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 .
Response to Arguments
Applicant’s arguments, see Remarks, filed 4/28/2026, with respect to the rejection of claims 1-8 under 35 USC 101 have been fully considered and are persuasive. The rejection of claims 1-8 under 35 USC 101 has been withdrawn.
Applicant's arguments filed 4/28/2026, with respect to the rejection of claims 1-4 under 35 USC 102 have been fully considered but they are not persuasive.
Applicant's arguments filed 4/28/2026, with respect to the rejection of claims 5-8 under 35 USC 103 have been fully considered but they are not persuasive.
Regarding the rejection of claims 1-4 under 35 USC 102 in view of Garber(US2013/0002264) hereinafter Garber and Regarding the rejection of claims 5-8 under 35 USC 103 in view of Garber(US2013/0002264) hereinafter Garber in view of Liu et al.(CN111192337) hereinafter Liu et al.
Applicant amends claim 1 to include “ a region of interest identification unit that performs an identification process on the created electrical property distribution to identify a region of interest for visualization in the subject, the region of interest comprising biological tissue of the subject, and creates a post- identification electrical property distribution.” And applicant argues “According to aspects of at least claim I including the region of interest identification unit enables the system to identify local changes in time and space with high precision in a less invasive manner.2 Even further, utilizing the specified identification technique, as shown by the experimental results of Experiment Examples 1 and 3 in the Specification, artifacts occur when no identification processing is performed. However, these artifacts are eliminated by the claimed acquisition and processing techniques.”
The examiner respectfully disagrees, it is noted that the features upon which applicant relies as stated above are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). It appears applicant’s interpretation of the claims is narrower than the actual claim language allows.
Applicant further argues, “Garber generally describes an EIT device that can measure the signals in a body and provide for a general overall impedance distribution of the body with a reconstruction algorithm. However, Garber neither discloses nor suggests the region of interest identification unit or any identification of such region of interest in a subject where the region of interest is biological tissue. Garber does not disclose the performance of an identification process to identify regions of interest for visualization in biological tissue based on an electrical property distribution and creating or generating an electrical property distribution following such identification.”
The examiner respectfully disagrees, the region of interest, identification process 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. Garber teaches in paragraph [0031] Episodes of poor quality (which cannot be clinically interpreted) and episodes of good quality (which can be clinically interpreted) are marked differently (34) in a trend graph of EIT variables over longer time periods (33) or curves over shorter time periods (32), for example, the summed-up impedance changes (global curve) or impedance changes summed up over ROIs (regions of interest) or time series derived from EIT data and/or from voltage data and/or from operating data. Creating EIT images of specific areas of the body can be considered “regions of interest” and identifying quality or poor quality areas within those specific areas of the body can also be considered “regions of interest”. Although the intended use of the device as amended “for visualization of biological tissue” is not structurally limiting to the device, Paragraph [0051] in Garber is specific to images of lung tissue, [0051] In FIG. 6, we find the EIT image, which is based on the same interfered measured data as the image in FIG. 4, with applied adaptation of the reconstruction rule according to the above formula, including the quality indices in FIG. 5, which were determined from the quality analysis, in the systematic error weighting matrix W. Complete suppression of the values of the three q.sub.m=0 channels cause the artifact in the EIT image in FIG. 6 to disappear. A normally ventilated left lung is found here (thoracic CT cross section convention: on the right in the figure), but also a hypoventilated right lung in the dorsal area, in agreement with clinical X-ray findings. By applying process step 4), which will be explained in more detail below for this exemplary embodiment, a global quality index of Q=0.5 was assigned, which corresponds to medium data quality, because even when the EIT data were interpreted with the use of the adaptative reconstruction method, the user should be informed that measuring errors occurred. It appears applicant’s interpretation of the claim language is narrower than the actual claim language allows.
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. Furthermore, the control unit continuously updates the determination of the properties e.sub.m.sup..alpha. and/or the property quality parameters derived therefrom and/or the combined channel-specific quality parameters, wherein the determination is performed over a time window.
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.
Gärber(US 10285619) teaches electrical impedance tomography device includes a plurality of electrodes, a display apparatus, and a control and evaluation unit for reconstructing a matrix of image elements, a spatial distribution of impedance changes in an electrode plane, from measurement signals with a reconstruction algorithm. A time series of matrices are provided from an inhalation and an exhalation that follow. The control unit detects a local impedance change, as a function of a quantity monotonically dependent on average alveolar pressure, as inhalation and exhalation curves from the time series of matrices, to calculate a parameter representing the deviation between the inhalation and exhalation curves as a dissipation value of the image element and to associate the parameter with the image element, to combine the dissipation values of all examined image elements in a dissipation map, and to display the dissipation values with graphical codings dependent on dissipation values of the dissipation map. (6) Thoracic electrical impedance tomography for measuring the regional lung ventilation has been increasingly used in intensive care medicine focused on research. Theoretical models and experimental comparisons of EIT with CT images of the thorax show a nearly complete proportionality of the air content in the lung tissue and the impedance of the latter. The breaths are resolved spatially with about 20% of the thoracic diameter and in time typically with about 20 to about 40 matrices per second, which makes possible a bed-side monitoring of the regional lung ventilation. The matrices are occasionally also called images of the impedance distribution (with 32×32=1024 image elements) or frames.
Emery(US 10154819) teaches systems and methods are disclosed for classifying a condition of an entity that includes a conductive medium having multiple conductive paths, using a pattern recognition strategy to classify a signature constructed from impedance-interrogation measurements and optionally including inputs from other informative sources which may be external to the system. (109) In embodiments, classifying a condition of an entity may include determining or estimating a classification from a signature by machine execution of a pattern recognition strategy. In embodiments, a pattern recognition strategy may include any of the many strategies for classifying states or conditions represented by signatures known to persons of skill in the art of pattern recognition, including, for example, neural networks, genetic algorithms, evolutionary algorithms, deep learning algorithms, supervised machine learning, unsupervised machine learning, dimension reduction strategies, support vector machines, linear classifiers, binary tree classifiers, Gaussian process classifiers, k-nearest neighbor classifiers, Bayesian network classifiers, and comparison of one or more signatures against a reference or standard. It will be apparent that, although very simple pattern recognition can in some situations be carried out by hand calculations, and although visual or other comparisons by a human agent may in some settings be thought of as a kind of pattern recognition, effective pattern recognition of the kind contemplated herein entails a complexity and quantity of data manageable, as a practical matter, only by machine-executed processes; therefore “pattern recognition” as used herein refers to the machine-executed methods and techniques encompassed within the meaning of the term as ordinarily understood by persons of skill in the art.
Boverman et al.( US 10357177) teaches electrical impedance imaging of a subject of interest using a plurality of electrodes is provided. The method includes applying one or more determined current patterns to one or more electrodes of the plurality of electrodes. Further, the method includes determining a resultant voltage at least one electrode of the one or more electrodes in response to the one or more determined current patterns. Moreover, the method includes estimating a change in a contact impedance for the at least one electrode of the one or more electrodes. Additionally, the method includes calculating a compensated voltage for the at least one electrode based on an estimated change in a corresponding contact impedance of the at least one electrode.
Holzhacker et al.( US 11412946) teaches Electrical impedance tomography devices and systems having a multi-dimensional electrode arrangement are disclosed including a related method for operating the devices and systems. The reconstructed images may correspond to the planes of the multi-dimensional electrode arrangements as well as one or more images corresponding to a region outside of the electrode planes. Such reconstruction may be performed by application of a finite element mesh having multiple layers defined for the different regions for generating the images. (3) FIG. 1 is a schematic diagram of a portion of an EIT system 100 showing a plurality of electrodes 110 positioned around a region of interest (e.g., thorax) of a patient 105. The electrodes 110 of conventional EIT systems 100 are typically physically held in place by an electrode belt to ensure consistent spacing as well as a linear (one dimensional) alignment of the electrodes 110. The placement of the electrodes 110 is typically in a single plane 102 transverse to the cranial caudal axis 104 of the patient. Although the electrodes 110 are shown in FIG. 1 as being placed only partially around the patient 105, electrodes 110 may by placed around the entire patient 105 depending on the specific region of interest available or desired for measurement. The electrodes 110 may be coupled to a computing system (not shown) configured to control the operation of the electrodes 110 and perform reconstruction of the EIT image.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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
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