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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to an abstract idea without significantly more.
With Respect to claim 1, the claim recites the following limitation(s):
Claim 1: “
an image reconstruction unit which creates an electrical property distribution inside the subject's body based on the first measurement data or the second measurement data and predetermined parameters;
and a region of interest identification unit that performs an identification process on the electrical property distribution to identify a region of interest and creates a post- identification electrical property distribution.”
Step 1- Claim 1 is directed to a visualization device for generating in-vivo impedance images of a subject.
Step 2a Prong 1 – The claimed invention is directed to non-statutory subject matter. The above limitations, under their broadest reasonable interpretation, fall within the “Certain
Mathematical concepts and mental processes grouping of abstract ideas, enumerated in MPEP
2106.04(a)(2)(II), in that they recite a series of mathematical calculations and mental steps which
produce a visualization of a region of interest in a user. When given their BRI, the limitations are considered an abstract idea of being certain mathematical concepts and mental processes.
With respect to claim 1, “an image reconstruction unit which creates an electrical property distribution inside the subject's body based on the first measurement data or the second measurement data and predetermined parameters;
and a region of interest identification unit that performs an identification process on the electrical property distribution to identify a region of interest and creates a post- identification electrical property distribution.” is considered to fall within the Mathematical concepts and mental processes grouping of abstract ideas. The image reconstruction unit and the region of interest unit are realized by program instructions operating on collected data and performing mathematical operations to obtain a result.
Step 2a Prong 2 - The recitation of the additional elements of a “current/voltage injection measurement unit which has a sensor provided with a plurality of electrodes arrangeable on a subject's skin at intervals from each other”, merely invokes such additional element(s) as tools to perform the abstract idea. MPEP 2106.05(f).
Further, the recitation of these additional element(s) in the claim generally links the use of the abstract idea to a particular technological environment or field of use, i.e., a computerized environment. MPEP 2106.05(h).
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). MPEP 2106.04(d).
“As set forth in MPEP 2106.05(g) Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.” Note: Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis).
With respect to claim 1, “current/voltage injection measurement unit which has a sensor provided with a plurality of electrodes arrangeable on a subject's skin at intervals from each other” merely recite elements that represent insignificant extra-solution data gathering.
Applicant’s specification only sets forth the additional elements in a high level and in a general sense and the thrust of the invention is directed to how the signals are processed once they are collected.
As such, these additional elements do not integrate the abstract idea into a practical application and therefore the claim is directed to the judicial exception.
Step 2B - The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
Even when considered as an ordered combination, the additional elements of claim 1 does not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claim 1 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself (Step 2B: NO). MPEP 2106.05.
Accordingly, under the Subject Matter Eligibility test, claim 1 is ineligible.
Furthermore, the dependent claims, 2-8 do not add significantly more to the
abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Garber(US2013/0002264) hereinafter Garber.
Garber teaches an electrical impedance tomography (EIT) device having a plurality of electrodes (4) that can be arranged on a body in order to reconstruct the impedance distribution of the body with a reconstruction algorithm. The control unit (10) of the EIT device is set up by suitable programming to continuously determine at least one property (e1, . . . , eM) each from the measured signals (U1, . . . , UM) of all measuring channels and to correct measured signals of the measuring channels on the basis of the properties or to adapt the reconstruction algorithm on the basis of the properties.
Regarding claim 1, Garber teaches a current/voltage injection measurement unit which has a sensor provided with a
plurality of electrodes arrangeable on a subject's skin at intervals from each other and which injects a current or applies a potential difference between each of the electrodes in a state where each of the electrodes is in contact with the skin and measures first measurement data, which is a potential difference and phase, based on a current injection/voltage measurement pattern when injecting the current. or measures second measurement data. which is a current and phase, based on a voltage injection/current measurement pattern when applying the potential difference between each of the electrodes;
an image reconstruction unit which creates an electrical property distribution inside the subject's body based on the first measurement data or the second measurement data and predetermined parameters;
and a region of interest identification unit that performs an identification process on the electrical property distribution to identify a region of interest and creates a post- identification electrical property distribution. Note figs. 1-3, paragraph [0011] According to the invention, an electrical impedance tomography device is provided with: a plurality of electrodes, which can be placed on a body, with control and measuring circuits to feed alternating current or alternating voltage to the electrodes and to receive voltage or current signals received from the electrodes as measured signals, and with a control unit. The control unit is connected to the control and measuring circuits and is set up by suitable programming to supply an electrode pair each with an alternating current or with an alternating voltage, to receive the voltage signal or current signal of each electrode pair from all other electrode pairs as a measured signal of a measuring channel, and to change the feeding electrode pair--have the feeding electrode pair successively run through the plurality of electrodes in order to thus receive and to process measured signals (U.sub.1, . . . , K.sub.M) in order to reconstruct therefrom the impedance distribution of the body with a reconstruction algorithm. The control unit is set up, furthermore, by suitable programming to continuously determine at least one property (e.sub.1, . . . , e.sub.M) each from the measured signals (U.sub.1, . . . , U.sub.M) of all measuring channels and to correct measured signals of the measuring channels on the basis of the properties or to adapt the reconstruction algorithm on the basis of the properties. [0013] The present invention pertains to an EIT device, in which a process for continuous adaptation of the reconstruction rule and/or of the measured signals to measuring interferences of both a statistical and especially also systematic nature is implemented. The measured data of all channels are analyzed continuously for possible measuring errors and, if necessary, the reconstruction rule is adapted to the particular error situation, whereby EIT images that are free from interferences or artifacts to the greatest extent possible are guaranteed over the entire measuring time. Also note paragraphs [ 0025] teaches examples of measurable and derivable properties of measured voltages including phase, [0030] – [0031] set forth summing up the EIT data over regions of interest and over time.
Regarding claim 2, Garber teaches wherein the identification process is performed based on a predetermined threshold. Garber not only reconstructs EIT data to generate image data but also monitors the electrode channels continuously to ensure and identify quality versus poor quality EIT data to ensure the EIT image data generated is not corrupted by noise. The region of interest, identification process and the threshold as claimed are not specific to the type or nature of the EIT data and could be part of the determination of poor quality data. Note figs. 1-3, Fig. 3 specifically sets forth visual data based on whether a threshold value is exceeded, paragraph [0011], [0025]- [0028], [0030],[0031].
Regarding claims 3 and 4, Garber teaches wherein the region of interest identification unit creates a region of interest index distribution by performing a standard deviation process on the electrical property distribution and creates the post-identification electrical property distribution by performing the identification process on the region of interest index distribution. . Garber not only reconstructs EIT data to generate image data but also monitors the electrode channels continuously to ensure and identify quality versus poor quality EIT data to ensure the EIT image data generated is not corrupted by noise. The region of interest, identification process and the threshold as claimed are not specific to the type or nature of the EIT data and could be part of the determination of poor quality data. Note figs. 1-3, Fig. 3 specifically sets forth visual data based on whether a threshold value is exceeded, paragraph [0011], [0025]- [0028], [0030],[0031].
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 5-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garber(US2013/0002264) hereinafter Garber in view of Liu et al.(CN111192337) hereinafter Liu et al.
Regarding claims 5-8, Garber teaches the claimed invention as set forth above including an EIT device has a plurality of electrodes (4) that can be arranged on a body in order to reconstruct the impedance distribution of the body with a reconstruction algorithm. Note paragraph [0049] discusses [0049] The channel-specific quality indices q.sub.m were used to define a systematic error weighting matrix: W=diag(q.sub.1, q.sub.2, . . . , q.sub.208). A sensitivity matrix-based Newton-Raphson method was used to reconstruct the vector .DELTA.p.sup.n of the relative impedance change from the relative voltage change .DELTA.u.sup.n. Sensitivity matrix S was determined from a finite element model of the thorax using the linearized Geselowits relation. The adaptation of the reconstruction matrix A to the concrete systematic errors found was performed by integrating the systematic error weighting matrix W in the determination of the reconstruction matrix: A=R(S.sup.TWS+.lamda.L.sup.TL).sup.-1S.sup.TW, .DELTA.p.sup.n(t)=A.DELTA.u.sup.n(t). Matrix L designates the regularization matrix, the scalar variable .lamda. designates the regularization parameter, and matrix R represents a filtered registration matrix from the FEM system to the pixel system of the EIT image.
Garber does not specifically set forth a learning unit wherein the image reconstruction unit creates a multiple measurement matrix from at least one of impedance, resistance, reactance, capacitance. admittance, conductance, susceptance, and phase obtained from the first measurement data or the second measurement data, and creates the electrical property distribution from the multiple measurement matrix and the predetermined parameters, and the predetermined parameters include a prior variance vector y relating to sparsity, a positive definite matrix B relating to temporal correlation, and a variance value k of a multidimensional normal distribution relating to noise.
Liu et al. teaches a sequential multi-frame electrical impedance tomography image reconstruction algorithm based on the sparse Bayesian learning, belonging to the technical field of medical and industrial detection. Liu et al. also teaches using sequential multi-frame impedance tomography algorithm for image reconstruction (1) set a priori knowledge, improving model
after obtaining the measurement matrix Y composed of voltage measurement vector, the following problem is solved matrix K by measuring matrix reconstruction. Note that formula (2) meets the standard multiple measurement vector (Multiple Measurement Vector, MMV) model. sparse supports all frame we assume that K has the same or similar, that is, inter-frame time continuity exists, and also assuming non-zero entries in each frame have intra-frame spatial correlation. Before each iteration starts, firstly using based on approximate message passing (Approximate Message Passing, AMP) MMV model of the expected number (E-step) approximation algorithm and reduce the computational complexity. The basic idea is using the AMP method, it can use matrix corresponding to multiplication operation between elements and the vector calculation to replace the high dimensional matrix operation so as to greatly reduce the complexity of calculation and improve the algorithm efficiency.
Then, with the evidence maximization method (the Evidence Maximization Methodology) obtained for estimation of Θ. firstly using optimization method for minimizing (Majorization-minimization Method), value obtained by learning γg, wherein parameter β to reflect the gth sample clusters (clusters) of variance γg and the relationship of its adjacent cluster g + and g -. On this basis, updating the value of A to obtain Adiff. A temporary whitening processing (temporary whitening), and obtaining the mutual coupled relation A and Bg-Bg by learning in this environment. Note FIG. 1. and claim 1.
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to substitute the reconstruction algorithm used in Garber for the sparse Bayesian learning algorithm as taught by Liu et al. to reduce the calculation complexity, improve robustness, and further improve the accuracy of the image reconstruction to obtain high image resolution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhu(US 20040242989) teaches a system for performing electrical impedance tomography. The system includes a first set of electrodes positioned in a first plane and a second set of electrodes positioned in a second plane. The system also includes a third set of electrodes positioned in a third plane between the first and second planes.
HOLDER(WO 2022258963) teaches methods and apparatus for carrying out impedance tomography on human or animal subject, comprising applying probe electrical signals to the subject using a plurality of electrodes, such that imposed electrical currents flow within the subject, Various non-linear reconstruction methods may be used in determining the maps or images of tissue in these circumstances. For example Bayesian techniques, or machine learning such as deep learning techniques may be used. Maps or images of absolute impedance may be constructed using probe electrical signals of either a single frequency or of multiple different frequencies, or maps or images of impedance spectra with respect to frequency may be constructed using probe electrical signals at multiple different frequencies.
LEE et al.( EP 3416078) teaches a biometric identification system comprising: a bio-impedance measurement unit adapted to be worn by a user, the bio-impedance measurement unit including: a plurality of measurement electrodes; a measurement circuit adapted to perform bio-impedance measurements on the user via a plurality of different groups of said plurality of measurement electrodes, each of said groups including at least two measurement electrodes, wherein the impedance measurement circuit, for each group of measurement electrodes, is adapted to measure an impedance in each frequency channel of a set of frequency channels, the system further comprising: a storage unit adapted to store each measured impedance in a measurement data set, and an identification unit adapted to perform a comparison based on the measurement data set and a reference data set.
Cherepenin et al.( 6236886) teaches obtaining tomographic images of the human body and the electrical impedance tomograph, in which a source of electric current is used to send electric current at levels undetectable by a human being to pairs of electrodes, between which at least two electrodes are placed. An algorithm of image reconstruction makes it possible to obtain the distribution of absolute conductivity of a body, characterizing the state of soft and bone tissues and blood vessels.
WEXLER et al.(2697907) teaches A method for producing a computationally efficient system that reduces the number of iterations required to generate a conductivity image pattern of a subsurface object, and its attendant conductivity distribution, through a solution to the system of field equations that simultaneously satisfies all of the boundary conditions and conserves internal current flux densities.
Mu et al.( US 20190033974) teaches a wearable device including sensors arranged at different locations on the wearable device. Each sensor measures electrical signals transmitted from a wrist or arm of a user. A position computation circuit is coupled to the sensors. The position computation circuit computes, using information derived from the electrical signals with a machine learning model, an output that describes a hand position of a hand of the wrist or arm of the user. the sensors 112 include electrodes that use electrical impedance tomography (EIT) to generate inputs used to determine the hand position of the user's hand 120. The impedances derived from the electrical signal 216 may be passed on to a computing device (e.g., a separate host system or a processor integrated with the wearable device 100) to perform image reconstruction and display based at least in part on the electrical signal 216. Several different approaches to training may be used, including decision tree learning, association rule learning, neural networks, deep learning, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, and representation learning, etc.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN L CASLER whose telephone number is (571)272-4956. The examiner can normally be reached M-Th 6:30 to 4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Marmor can be reached at (571)272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN L CASLER/Primary Examiner, Art Unit 3791