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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 .
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Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections- 35 USC §101
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 1-9 are rejected under 35 U.S.C.§101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, A magnetic particle imaging (MPI) reconstruction method based on a time-domain system matrix and x-space, comprising:
obtaining a voltage signal through MPI scanning based on a Cartesian trajectory;
obtaining an original image according to an x-space-based reconstruction method;
constructing, based on the voltage signal as well as a velocity compensation step and a grading step in the x-space-based reconstruction method, a forward model for time-domain-based MPI and x-space-based reconstruction; and
taking the original image as an input of an inverse problem solver of the forward model and
obtaining an optimized particle distribution diagram through solving by using an algebraic iteration method.
The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. 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 (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., a mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it 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 and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “A magnetic particle imaging (MPI) reconstruction method based on a time-domain system matrix and x-space”, obtaining an original image according to an x-space-based reconstruction method; constructing, (…) as a velocity compensation step and a grading step in the x-space-based reconstruction method, a forward model for time-domain-based MPI and x-space-based reconstruction. taking the original image as an input of an inverse problem solver of the forward model and obtaining an optimized particle distribution diagram through solving by using an algebraic iteration method. All these steps represent a mathematical manipulations of data/ image data using mathematical model concepts / algorithm. The reconstructions of MPI method based on the X-space method is a transformed domain (X-space) where the reconstruction is performed using a physical model of the imaging system. The reconstruction of magnetic particle imaging is based on a mathematical model for or time domain-based MPI and x-space-based reconstruction and a well known physics of the MPI system (e.g., gradient field) to invert the measurement process. obtaining an optimized particle distribution diagram through solving by algebraic iteration (see Specification [0012]- [0027], and [0047]-[0056]) . These steps represent a process (a mathematical manipulation) that, under its broadest reasonable interpretation, it encompasses a mathematical analysis and an abstract idea making manipulated by computing system.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 1 recites additional elements
“obtaining a voltage signal through MPI scanning based on a Cartesian trajectory”, and “based on the voltage signa”; are data gathering steps for the particular technological environment or field of use. Obtaining voltage signal data and constructing the MPI based on voltage signal data represent mere data gathering steps and only add an insignificant extra-solution activity to the judicial exception. Furthermore, obtaining voltage signals and mathematical analysis of data, generating an optimized diagram, indicates that anything other than a generic computer ( processors") needs to be used to carry out the abstract idea. The above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data and types of data. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
claims 2-9 are rejected under 35 U.S.C. 101 because claims depend on claim 1, therefore, has the abstract idea of claim 1 and also has the routine and conventional structure above of claim 1. In addition, claims 2-9 further recite the elements which are simply more standard computational, mathematical-calculation to data gathering /generate data and/ or a model, and. Furthermore, claims 2-9 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
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 1-2, and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Semih Kurt et al. (hereinafter, Kurt) “Partial FOV Center Imaging (PCI): A Robust X-Space Image Reconstruction for Magnetic Particle Imaging”, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 39, NO. 11, NOVEMBER 2020 and in view of Tian et al. (CN 113129403A, hereinafter Tian, an original copy combined with translation preview is uploaded by the examiner)
Regarding Claim 1, Kurt teaches,
A magnetic particle imaging (MPI) reconstruction method based on a time-domain system matrix and x-space (Kurt, see abstract, Magnetic Particle Imaging (MPI), and page 3442, left col. Middle paragraph,” a robust x-space image reconstruction technique. For a trajectory that contains a 1D drive field superimposed with a slowly varying focus field, the pFOV centers are closely spaced and the FFP speed is dominated by the drive field. For such a trajectory, the time-domain MPI signal, s (t),. See equation 1-2, “xs (t) is the instantaneous FFP position, ˙ xs (t) is the instantaneous FFP speed, ρ(x) is the particle distribution, h (x) is the PSF, and ρˆ(x) is the PSF-blurred “ideal” MPI image”) comprising:
obtaining a voltage signal through MPI scanning based on a Cartesian trajectory (Kurt, Page 3444, right col. Bottom paragraph, “Imaging experiments were performed on in-house FFP MPI scanner. Page 3445 “Fig. 3. “in-house FFPMPI scanner and the linear scan trajectory used in the imaging experiments. This scanner features (−4.8, 2.4, 2.4) T/m/μ0 selection field gradients in (x, y, z) directions”, FFP MPI Scanner’s receive coil detects signal as a time varying voltage signal. It is known in the art); obtaining an original image according to an x-space-based reconstruction (Kurt, Page 3442, , Left col. Middle paragraph,” method; In this work, we present a robust x-space image reconstruction technique called “pFOV center imaging”. “The proposed technique first forms a raw image of the entire FOV by mapping the MPI signal directly to the pFOV center locations.In this work, we present a robust x-space image reconstruction technique called “pFOV center imaging” (PCI), which features substantially simplified pFOV processing and increased robustness against harmonic interferences. The proposed technique first forms a raw image of the entire FOV by mapping the MPI signal directly to the pFOV center locations. Then, this raw image is deconvolved by a fully known, compact kernel to obtain the corresponding MPI image) constructing,
based on the voltage signal (Kurt, Fig. 3, “Fig. 3. “in-house FFPMPI scanner and the linear scan trajectory used in the imaging experiments”, FFP MPI Scanner’s receive coil detects signal as a time varying voltage signal. It is known in the art) as well as a velocity compensation step and a grading step in the x-space-based reconstruction method (Kurt, Abstract, in standard x-space approach to MPI, the image is reconstructed by gridding the speed-compensated nanoparticle signal to the instantaneous position of the field free point (FFP)”),
Kurt is silent on a forward model for time-domain-based MPI and x-space-based reconstruction; and taking the original image as an input of an inverse problem solver of the forward model and obtaining an optimized particle distribution diagram through solving by using an algebraic iteration method.
However, Tian teaches a forward model for time-domain-based MPI and x-space-based reconstruction (Tian, Page 4, middle paragraph, “dispersing the filtered and amplified induced voltage into a product sum form of a plurality of position system functions and corresponding magnetic particle concentrations to obtain a system matrix of a forward model of the magnetic particle imaging system”); and
taking the original image as an input of an inverse problem solver of the forward model and obtaining an optimized particle distribution diagram through solving by using an algebraic iteration method (Tian, Page 4, step S50, middle paragraph, “based on the system matrix of the forward model of the magnetic particle imaging system, solving the concentration distribution of the magnetic particles to be measured by using the output voltage obtained by measurement, and obtaining the concentration distribution of the magnetic particles as a reconstructed image”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Kurts’s method to incorporate a forward model using the system matrix and generate optimized particle distribution as taught by Tian and obtain an accurate reconstructions of particle distribution diagrams at a higher speed and reduce difficulty (Tian, page 6, middle paragraph). It would have been obvious to a person of ordinary skill to include the well-known Forward, in order to yield the predicted results of reconstruction of accurate Magnetic Particle distribution diagram in less time, yet with higher accuracy and speed (KSR).
Regarding Claim 2, combination of Kurt and Tian teaches the MPI reconstruction method based on a time-domain system matrix and x-space according to claim 1,
Kurt further teaches wherein the constructing, based on the voltage signal as well as a velocity compensation step and a grading step in the x-space-based reconstruction method (Kurt, Abstract,” In standard x-space approach to MPI, the image is reconstructed by gridding the speed-compensated nanoparticle signal to the instantaneous position of the field free point (FFP)”),
comprises: recovering, by using a fundamental frequency recovery algorithm (Kurt, Page 3445, right Col. “Standard x-space reconstruction with DC recovery algorithm [9] and SNR optimized pFOV stitching [24] was implemented for comparison purposes”), an excitation frequency component filtered out in a receiving process of the voltage signal constructing an MPI simulation model that retains a fundamental frequency signal (Page 3444, Right col. Top paragraph, white Gaussian noise was added to the time-domain MPI signal at 10 different noise levels, with signal-to-noise ratio (SNR) varying between 5-50 dB. SNR was defined using the peak signal amplitude as follows:s ee equation 20, σ denotes the standard deviation of noise, and s (t) is the MPI signal after direct feedthrough filtering. For harmonic interference analysis, harmonic interference was added to the spectrum of s (t). When the drive field is applied alone, the spectrum of the MPI signal contains only the harmonics of the fundamental frequency, f0”); and
performing time and space discretization on the MPI simulation model to obtain a discrete time-domain system matrix S (Page 3444. Right col. Bottom paragraph, we simulated noise and harmonic interference effects separately. Then, we incorporated both effects simultaneously with SNR ranging between 5-50 dB and SIR ranging between 4-24 dB. Monte Carlo simulations were performed via repeating each case 50 times. Next, to incorporate the effects of relaxation, we utilized a realistic time constant of τ = 3 μs [28], [29], using the model provided in [30]. For this analysis, SNR was fixed at 30 dB and SIR at 8 dB”).
Kurt is silent on a forward model for time-domain-based MPI and x-space reconstruction.
However, Tian teaches a forward model for time-domain-based MPI and x-space reconstruction (Tian, Page 4, step S50, middle paragraph, “based on the system matrix of the forward model of the magnetic particle imaging system, solving the concentration distribution of the magnetic particles to be measured by using the output voltage obtained by measurement, and obtaining the concentration distribution of the magnetic particles as a reconstructed image”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Kurts’s method to incorporate a forward model using the system matrix and generate optimized particle distribution as taught by Tian and obtain an accurate reconstructions of particle distribution diagrams at a higher speed and reduce difficulty (Tian, page 6, middle paragraph). It would have been obvious to a person of ordinary skill to include the well-known Forward, in order to yield the predicted results of reconstruction of accurate Magnetic Particle distribution diagram in less time, yet with higher accuracy and speed (KSR).
Regarding Claim 7, combination of Kurt and Tian teaches the MPI reconstruction method based on a time-domain system matrix and x-space according to claim 2,
Kurt further teaches wherein the recovering, by using a fundamental frequency recovery algorithm, (Kurt, Page 3445, right Col. “Standard x-space reconstruction with DC recovery algorithm [9] and SNR optimized pFOV stitching [24] was implemented for comparison purposes”) an excitation frequency component filtered out in a receiving process of the voltage signal (Page 3444, Right col. Top paragraph, white Gaussian noise was added to the time-domain MPI signal at 10 different noise levels, with signal-to-noise ratio (SNR) varying between 5-50 dB. SNR was defined using the peak signal amplitude as follows:s ee equation 20, σ denotes the standard deviation of noise, and s (t) is the MPI signal after direct feedthrough filtering. For harmonic interference analysis, harmonic interference was added to the spectrum of s (t). When the drive field is applied alone, the spectrum of the MPI signal contains only the harmonics of the fundamental frequency, f0”);; comprises:
collecting the voltage signal through partial field-of-view (pFOV) scanning to obtain a pFOV image; and recovering DC image intensity of the pFOV image. (Kurt, Page 3442, left Col. Middle paragraph, “ a robust x-space image reconstruction technique called “pFOV center imaging” (PCI),which features substantially simplified pFOV processing and increased robustness against harmonic interferences. The proposed technique first forms a raw image of the entire FOV by mapping the MPI signal directly to the pFOV center locations. Then, this raw image is deconvolved by a fully known, compact kernel to obtain the corresponding MPI image”. Bottom paragraph, “It has been shown that the contribution of the lost first harmonic for each pFOV is a DC term [9], [25]. In standard x-space reconstruction, DC terms are recovered via pFOV stitching by enforcing non-negativity and continuity constraints on the reconstructed image”).
Regarding Claim 8, combination of Kurt and Tian teaches The MPI reconstruction method based on a time-domain system matrix and x-space according to claim 2,
Kurt further teaches wherein the performing time and space discretization on the MPI simulation model to obtain a discrete time-domain system matrix S (Kurt, Page 3442, the time-domain MPI signal, s (t), can be written as equation 1-2, s (t) = αx˙s (t) ρˆ xs (t). Here, xs (t) is the instantaneous FFP position, ˙ xs (t) is the instantaneous FFP speed, ρ(x) is the particle distribution, h (x) is the PSF, and ρˆ(x) is the PSF-blurred “ideal” MPI image”) comprises:
taking sampling time as a time interval of the time discretization (Kurt, Page 3442, right col.Equaition 3, “ Let x0 j be the center position of the j th pFOV and t 0 j be the time instant when the FFP is at x 0 j , i.e.,xs (t) t=t 0 j = x0 j , for j = 1, . . . , N (3) where N is the total number of pFOVs.”NOTE: discrete time intervals t0j for j=1-N)
taking a pixel size of an MPI device as a voxel size of the space discretization (Kurt, Page 3449, bottom paragraph The compact kernels used in PCI and Lumped-PCI both have fully-known shapes that solely depend on the pFOV size. The scaling factor β0, however, depends on the nanoparticle type. Using a constant β0 (as done in this work) results in a global scaling of the reconstructed image for cases with a single type of nanoparticle, and nanoparticle-dependent scaling of pixel intensities for cases with more than one type of nanoparticle)
Kurt is silent on obtaining a magnetization formula for a particle with a unit concentration at each location; and
obtaining the time-domain system matrix S based on a vacuum permeability, the magnetization formula for the particle, and a difference between sampling time points.
However, Tian teaches obtaining a magnetization formula for a particle with a unit concentration at each location middle paragraph, step S30, based on the magnetic moment vectors of the magnetic particles, obtaining the induced voltage generated by the magnetic particles in the detection coil under the magnetic particle concentration c through Faraday' S law of electromagnetic induction, and filtering and amplifying the induced voltage”. Page 5, middle paragraph, the magnetization of the SPIOs generates a saturation effect, so that the magnetic moment vector responses of a plurality of SPIOs under the external excitation magnetic field are described by adopting a langevin function, and therefore, the response function of a plurality of magnetic particles under the external excitation magnetic field is expressed by a formula shown in formula (2):
PNG
media_image1.png
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591
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wherein, mu0Represents the magnetic permeability, k, of vacuum BIs the Boltzmann constant, T is the temperature, H is the applied excitation magnet. The field strength of the field, c (r) is the concentration of magnetic particles at location r, and M (H, t, r) represents the average moment vector of the response of the plurality of magnetic particles at location r at time t under the field strength H of the applied excitation field.” See original copy for equation 2)
and obtaining the time-domain system matrix S based on a vacuum permeability, the magnetization formula for the particle, and a difference between sampling time points (Tian, Page 11, middle paragraph, and step S42, combining the system functions of all the positions of the discrete induced voltage to obtain a system matrix of the forward model of the magnetic particle imaging system. The system matrix is represented by the formula (8): S(f,r)＝G(f)·μ0p(r)·2πfM(f,r) (8). wherein S (f, r) is the system matrix of the forward model of the magnetic particle imaging system, G (f) is the transfer function of the filter amplifying circuit, mu0Representing the vacuum permeability, p (r) representing the sensitivity of the detection coil, and M (f, r) representing the average magnetic moment vector at frequency f of the plurality of magnetic particles at position r.”).
Regarding Claim 9, combination of Kurt and Tian teaches the MPI reconstruction method based on a time-domain system matrix and x-space according to claim 1,
Kurt further teaches wherein the obtaining a voltage signal through MPI scanning based on a Cartesian trajectory (Kurt, Page 3445 “Fig. 3. “in-house FFPMPI scanner and the linear scan trajectory used in the imaging experiments. This scanner features (−4.8, 2.4, 2.4) T/m/μ0 selection field gradients in (x, y, z) directions”, comprises:
Kurt is silent on obtaining the voltage signal based on a vacuum permeability,
an included angle between a direction of a magnetic field and a direction of a receiving coil, a magnetization response of a magnetic nanoparticle with a unit concentration, sensitivity of the receiving coil, a particle concentration, a temperature, and a magnetic moment of a single magnetic nanoparticle
However, Tian teaches obtaining the voltage signal based on a vacuum permeability (Tian, Page 11, middle paragraph equation 8, “µo representing the vacuum permeability”),
an included angle between a direction of a magnetic field and a direction of a receiving coil (Tian, and M (f, r) representing the average magnetic moment vector at frequency f of the plurality of magnetic particles at position r”., a magnetization response of a magnetic nanoparticle with a unit concentration, sensitivity of the receiving coil (Tian, equation 8, page 11, p (r) representing the sensitivity of the detection coil) a particle concentration, a temperature, and a magnetic moment of a single magnetic nanoparticle (Tian, Equation 2, is the temperature, H is the applied excitation magnet. The field strength of the field, c (r) is the concentration of magnetic particles at location r, and M (H, t, r) represents the average moment vector of the response of the plurality of magnetic particles at location r at time t under the field strength H of the applied excitation field.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Kurt’s method to incorporate a forward model using the system matrix and generate optimized particle distribution as taught by Tian and obtain an accurate reconstructions of particle distribution diagrams at a higher speed and reduce difficulty (Tian, page 6, middle paragraph). It would have been obvious to a person of ordinary skill to include the well-known Forward, in order to yield the predicted results of reconstruction of accurate Magnetic Particle distribution diagram in less time, yet with higher accuracy and speed (KSR).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Du et al. (CN 114998471 B) recites “The invention belongs to the field of image reconstruction of magnetic particle imaging, and particularly relates to a magnetic particle imaging reconstruction method, system and equipment based on a RecNet model, aiming at solving the problems that in the existing magnetic particle imaging reconstruction method, the difficulty of acquiring a system matrix is high, the reconstruction result contains noise and artifacts, and the reconstruction method of x-space has poor image quality and definition. The method comprises the following steps: acquiring a one-dimensional MPI signal to be imaged and reconstructed as an input signal; inputting the input signal and the corresponding non-magnetic field point speed signal into a trained magnetic particle reconstruction model RecNet for image reconstruction to obtain a two-dimensional MPI image; the magnetic particle reconstruction model RecNet is constructed based on a domain switching network and an improved UNet network. The invention can obtain high-quality and clear magnetic particle distribution images under the condition of not acquiring a system matrix” (Abstract).
Chen et al. (CN 115541693 A) The invention provides “The invention discloses a forward model constrained neural network magnetic particle imaging reconstruction method, which comprises the steps of obtaining a system matrix through calibration, and measuring voltage data generated by a sample; carrying out Fourier transform on the acquired data to convert the acquired data into a frequency domain, and screening the frequency characteristics of the data by using a signal-to-noise threshold; a Pythrch reconstruction network is used for mapping one-dimensional voltage data to multi-dimensional magnetic particle concentration distribution; using the system matrix as a forward model of magnetic particle imaging, generating voltage simulation data by using the reconstructed magnetic particle concentration distribution, and calculating the difference between the voltage simulation data and input voltage data to serve as a loss function to update network parameters; and adding a regularization term in the loss function, and adjusting the training parameters and the regularization parameters to obtain the optimal reconstruction effect. The invention uses the neural network constrained by the forward model to carry out magnetic particle imaging reconstruction, and further improves the reconstruction effect by adding a total variation regularization term in the loss function of the network” (Abstract).
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/DILARA SULTANA/ Examiner, Art Unit 2858
05/30/2026
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 6/1/2026
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