DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-15, 17-20, and 29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Specifically, representative Claim 1 recites: “ A system, comprising: at least one storage device including a set of instructions; at least one processor in communication with the at least one storage device, wherein when executing the set of instructions, the at least one processor is configured to cause the system to perform operations including: obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device in a pre-scan, the pre-scan being performed on a subject before an MRI scan of the subject; obtaining a first fault detection model, the first fault detection model being a trained machine learning model; and determining whether the coil has a failure based on the one or more sets of reference signals and the first fault detection model . ” The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”. Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (machine). Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mental processes — concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion. For example, the steps of “ determining whether the coil has a failure based on the one or more sets of reference signals and the first fault detection model ” are treated as belonging to mental process grouping. With regards to the steps of “ determining whether the coil has a failure based on the one or more sets of reference signals and the first fault detection model ”, this mental step represents a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. In the context of this claim, it encompasses the user making mental decisions (evaluation/judgement) with regards to determining whether the coil has a failure . Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. The above claims comprise the following additional elements: Claim 1: A system, comprising: at least one storage device including a set of instructions; at least one processor in communication with the at least one storage device, wherein when executing the set of instructions, the at least one processor is configured to cause the system to perform operations including: obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device in a pre-scan, the pre-scan being performed on a subject before an MRI scan of the subject; obtaining a first fault detection model, the first fault detection model being a trained machine learning model Claim 15 : A method implemented on a computing device having at least one processor and at least one storage device, comprising: obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device in a pre-scan, the pre-scan being performed on a subject before an MRI scan of the subject; obtaining a first fault detection model, the first fault detection model being a trained machine learning model Claim 29 : A non-transitory computer readable medium including executable instructions, the instructions, when executed by at least one processor, causing the at least one processor to effectuate a method comprising: obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device in a pre-scan, the pre-scan being performed on a subject before an MRI scan of the subject; obtaining a first fault detection model, the first fault detection model being a trained machine learning model The above additional elements in Claim 1 such as a system, comprising: at least one storage device including a set of instructions; at least one processor in communication with the at least one storage device, wherein when executing the set of instructions, the at least one processor is configured to cause the system to perform operations including: is recited in generality and field of use which is part of an expanded abstract idea, obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device in a pre-scan, the pre-scan being performed on a subject before an MRI scan of the subject; obtaining a first fault detection model, the first fault detection model being a trained machine learning model are examples of data gathering and are generically recited and are not meaningful. The additional elements in c laims 1, 15, and 29 such a computer, a processor, and a non-transitory computer-readable medium comprising instructions that are executable by a processor for causing the processor is an example of generic computer equipment (components) that is generally recited and, therefore, is not qualified as a particular machine. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B. However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record including references in the submitted IDS ( 09 /0 2 /2025) by the Applicant ( Van Wieringen and Huang ). The independent claims, therefore, are not patent eligible. With regards to the dependent claims, claims 2- 14 , 17-20, and 2 9 provide additional features/steps which are either part of an expanded abstract idea of the independent claims (additionally comprising mathematical (Claims 2-14, 17-20, and 29 ) or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine/ and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record. Regarding Claim 14, Van Wieringen discloses obtaining device information of the MRI device (When a failure of a gradient amplifier occurs in an MRI system the measurements from sensors available in and around the gradient amplifier are collected [0079]); and determining whether the coil has a failure based on the one or more sets of reference signals, the first fault detection model, and the device information (The scanned parameter data may for instance be data derived from a so called pulse sequence which describes the usage of the gradient coil amplifier during the acquisition of magnetic resonance data [0025]; Next in step 202 a probability 118 of the failure of a gradient coil amplifier of a magnetic resonance imaging system calculated for a predetermined number of days in the future by inputting the measurement vector 114 into the trained neural network program 124 [0065]). 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. Claim s 1- 2, 10-12, and 14- 15 are rejected under 35 U.S.C. 103 as being unpatentable over Van Wieringen et al. (US 201 60327606 ), hereinafter referred to as ‘ Van Wieringen ' and in further view of Huang et al. (US 20 120002859 ), hereinafter referred to as ‘ Huang ' . Regarding Claim 1, Van Wieringen discloses a system, comprising: at least one storage device including a set of instructions; at least one processor in communication with the at least one storage device (The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A ‘computer-readable storage medium’ as used herein encompasses any tangible storage medium which may store instructions which are executable by a processor of a computing device [0006]) , wherein when executing the set of instructions, the at least one processor is configured to cause the system to perform operations including (A hardware interface may allow a processor to send control signals or instructions to an external computing device and/or apparatus. A hardware interface may also enable a processor to exchange data with an external computing device and/or apparatus [0016]) : obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device (Magnetic Resonance (MR) data is defined herein as being the recorded measurements of radio frequency signals emitted by atomic spins by the antenna of a Magnetic resonance apparatus during a magnetic resonance imaging scan [0018] ; In another embodiment execution of the instructions further causes the processor to store scanned parameter data in the measurement database. Scanned parameter data as used herein is descriptive of the usage of the gradient coil amplifier during acquisition of the magnetic resonance data. The scanned parameter data may for instance be data derived from a so called pulse sequence which describes the usage of the gradient coil amplifier during the acquisition of magnetic resonance data [0025] ) ; obtaining a first fault detection model ( Execution of the instructions further cause the processor to repeatedly calculate a probability of failure of a gradient coil amplifier of the magnetic resonance imaging system a predetermined number of days in the future by inputting the measurement vector into a trained neural network program [ 0020 ]) , the first fault detection model being a trained machine learning model (Execution of the instructions further cause the processor to repeatedly calculate a probability of failure of a gradient coil amplifier of the magnetic resonance imaging system a predetermined number of days in the future by inputting the measurement vector into a trained neural network program [0020]) ; and determining whether the coil has a failure based on the one or more sets of reference signals and the first fault detection model (The method further comprises the step of repeatedly calculating a probability of failure of a gradient coil amplifier of the magnetic resonance imaging system a predetermined number of days in the future by inputting the measurement vector into a trained neural network program [0047]) . However, Van Wieringen discloses obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device in a pre-scan and the pre-scan being performed on a subject before an MRI scan of the subject. Nevertheless, Huang discloses obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device in a pre-scan (In accordance with one disclosed aspect, a method comprises: acquiring initial sensitivity maps for a plurality of radio frequency coils using a magnetic resonance (MR) pre-scan of a subject [0006]) and the pre-scan being performed on a subject before an MRI scan of the subject (In accordance with another disclosed aspect, a method comprises: ( i ) acquiring sensitivity maps for a plurality of radio frequency coils using a magnetic resonance (MR) pre-scan of a subject [0007]) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen with the teachings of Huang to acquire/store initial sensitivity maps and improve accuracy of the magnetic resonance scan data . Regarding Claim 2, Van Wieringen and Huang disclose the claimed invention discussed in Claim 1. Van Wieringen discloses the one or more sets of reference signals include a set of MR signals collected in an acquisition that is performed on the subject after an excitation pulse is applied to the subject ( The computer storage 110 is further shown as containing a pulse sequence 336. The pulse sequence 336 may be instructions or data which may be converted into instructions which enable the processor 106 to control the magnetic resonance imaging system 302 to acquire magnetic resonance data 337 [0071] ) . Regarding Claim 10, Van Wieringen and Huang disclose the claimed invention discussed in Claim 1. Van Wieringen discloses the operations further :in response to determining the coil has a failure, determining a position of the failure using the first fault detection model (as discussed above). Regarding Claim 11, Van Wieringen and Huang disclose the claimed invention discussed in Claim 10. Van Wieringen discloses the coil includes a plurality of channels (Adjacent to the imaging zone 308 is a radio-frequency coil 314 for manipulating the orientations of magnetic spins within the imaging zone 308 and for receiving radio transmissions from spins also within the imaging zone 308. The radio frequency antenna may contain multiple coil elements. The radio frequency antenna may also be referred to as a channel or antenna [0068]), and the position of the failure includes a series number of a channel that has the failure among the plurality of channels (Execution of the instructions further cause the processor to repeatedly calculate a probability of failure of a gradient coil amplifier of the magnetic resonance imaging system a predetermined number of days in the future by inputting the measurement vector into a trained neural network program [0046]) . Regarding Claim 12, Van Wieringen and Huang disclose the claimed invention discussed in Claim 1. Van Wieringen discloses the operations further comprising: in response to determining the coil has a failure (as discussed above), determining a type or a level of the failure using one or more second fault detection models different from the first fault detection model (In another embodiment execution of the instructions further cause the processor to train the trained neural network using the measurement database and/or historical gradient coil amplifier failure database. Using a historical record of when gradient coil amplifiers have failed measurement vectors can be constructed from the historical data [0024]). Regarding Claim 1 4 , Van Wieringen and Huang disclose the claimed invention discu ssed in Claim 1. Van Wieringen discloses obtaining device information of the MRI device (When a failure of a gradient amplifier occurs in an MRI system the measurements from sensors available in and around the gradient amplifier are collected [0079]); and determining whether the coil has a failure based on the one or more sets of reference signals, the first fault detection model, and the device information (The scanned parameter data may for instance be data derived from a so called pulse sequence which describes the usage of the gradient coil amplifier during the acquisition of magnetic resonance data [0025]; Next in step 202 a probability 118 of the failure of a gradient coil amplifier of a magnetic resonance imaging system calculated for a predetermined number of days in the future by inputting the measurement vector 114 into the trained neural network program 124 [0065]). Regarding Claim 15, Van Wieringen discloses a method implemented on a computing device having at least one processor and at least one storage device, comprising: (The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A ‘computer-readable storage medium’ as used herein encompasses any tangible storage medium which may store instructions which are executable by a processor of a computing device [0006]), obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device (Magnetic Resonance (MR) data is defined herein as being the recorded measurements of radio frequency signals emitted by atomic spins by the antenna of a Magnetic resonance apparatus during a magnetic resonance imaging scan [0018]; In another embodiment execution of the instructions further causes the processor to store scanned parameter data in the measurement database. Scanned parameter data as used herein is descriptive of the usage of the gradient coil amplifier during acquisition of the magnetic resonance data. The scanned parameter data may for instance be data derived from a so called pulse sequence which describes the usage of the gradient coil amplifier during the acquisition of magnetic resonance data [0025]); obtaining a first fault detection model (Execution of the instructions further cause the processor to repeatedly calculate a probability of failure of a gradient coil amplifier of the magnetic resonance imaging system a predetermined number of days in the future by inputting the measurement vector into a trained neural network program [0020]), the first fault detection model being a trained machine learning model (Execution of the instructions further cause the processor to repeatedly calculate a probability of failure of a gradient coil amplifier of the magnetic resonance imaging system a predetermined number of days in the future by inputting the measurement vector into a trained neural network program [0020]); and determining whether the coil has a failure based on the one or more sets of reference signals and the first fault detection model (The method further comprises the step of repeatedly calculating a probability of failure of a gradient coil amplifier of the magnetic resonance imaging system a predetermined number of days in the future by inputting the measurement vector into a trained neural network program [0047]). However, Van Wieringen discloses obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device in a pre-scan and the pre-scan being performed on a subject before an MRI scan of the subject. Nevertheless, Huang discloses obtaining one or more sets of reference signals collected by a coil of a magnetic resonance imaging (MRI) device in a pre-scan (In accordance with one disclosed aspect, a method comprises: acquiring initial sensitivity maps for a plurality of radio frequency coils using a magnetic resonance (MR) pre-scan of a subject [0006]) and the pre-scan being performed on a subject before an MRI scan of the subject (In accordance with another disclosed aspect, a method comprises: ( i ) acquiring sensitivity maps for a plurality of radio frequency coils using a magnetic resonance (MR) pre-scan of a subject [0007]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen with the teachings of Huang to acquire/store initial sensitivity maps and improve accuracy of the magnetic resonance scan data. Claims 3-4 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Van Wieringen and Huang, and further in view of Ookawa et al. (US20080231269) hereinafter referred to as ‘ Ookawa ’. Regarding Claim 3, Van Wieringen and Huang disclose the claimed invention discussed in Claim 1. Van Wieringen discloses the one or more sets of reference signals include a set of signals collected in an acquisition that is performed on the subject (as discussed above) . However, Van Wieringen does not explicitly disclose the one or more sets of reference signals include a set of noise signals collected in an acquisition that is performed on the subject without applying an excitation pulse to the subject. Nevertheless, Huang discloses collected in an acquisition that is performed on the subject without applying an excitation pulse to the subject (as discussed above). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen with the teachings of Huang to acquire/store initial sensitivity maps and improve accuracy of the magnetic resonance scan data. However, the combination does not explicitly disclose the one or more sets of reference signals include a set of noise signals collected in an acquisition. Nevertheless, Ookawa discloses the one or more sets of reference signals include a set of noise signals collected in an acquisition (However, another abnormality can be detected using the collected data. For example, an abnormality in the channel, a gradient magnetic field for readout, and the like can be identified through detection of a spike-shaped signal in the raw data of all channels, a spike-shaped signal in the raw data of only some channels, a constant noise generated in a readout direction of the reconstructed data, and the like [0072]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen and Huang with the teachings of Ookawa to obtain a signal to noise ratio to improve the accuracy of identifying abnormalities in the coil . Regarding Claim 4 , Van Wieringen and Huang disclose the claimed invention discussed in Claim 3 . Van Wieringen discloses the determining whether the coil has a failure based on the one or more sets of reference signals and the first fault detection model includes (as discussed above) . However, Van Wieringen does not explicitly disclose determining a distribution of the set of noise signals; and determining whether the coil has a failure based on the distribution of the set of noise signals and the first fault detection model. Nevertheless, Ookawa discloses determining a distribution of the set of noise signals (as discussed above); and determining whether the coil has a failure based on the distribution of the set of noise signals ( However, another abnormality can be detected using the collected data. For example, an abnormality in the channel, a gradient magnetic field for readout, and the like can be identified through detection of a spike-shaped signal in the raw data of all channels, a spike-shaped signal in the raw data of only some channels, a constant noise generated in a readout direction of the reconstructed data, and the like [0072] ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen and Huang with the teachings of Ookawa to obtain a signal to noise ratio to improve the accuracy of identifying abnormalities in the coil. Regarding Claim 17, Van Wieringen and Huang disclose the claimed invention discussed in Claim 15. Van Wieringen discloses the one or more sets of signals collected in an acquisition that is performed on the subject (as discussed above). However, Van Wieringen does not explicitly disclose the one or more sets of reference signals include a set of noise signals collected in an acquisition that is performed on the subject without applying an excitation pulse to the subject. Nevertheless, Huang discloses collected in an acquisition that is performed on the subject without applying an excitation pulse to the subject (as discussed above). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen with the teachings of Huang to acquire/store initial sensitivity maps and improve accuracy of the magnetic resonance scan data. However, the combination does not explicitly disclose the one or more sets of reference signals include a set of noise signals collected in an acquisition. Nevertheless, Ookawa discloses the one or more sets of reference signals include a set of noise signals collected in an acquisition (However, another abnormality can be detected using the collected data. For example, an abnormality in the channel, a gradient magnetic field for readout, and the like can be identified through detection of a spike-shaped signal in the raw data of all channels, a spike-shaped signal in the raw data of only some channels, a constant noise generated in a readout direction of the reconstructed data, and the like [0072]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen and Huang with the teachings of Ookawa to obtain a signal to noise ratio to improve the accuracy of identifying abnormalities in the coil. Regarding Claim 18, Van Wieringen and Huang disclose the claimed invention discussed in Claim 17. Van Wieringen discloses the determining whether the coil has a failure based on the one or more sets of reference signals and the first fault detection model includes (as discussed above). However, Van Wieringen does not explicitly disclose determining a distribution of the set of noise signals; and determining whether the coil has a failure based on the distribution of the set of noise signals and the first fault detection model. Nevertheless, Ookawa discloses determining a distribution of the set of noise signals (as discussed above); and determining whether the coil has a failure based on the distribution of the set of noise signals (as discussed above). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen and Huang with the teachings of Ookawa to obtain a signal to noise ratio to improve the accuracy of identifying abnormalities in the coil. Claims 5-6, 8, 13 , and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Van Wieringen and Huang, and further in view of Dagher et al. (US20170059682) hereinafter referred to as ‘ Dagher ’. Regarding Claim 5 , Van Wieringen and Huang disclose the claimed invention discussed in Claim 4 . However, Van Wieringen does not explicitly disclose the determining a distribution of the set of noise signals includes: obtaining a set of reference noise signals; and determining, based on the set of noise signals and the set of reference noise signals, a probability distribution line representing the distribution of the set of noise signals in a two-dimensional probability space. Nevertheless, Ookawa discloses the determining a distribution of the set of noise signals includes (as discussed above) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen and Huang with the teachings of Ookawa to obtain a signal to noise ratio to improve the accuracy of identifying abnormalities in the coil. However, the combination does not explicitly disclose the determining a distribution of the set of noise signals includes: obtaining a set of reference noise signals; and determining, based on the set of noise signals and the set of reference noise signals, a probability distribution line representing the distribution of the set of noise signals in a two-dimensional probability space. Nevertheless, Dagher discloses the determining a distribution of the set of noise signals includes: obtaining a set of reference noise signals; and determining, based on the set of noise signals and the set of reference noise signals (Generally, there are three phase-imaging regimes that MR phase acquisition methods may operate in: (I) a regime dominated by phase-wrapping, with reduced levels of noise, (II) a regime dominated by noise, with minimal instances of phase-wrapping and (III) a regime where the original signal needs to be disambiguated from both phase-wrapping and noise contributions to MR phase measurement error [0050]), a probability distribution line representing the distribution of the set of noise signals in a two-dimensional probability space ( This ratio approximates snr.sub.k,c in Eq. (10) which allows rapid computation of the noise probability distribution [9], phase noise standard deviation [25] and phase wrapping probability distribution [7] [0165] ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen , Huang, and Ookawa with the teachings of Dagher to process and interpret magnetic resonance data and improve filtering noise and accuracy of signal estimation. Regarding Claim 6 , Van Wieringen and Huang disclose the claimed invention discussed in Claim 5 . Van Wieringen discloses the determining whether the coil has a failure based on the distribution of the set of signals and the first fault detection model includes (as discussed above) : determine a feature vector (The measurement vector is constructed using data stored in the measurement database. Repeatedly acquiring and storing the environmental sensor data for both the examination room and the technical room enables the measurement vector to be constructed using current data [0023]) ; and determining whether the coil has a failure by inputting the feature vector into the first fault detection model (Using a historical record of when gradient coil amplifiers have failed measurement vectors can be constructed from the historical data. This may be used to train the neural network using standard neural network techniques and/or software tools [0024]) . However, Van Wieringen does not explicitly disclose the determining whether the coil has a failure based on the distribution of the set of noise signals and the first fault detection model includes: determine a feature vector representing the probability distribution line. Nevertheless, Ookawa discloses the determining whether the coil has a failure based on the distribution of the set of noise signals (However, another abnormality can be detected using the collected data. For example, an abnormality in the channel, a gradient magnetic field for readout, and the like can be identified through detection of a spike-shaped signal in the raw data of all channels, a spike-shaped signal in the raw data of only some channels, a constant noise generated in a readout direction of the reconstructed data, and the like [0072]) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen and Huang with the teachings of Ookawa to obtain a signal to noise ratio to improve the accuracy of identifying abnormalities in the coil. However, the combination does not explicitly disclose determine a feature vector representing the probability distribution line. Nevertheless, Dagher discloses the probability distribution line ( In FIG. 6 and FIG. 7, Δβ = 0 Hz, and R.sub.max = 0, thus the likelihood is simply the noise distribution. In each of FIGs. 6 and 7, the dashed line is a Gaussian distribution with the same mean and standard deviation as the true noise distribution. Note the divergence between the noise probability distribution (solid curve and crosses) and the Gaussian approximation [00119] ) ; and determining whether the coil has a failure by inputting the feature vector into the first fault detection model ( In FIG. 6 and FIG. 7, Δβ = 0 Hz, and R.sub.max = 0, thus the likelihood is simply the noise distribution. In each of FIGs. 6 and 7, the dashed line is a Gaussian distribution with the same mean and standard deviation as the true noise distribution. Note the divergence between the noise probability distribution (solid curve and crosses) and the Gaussian approximation [00119] ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen , Huang, and Ookawa with the teachings of Dagher to process and interpret magnetic resonance data and improve filtering noise and accuracy of signal estimation. Regarding Claim 8, Van Wieringen and Huang disclose the claimed invention discussed in Claim 5. However, Van Wieringen does not explicitly disclose the obtaining a set of reference noise signals includes: obtaining a plurality sets of reference noise signals, each set of reference noise signals being collected by a normal coil in an acquisition that is performed on a reference subject without applying an excitation pulse to the reference subject; and selecting the set of reference noise signals from the plurality sets of reference noise signals. Nevertheless, Ookawa discloses the obtaining a set of reference noise signals includes (as discussed above): obtaining a plurality sets of reference noise signals (as discussed above), and selecting the set of reference noise signals (as discussed above). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen and Huang with the teachings of Ookawa to obtain a signal to noise ratio to improve the accuracy of identifying abnormalities in the coil. However, the combination does not explicitly disclose obtaining a plurality sets of reference noise signals, each set of reference noise signals being collected by a normal coil in an acquisition that is performed on a reference subject without applying an excitation pulse to the reference subject; and selecting the set of reference noise signals from the plurality sets of reference noise signals. Nevertheless, Dagher discloses obtaining a plurality sets of reference noise signals, each set of reference noise signals being collected by a normal coil in an acquisition that is performed on a reference subject without applying an excitation pulse to the reference subject; and selecting the set of reference noise signals from the plurality sets of reference noise signals (Generally, there are three phase-imaging regimes that MR phase acquisition methods may operate in: (I) a regime dominated by phase-wrapping, with reduced levels of noise, (II) a regime dominated by noise, with minimal instances of phase-wrapping and (III) a regime where the original signal needs to be disambiguated from both phase-wrapping and noise contributions to MR phase measurement error [0050]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen , Huang, Ookawa , and Dagher with the teachings of Shi to process and interpret magnetic resonance data and improve filtering noise and accuracy of signal estimation. Regarding Claim 13, Van Wieringen and Huang disclose the claimed invention discussed in Claim 12. Van Wieringen discloses the failure (as discussed above). However, Van Wieringen does not explicitly disclose the type of the failure includes at least one of a circuit disconnection, a failure of a receiving circuit, or a frequency offset of the coil. Nevertheless, Dagher discloses the type of the failure includes at least one of a circuit disconnection, a failure of a receiving circuit, or a frequency offset of the coil (A phase-offset φ.sub.0,c(x, y) corresponding to each channel c varies both spatially and across the channels, due to dependence on coil position, cable lengths, and electronic delay. Phase-offset φ.sub.0,c(x, y) is also known as a “receiver phase offset,” for example in Ref. 12, which describes phase-offset φ.sub.0,c(x, y) as a component of the MR signal phase that includes “contributions arising from the path length of the MR signal from a particular location in the object to the receiver coil in question given a finite MR wavelength. [0052]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen , Huang, and Ookawa with the teachings of Dagher to determine the performance and functionality of the coil and improve the performance while analyzing the signals’ transmission. Regarding Claim 19, Van Wieringen and Huang disclose the claimed invention discussed in Claim 18. However, Van Wieringen does not explicitly disclose the determining a distribution of the set of noise signals includes: obtaining a set of reference noise signals; and determining, based on the set of noise signals and the set of reference noise signals, a probability distribution line representing the distribution of the set of noise signals in a two-dimensional probability space. Nevertheless, Ookawa discloses the determining a distribution of the set of noise signals includes (as discussed above). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen and Huang with the teachings of Ookawa to obtain a signal to noise ratio to improve the accuracy of identifying abnormalities in the coil. However, the combination does not explicitly disclose the determining a distribution of the set of noise signals includes: obtaining a set of reference noise signals; and determining, based on the set of noise signals and the set of reference noise signals, a probability distribution line representing the distribution of the set of noise signals in a two-dimensional probability space. Nevertheless, Dagher discloses the determining a distribution of the set of noise signals includes: obtaining a set of reference noise signals; and determining, based on the set of noise signals and the set of reference noise signals (Generally, there are three phase-imaging regimes that MR phase acquisition methods may operate in: (I) a regime dominated by phase-wrapping, with reduced levels of noise, (II) a regime dominated by noise, with minimal instances of phase-wrapping and (III) a regime where the original signal needs to be disambiguated from both phase-wrapping and noise contributions to MR phase measurement error [0050]), a probability distribution line representing the distribution of the set of noise signals in a two-dimensional probability space (This ratio approximates snr.sub.k,c in Eq. (10) which allows rapid computation of the noise probability distribution [9], phase noise standard deviation [25] and phase wrapping probability distribution [7] [0165]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen , Huang, and Ookawa with the teachings of Dagher to process and interpret magnetic resonance data and improve filtering noise and accuracy of signal estimation. Regarding Claim 20, Van Wieringen and Huang disclose the claimed invention discussed in Claim 19. Van Wieringen discloses the determining whether the coil has a failure based the first fault detection model includes (as discussed above): determine a feature vector (as discussed above). However, Van Wieringen does not explicitly disclose the determining whether the coil has a failure based on the distribution of the set of noise signals and the first fault detection model includes: determine a feature vector representing the probability distribution line; and determining whether the coil has a failure by inputting the feature vector into the first fault detection model. Nevertheless, Ookawa discloses the determining whether the coil has a failure based on the distribution of the set of noise signals and the first fault detection model includes (However, another abnormality can be detected using the collected data. For example, an abnormality in the channel, a gradient magnetic field for readout, and the like can be identified through detection of a spike-shaped signal in the raw data of all channels, a spike-shaped signal in the raw data of only some channels, a constant noise generated in a readout direction of the reconstructed data, and the like [0072]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen and Huang with the teachings of Ookawa to obtain a signal to noise ratio to improve the accuracy of identifying abnormalities in the coil. However, the combination does not explicitly disclose determine a feature vector representing the probability distribution line; and determining whether the coil has a failure by inputting the feature vector into the first fault detection model. Nevertheless, Dagher discloses determine a feature vector representing the probability distribution line (In FIG. 6 and FIG. 7, Δβ = 0 Hz, and R.sub.max = 0, thus the likelihood is simply the noise distribution. In each of FIGs. 6 and 7, the dashed line is a Gaussian distribution with the same mean and standard deviation as the true noise distribution. Note the divergence between the noise probability distribution (solid curve and crosses) and the Gaussian approximation [00119]) ; and determining whether the coil has a failure by inputting the feature vector into the first fault detection model (In FIG. 6 and FIG. 7, Δβ = 0 Hz, and R.sub.max = 0, thus the likelihood is simply the noise distribution. In each of FIGs. 6 and 7, the dashed line is a Gaussian distribution with the same mean and standard deviation as the true noise distribution. Note the divergence between the noise probability distribution (solid curve and crosses) and the Gaussian approximation [00119]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen , Huang, and Ookawa with the teachings of Dagher to process and interpret magnetic resonance data and improve filtering noise and accuracy of signal estimation. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Van Wieringen and Huang, and further in view of Shi et al. (US20210088605) hereinafter referred to as ‘Shi’. Regarding Claim 7 , Van Wieringen and Huang disclose the claimed invention discussed in Claim 5 . Van Wieringen discloses the first fault detection model comprising a support vector machine (SVM) model (as discussed above) , wherein the determining whether the coil has a failure based on the distribution of the set of noise signals and the first fault detection model includes (as discussed above) . However, Van Wieringen does not explicitly disclose the first fault detection model comprising a support vector machine (SVM) model, wherein the determining whether the coil has a failure based on the distribution of the set of noise signals and the first fault detection model includes: determining one or more decision boundaries based on the SVM model; determining a position of the probability distribution line relative to each of the one or more decision boundaries; and determining, whether the coil has a failure based on the position of the probability distribution line relative to each of the one or more decision boundaries. Nevertheless, Ookawa discloses the set of noise signals (as discussed above) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen and Huang with the teachings of Ookawa to obtain a signal to noise ratio to improve the accuracy of identifying abnormalities in the coil. However, the combination does not explicitly disclose the first fault detection model comprising a support vector machine (SVM) model, wherein the determining whether the coil has a failure based on the distribution of the set of noise signals and the first fault detection model includes: determining one or more decision boundaries based on the SVM model; determining a position of the probability distribution line relative to each of the one or more decision boundaries; and determining, whether the coil has a failure based on the position of the probability distribution line relative to each of the one or more decision boundaries. Nevertheless, Dagher discloses determining a position of the probability distribution line relative to each of the one or more decision boundaries (as discussed above); and determining, whether the coil has a failure based on the position of the probability distribution line relative to each of the one or more decision boundaries (as discussed above). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen , Huang, and Ookawa with the teachings of Dagher to process and interpret magnetic resonance data and improve filtering noise and accuracy of signal estimation. However, the combination does not explicitly disclose the first fault detection model comprising a support vector machine (SVM) model, determining one or more decision boundaries based on the SVM model. Nevertheless, Shi discloses a support vector machine (SVM) model (In some embodiments, the first cost function may include a mean squared loss function, a Sigmoid activation function, a softmax loss function, a cross entropy loss function, a support vector machine (SVM) hinge loss function, a Smooth L1 loss function, or the like, or any combination thereof [0125]), determining one or more decision boundaries based on the SVM model (In some embodiments, the first cost function may include a mean squared loss function, a Sigmoid activation function, a softmax loss function, a cross entropy loss function, a support vector machine (SVM) hinge loss function, a Smooth L1 loss function, or the like, or any combination thereof [0125]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen , Huang, Ookawa , and Dagher with the teachings of Shi to identify fault detection to separate and classify regions of system performance and improve accuracy of fault detection . Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Van Wieringen and Huang, and further in view of Knierim et al. (US20210229946) hereinafter referred to as ‘ Knierim ’. Regarding Claim 9 , Van Wieringen and Huang disclose the claimed invention discussed in Claim 5 . However, Van Wieringen does not explicitly disclose the obtaining a set of reference noise signals includes: obtaining a plurality of sets of preliminary noise signals, each set of preliminary noise signals being collected by a coil in an acquisition that is performed on a reference subject without applying an excitation pulse to the reference subject; determining fitting parameters of a Weibull distribution model based on the plurality of sets of preliminary noise signals; and determining the set of reference noise signals based on the fitting parameters. Nevertheless, Huang discloses signals being collected by a coil in an acquisition that is performed on a reference subject without applying an excitation pulse to the reference subject (as discussed above). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Van Wieringen with the teachings of Huang to acquire/store initial sensitivity maps and improve accuracy of the magnetic resonance scan data. However, the combination does not explicitly disclose the obtaining a set of reference noise signals includes: obtaining a plurality of sets of preliminary noise signals, each set of preliminary noise signals being collected by a coil in an acquisition that is performed on a reference subject without applying an excitation pulse to the reference subject; determining fitting parameters of a Weibull distribution model based on the plurality of sets of preliminary noise signals; and determining the set of reference noise signals based on the fitting parameters. Nevertheless, Ookawa discloses the obtaining a set of reference noise signals includes (as discussed above) ; the plurality of sets of noise signals (as discussed above) ; determining the set of reference noise signals based on the fitting parameters (as discussed above) . However, the combi