The trainable model amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f)). Even when considered in combination, the additional elements do not provide an inventive concept, claim 17 therefore is ineligible.
Claim 18 recites additional element of “wherein the instructions further cause the biological sensor to one or more of filter or normalize the biological sensor data generated by the at least one measuring unit” which is simply data gathering step and therefore are insignificant extra-solution activities (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 18 therefore is ineligible.
Claim 19:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
Claim 19 recites one or more limitations that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper:
“classify a quality of the at least one signal by using at least one trainable model”. Except for the “using one or more trained model” there is nothing in the claim that prevents the step from being performed in the human mind.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 19 recites additional elements of “generate biological sensor data comprises at least one signal” which is simply data gathering step and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)). The additional elements of “biological sensor” amount to no more than mere instructions to apply the exception using generic computer component.
Claim 19 further recites additional elements of “wherein the trainable model is trained on historical biological sensor data based on a supervised and/or semi-supervised deep learning architecture, wherein the trainable model is trained by optimizing one loss function in terms of classification or two loss functions in terms of signal reconstruction and classification” . The trainable model amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f))
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 19 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, data gathering is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). The “biological sensor” is at best equivalent of adding the word “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 19 therefore is ineligible.
Claim 20 recites additional element of “wherein the biological sensor is a portable photoplethysmogram device and the biological sensor data comprises at least one photoplethysmogram obtained by the at least one portable photoplethysmogram device” which is simply data gathering step and therefore are insignificant extra-solution activities (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 20 therefore is ineligible.
Claim 21 recites additional element of “wherein to classify a quality of the at least one signal by using a trainable model comprises to discriminate between noisy signals and clean signals” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 16 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 21 is not patent eligible.
Claim 22 recites additional element of “wherein the trainable model is trained with labeled historical biological sensor data and unlabeled historical biological sensor data based on the semi-supervised deep learning architecture”. The trainable model amounts to no more than mere instructions to apply an abstract idea on a computer or merely using a computer as a tool to perform the abstract idea.(See MPEP 2106.05(f)). Even when considered in combination, the additional elements do not provide an inventive concept, claim 22 therefore is ineligible.
Claim 23 recites additional element of “wherein the instructions further cause the biological sensor to one or more of filter or normalize the generated biological sensor data” which is simply data gathering step and therefore are insignificant extra-solution activities (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 23 therefore is ineligible.
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 1-7, 10-12,16,18-21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Kaminsky et al.(US Patent Application Publication 2022/0015713 A1, hereinafter “Kaminsky “ ) and further in view of Landgraf et al.(US Patent Application Publication 2021/0345934 A1, hereinafter “Landgraf”)
As to claim 1, Kaminsky teaches a computer-implemented method for classifying quality of biological sensor data comprising:
providing biological sensor data obtained by at least one biological sensor, wherein the biological sensor data comprises at least one signal , ( Kaminsky par [0063] teaches continuously acquired PPG signal may be received based on a continuous video stream of a target subject) ; and classifying a quality of the at least one signal by using at least one trained trainable model ( Kaminsky par [0065] teaches a trained machine learning model may be applied in real time to generate a prediction and/or classification of each signal time window into one or more quality categories such as good quality and noisy)
wherein the trainable model is trained on historical biological sensor data based on a supervised and/or semi-supervised deep learning architecture,( Kaminsky par [0056]-[0057] teaches training set annotations are performed by specialists)
[wherein the trainable model is trained by optimizing one loss function in terms of classification or two loss functions in terms of signal reconstruction and classification]
Kaminsky fails to expressly teach [wherein the trainable model is trained by optimizing one loss function in terms of classification or two loss functions in terms of signal reconstruction and classification].
However, Landgraf teaches wherein the trainable model is trained by optimizing one loss function in terms of classification or two loss functions in terms of signal reconstruction and classification .( Landgraf par [0168] teaches after each epoch, classifier loss may be evaluated based on validation set)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Kaminsky and Landgraf to achieve the claimed invention. One would have been motivated to make such combination to achieve effective performance.(Landgraf par [0168])
As to claim 2, Kaminsky and Landgraf teach the method according of claim 1,wherein the at least one biological sensor is at least one portable photoplethysmogram device and the biological sensor data comprises at least one photoplethysmogram obtained by the at least one portable photoplethysmogram device. (Kaminsky par [0026] teaches camera based measurement of blood oxygen levels provides a contact less alternative to conventional photoplethymography)
As to claim 3, Kaminsky and Landgraf teach the method of claim 2,wherein the quality of the at least one signal is used as a quality indicator for heart rate variability data, wherein the quality is used for distinguishing between acceptable and non-acceptable heart rate variability data. (Kaminsky par [0029] teaches an optically-obtained physiological signal (e.g., PPG) may be used to determine one or more subsequent physiological, diagnostic parameter values, including for example heart-rate, blood flow, blood pressure, blood oxygenation levels, ankle-brachial index (ABI), toe-brachial index (TBI), R-peak amplitude, and the like. Embodiments of the invention may enable diagnosis of the subject based on a quality parameter of said optically-obtained physiological signal (e.g., PPG). For example, embodiments of the invention may produce an indication of a quality parameter (e.g., Signal to Noise (SNR)) of said at least one optically-obtained physiological signal and/or physiological, diagnostic parameter values, to indicate whether said diagnosis is reliable or not)
As to claim 4, Kaminsky and Landgraf teach the method , of claim 1 wherein classifying the quality of the at least one signal comprises discriminating between noisy and clean signals. (Kaminsky par [0065] teaches “good quality “ and “noisy” )
As to claim 5, Kaminsky and Landgraf teach the method a of claim 1 wherein the trainable model comprises at least one deep neural network selected from the group consisting of a Convolutional Neural Network (CNN), a recurrent neural network (RNN), and a Long short-term memory (LSTM). (Kaminsky par [0059] teaches the machine learning model may be any one of SVM, KNN, Random Forest. CNNN or RNN is well known in the art)
As to claim 6, Kaminsky and Landgraf teach the method of claim 1, further comprising training the trainable model on the at least one training dataset comprising the historical biological sensor data, based on the supervised and/or semi-supervised deep learning architecture. (Kaminsky par [0056]-[0057] teaches training set annotations are performed by specialists)
As to claim 7, Kaminsky and Landgraf teach the method of claim 6, wherein the historical biological sensor data comprises manually labeled historical biological sensor data (Kaminsky par [0056]-[0057] teaches training set annotations are performed by specialists); and wherein training the trainable model comprises training the trainable model on the at least one dataset comprising the manually labeled historical biological sensor data is used for training the trainable model based on the supervised deep learning architecture.( Kaminsky par [0010] teaches at the training stage, train a machine learning model on a training set comprising feature sets and labels indicating a quality parameters)
As to claim 10, Kaminsky and Landgraf teach the method of claim 1, further comprising preprocessing the biological sensor data by one or more of filtering or normalizing the biological sensor data. (Kaminsky par [0064] teaches the PPG signal may be divided into consecutive overlapping signal time windows)
Claim 11 merely recites a biological sensor to perform the method of claim 1. Accordingly, Kaminsky and Landgraf teach every limitation of claim 11 as indicates in the above rejection of claim 1.
As to claim 12, Kaminsky and Landgraf teach the biological sensor of claim 11, , wherein the biological sensor is a portable photoplethysmogram device wherein the portable photoplethysmogram device (Kaminsky par [0026] teaches camera based measurement of blood oxygen levels provides a contact less alternative to conventional photoplethymography) and further comprises; at least one illumination source; and at least one photodetector,( Kaminsky par [0063] teaches the video data may be acquired using a number of acquisition modalities, e.g. RGB, monochrome imaging, near infrared (NIR), Sort-wave infrared (SWIR)..etc.)
wherein the at least one illumination source and the at least one photodetector are configured to provide at least one photoplethysmogram; and wherein the instructions further cause the biological sensor to classify a quality of the photoplethysmogram with the trainable model. (Kaminsky par [0065] teaches a trained model may be applied in real time to generate a prediction and/or classification)
As to claim 16, Kaminsky and Landgraf teach the biological sensor of claim 11, wherein to classify the quality of the at least one signal comprises discriminating between noisy and clean signals. (Kaminsky par [0065] teaches “good quality “ and “noisy” )
the training stage, train a machine learning model on a training set comprising feature sets and labels indicating a quality parameters)
As to claim 18, Kaminsky and Landgraf teach the biological sensor of claim 11, wherein the instructions further cause the biological sensor to one or more of filter or normalize the biological sensor data generated by the at least one measuring unit. (Kaminsky par [0064] teaches the PPG signal may be divided into consecutive overlapping signal time windows)
Claims 19,20,21 and 23 merely recite a computer readable storage medium comprising instructions when executed by a processor, performs the method of claims 1,2,4 and 10 respectively. Accordingly, Kaminsky and Landgraf teach every limitation of claims 19,20,21 and 23 as indicates in the above rejection of claims 1,2,4 and 10 respectively.
Claims 8-9, 17 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Kaminsky , Landgraf and further in view of Zhang et al.(US Patent Application Publication 2018/0165554 A1, hereinafter “Zhang”)
As to claim 8, Kaminsky and Landgraf teach the method of claim 6, wherein the historical biological sensor data comprises manually labeled historical biological sensor data and [unlabeled historical biological sensor data] ; and wherein training the trainable model comprises training the trainable model on the at least one dataset comprising the manually labeled and [the unlabeled historical biological sensor data] based on the semi-supervised deep learning architecture. (Kaminsky par [0056]-[0057] teaches training set annotations are performed by specialists. Kaminsky par [0010] teaches at the training stage, train a machine learning model on a training set comprising feature sets and labels indicating a quality parameters)
Kaminsky and Landgraf fail to expressly teach unlabeled historical biological sensor data and training based on the unlabeled historical biological sensor data.
However, Zhang teaches unlabeled data and training based on unlabeled data. (Zhang par [0048] teaches the effectiveness of the model was evaluated on six sentiment analysis datasets, and was shown to significantly outperform all the competing methods with respect to classification accuracy. The model is able to take advantage of unlabeled datasets and get improved performance)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Kaminsky, Landgraf and Zhang to achieve the claimed invention. One would have been motivated to make such combination to improve performance.( Zhang par [0048])
As to claim 9, Kaminsky , Landgraf and Zhang teach the method of claim 8, wherein for the unlabeled historical biological sensor data, the trainable model is trained by optimizing the loss function in terms of signal reconstruction and by disregarding the loss function in terms of classification. ( Landgraf par [0168] teaches after each epoch, classifier loss may be evaluated based on validation set)
As to claim 17, Kaminsky and Landgraf teach the biological sensor of claim 11, wherein the trainable model is trained with labeled historical biological sensor data and [unlabeled historical biological sensor data ] based on the semi-supervised deep learning architecture. ( Kaminsky par [0056]-[0057] teaches training set annotations are performed by specialists. Kaminsky par [0010] teaches at the training stage, train a machine learning model on a training set comprising feature sets and labels indicating a quality parameters)
Kaminsky and Landgraf fail to expressly teach the training is based on unlabeled historical biological sensor data.
However, Zhang teaches the training is based on unlabeled data. (Zhang par [0048] teaches the effectiveness of the model was evaluated on six sentiment analysis datasets, and was shown to significantly outperform all the competing methods with respect to classification accuracy. The model is able to take advantage of unlabeled datasets and get improved performance)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Kaminsky, Landgraf and Zhang to achieve the claimed invention. One would have been motivated to make such combination to improve performance.( Zhang par [0048])
As to claim 22, see the above rejection of claim 17.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cheng et al., US Patent Application Publication 2021/0374570 A1, par [0026] discloses classifying data by self-supervising learning. Kamath et al., US Patent Application Publication 2009/0192745 A1. The abstract discloses classification of a level of noise of a sensor signal.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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