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 .
Drawings
The drawings are objected to because Figures 7, 8, and 9 do not show arrows providing the direction of flow from box to box (step to step).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: “means of an ultrasound device” and “means of a regressor” in claim 1, and “computing means” in claim 14.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim Objections
Claims are objected to because of the following informalities:
In claim 1, lines 7-8, “in which it is visible at least the pleural line and a portion of lung below it” should be similar to “in which at least a pleural line and a portion of lung below the pleural line is visible in the ultrasound image” for clarity.
For claim 1, line 13, remove the extra spacing between “consolidations” and “(c4 )”.
For claim 1, line 13, “consolidations (c4 ) ;” should be “consolidations (C4);” for clarity.
For claim 1, line 14, “characterized in that it comprises further the steps of:” should be similar to “segmenting further comprises:” for clarity.
For claim 8, line 4, remove the extra spacing between “individuated” and “ROIs”.
For claim 10, line 11, remove the extra spacing between “same” and “ROI”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors. Therefore, claims 1-14 are indefinite.
For claim 1, the limitation “individuating” is indefinite. It is unclear what is the difference between “individuating” and “segmenting”. For the purpose of advancing prosecution, the examiner assumes “individuating” and “segmenting” is the same.
Claim 1 recites the limitation "the type". There is insufficient antecedent basis for this limitation in the claim. For the purpose of advancing prosecution, the examiner assumes “the type” should be “type” for clarity.
For claim 2, the limitation “Pneumonia score = f(Coria,..., Corie,..., Cor4a, Cor4e,)” is indefinite. It is unclear what are the parameters of (Coria,..., Corie,..., Cor4a, Cor4e,). For the purpose of advancing prosecution, the examiner assumes parameters (Coria,..., Corie,..., Cor4a, Cor4e,) are related to the region of interest.
For claim 13, the limitation “healthy, initial, intermediate, advanced or peak spectrum” is indefinite. It is unclear what is meant by a healthy, initial, intermediate, advanced or peak spectrum, and what is the difference between a healthy, initial, intermediate, advanced or peak spectrum. For the purpose of advancing prosecution, the examiner assumes the different spectra is related to the health of the patient (healthy patient vs patient with pneumonia).
Claims 2-14 are dependent of claim 1, and therefore rejected under these 112(b) rejections as well.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over L. Carrer et al, “Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 67, no. 11, pp. 2207-2217, June 2020, in view of O. Zenteno et al, “Spectral-Based Pneumonia Detection Tool Using Ultrasound Data from Pediatric Populations.”, Chest, vol. 150, no. 3, pp. 640-651, Sept. 2016, hereinafter referred to as Carrer and Zenteno, respectively.
Regarding claim 1, Carrer teaches a method for calculating a diagnostic parameter indicating the stage of a pneumonia, comprising the steps of:
(100) acquiring, by means of an ultrasound device provided with a probe comprising an array of CMUT or piezoelectric transducers (known in the art) each configured to emit an ultrasonic impulse directed to the tissues object of classification and to receive the raw ultrasonic signal reflected by the tissues in response to said ultrasonic impulse (see pg. 2209, col. 2, para. 3 — “LUS [lung ultrasound] data acquired by a convex probe are mapped into a linear grid moving from a polar to a Cartesian coordinate system.”),
at least an ultrasound image of the lung of a patient, in which it is visible at least the pleural line and a portion of lung below it (see pg. 2208, col. 2, para. 5 — “To efficiently detect and characterize the pleural line in LUS data, an automatic detection method should possess the following requirements: 1) it should discriminate between the pleural line and other LUS data features such as the ribs...”);
(200) individuating, inside said at least one image acquired at point (100), the region under the pleural line (see pg. 2208, col. 2, para. 4 — “In this way, we obtain a set P = {p1,..., pv,..., pV } representing the geometric location of the pleural line at each image. The second part uses the pleural line pv to compute relevant features that describe both geometric and radiometric properties of the pleura and the area underneath it [region of interest ROI].”);
(300) segmenting said region under the pleural line in order to individuate a set of ultrasound makers (Ci,..., Cn) therein, relating to the pleural line (Ci), A-lines (C2), B- lines (C3), consolidations (c4 ) (see pg. 2211, col. 1, para. 1- “Let us define a metric M1 representing the intensity of the pleural line for each column normalized by the pleural line average intensity...”; see pg. 2212, col. 1, para. 1 - “The feature f2 quantifies phenomena such as white lungs and consolidations.”; see pg. 2212, col. 1, para. 2 — “The area below the pleural line is of particular importance as it contains several indicators of the pathological conditions (e.g., A and B lines).”); and
characterized in that it comprises further the steps of: (310) individuating at least a region of interest (ROI) as a function of the type, quantity and configuration of the ultrasound markers individuated at step (300) and associating said at least one region of interest to a specific ROI type (Fig. 1, Covid-19 score classification as a function of features extracted from lung image, features including A-lines, B-lines and pleural lines).
Carrer teaches calculating a diagnostic parameter representing the progression stage of a pneumonia as a function of a plurality of parameters (aka pneumonia score), but does not explicitly teach extracting a frequency spectrum to calculate a pneumonia score.
Whereas, Zenteno, in an analogous field of endeavor, teaches
(450) extracting frequency spectra relating to the raw ultrasonic signal corresponding to segments of the ultrasound image contained in each ROI individuated at step (310), associating to each spectrum the information relating to the individuated ROI type (see Fig. 3 – “Spectral information correlated to the depth of the data [frequency spectra] in a bi-dimensional matrix a) pneumonic patient b) healthy patient. The values obtained the slope in MHz/cm c) pneumonic patient d) healthy patient.” With pneumonic and healthy patients as different types of ROI);
(470) comparing each one of said spectra extracted at point (450) with relative reference spectra, relating to ROls of the same type and calculated for healthy patients and for patients suffering from pneumonia at various advancement stages, in order to calculate a plurality of parameters characteristic of the correlation of said spectra extracted at point (450) with said reference spectra (see Fig. 3 – “Spectral information correlated to the depth of the data [frequency spectra] in a bi-dimensional matrix a) pneumonic patient b) healthy patient. The values obtained the slope in MHz/cm c) pneumonic patient d) healthy patient.” Where the relative reference spectra is the frequency spectrum of a healthy patient, as compared to a frequency spectrum of a patient with pneumonia at any stage);
(500) calculating a diagnostic parameter representing the progression stage of a pneumonia as a function of said plurality of parameters characteristic of the correlation calculated at point (470), said diagnostic parameter being calculated by means of a regressor associating to said plurality of parameters of correlation a value of the diagnostic parameter (Fig. 3c-3d; see pg. 4130, col. 2, para. 6 – “The spectral variation slope function [regressor] presents lower values for pneumonic areas while presenting higher values for healthy tissue.” where the slope (correlation) of the frequency spectrum (diagnostic parameter) represents the health of tissue (healthy vs pneumonia)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified calculating a diagnostic parameter representing the progression stage of a pneumonia as a function of a plurality of parameters (aka pneumonia score), as disclosed in Carrer, by extracting a frequency spectrum to calculate a pneumonia score, as disclosed in Zenteno. One of ordinary skill in the art would have been motivated to make this modification in order to further improve specificity and accuracy of differentiating between pneumonia and healthy tissue in lungs (see Table 1 and 2).
Furthermore, regarding claim 2, Carrer further teaches wherein said regressor is a function of regression associating to a set of numeric values characteristic of the correlation of the spectra associated to the regions of interest of each one individuated at step (310) with spectra relating to regions of interest of the same type and relating to patients whose disease advancement stage is known, a numeric value of the diagnostic parameter: Pneumonia score = f(Coria,..., Corie,..., Cor4a, Cor4e,) (see pg. 2210, col. 2, para. 4 — “Indeed, the score value is directly connected to the presence of structures linked to COVID-19 [pneumonia] and its severity.” where using a regression function is a known alternative for the skilled person).
Furthermore, regarding claim 3, Carrer further teaches wherein said diagnostic parameter is calculated by using a regression neural network to which the correlation parameter values (Coria,..., Corie,..., Cor4a, Cor4e) are provided in input, and which provides in output the diagnostic parameter value (see pg. 2208, col. 2, para. 4 – “A supervised SVM [support vector machine] classifier is used to assign a COVID-19 score to each image…” where SVMs are known regression neural networks).
Furthermore, regarding claim 4, Carrer further teaches wherein at step (310), in case at step (300) the pleural line is continuous and one or more A-lines are visible, the portion of image between pleura and first A-line is considered as ROI (Fig. 1, Covid-19 score classification as a function of features extracted from lung image, features including A-lines, B-lines and pleural lines; see pg. 2208, col. 1, para. 1 – “While score 0 represents a continuous and regular pleural line with the associated presence of A-lines…”).
Furthermore, regarding claim 5, Carrer further teaches wherein at step (310), in case at step (300) the pleural line is continuous and A- lines are not visible, the portion of image between pleura and the depth at which in the time domain the signal has an amplitude with respect to the peak amplitude produced by the pleura reflection at least equal to 5% is considered as ROI (see pg. 2214, col. 2, para. 3 – “When the values of M1 and M2 are stable, the score associated with the image is 0. On the contrary, drops and peaks in the features indicate images with a higher score.”).
Furthermore, regarding claim 6, Carrer further teaches wherein at step (310), in case at step (300) the pleural line is individuated, the same being discontinuous and neither A-lines nor B- lines being visible, a plurality of ROIs is considered, each one corresponding to a tract where pleura is continuous, and for each one of them the portion of image between pleura and the depth at which in the time domain the signal has an amplitude with respect to the peak amplitude produced by the pleura reflection at least equal to 5% is considered as ROI (Fig. 2; see pg. 2210, col. 1, para. 3 – “For each time step, the observation vector is equal to the LUS image pixel intensities.”; see pg. 2211, col. 2, para. 1 – “Large variations of M1(k) are potential indicators of pleural line discontinuities.”; see pg. 2212, col. 1, para. 3 – “The area below the pleural line is of particular importance as it contains several indicators of the pathological conditions (e.g., A and B lines).”).
Furthermore, regarding claim 7, Carrer further teaches wherein at step (310), in case at step (300) the pleural line is individuated, the same being discontinuous and at least a B-line being visible, a plurality of ROIs is considered: (i) possible continuous pleural line tracts are treated as in the previous claims depending on whether "A-lines" are visible or not; (ii) each area identified by an isolated B-line or by more coalescent B-lines is considered as another ROI (see pg. 2212, col. 1, para. 3 – “High values of f6, f7, and f8 are related to pleural ruptures and consolidations as they provide a quantification of the amount of scattered ultrasound energy that originated from below the pleura as per definition of Ib.”).
Furthermore, regarding claim 8, Carrer further teaches wherein at step (310), in case at step (300) at least a consolidation is individuated, in addition to the yet individuated ROIs further ROIs are considered, coincident with the area relating to each consolidation (see pg. 2212, col. 1, para. 1 – “The feature f2 quantifies phenomena such as white lungs and consolidations. Hence, steep peaks of this metric indicate consolidations in the lungs, while stable values are associated with lower score.”).
Furthermore, regarding claim 9, Zenteno further teaches wherein said plurality of reference spectra (or models) comprises, for each type of Region of Interest: - a model relating to a healthy patient; - a model relating to a patient with initial stage disease; - a model relating to a patient with intermediate stage disease; - a model relating to a patient with advanced stage disease; - a model relating to a patient with peak stage disease (see Fig. 3 – “Spectral information correlated to the depth of the data [frequency spectra] in a bi-dimensional matrix a) pneumonic patient b) healthy patient. The values obtained the slope in MHz/cm c) pneumonic patient d) healthy patient.”).
Furthermore, regarding claim 10, Zenteno further teaches wherein at step (450), a plurality of frequency spectra are extracted, relating to the raw ultrasonic signal corresponding to a plurality of respective segments of the ultrasound image contained in each ROI, in that, after step (450) and before step (470), it comprises the step of: (460) calculating the average of all the spectra extracted at point (450) and relating to each type of ROI, in order to obtain an average spectrum representing each ROI type, and in that at point (470) each average spectrum representing each ROI is compared with a reference spectrum relating to a healthy patient for a ROI of the same type and with a plurality of reference spectra relating to patients suffering from pneumonia at various advancement stages, as well for the same ROI type, in order to calculate a plurality of parameters characteristic of the correlation of said average spectrum with said reference spectra relating to ROIs of the same type, and in that the diagnostic parameter calculated at point (500) is calculated as a function of said plurality of parameters characteristic of the correlation of said average spectrum with said reference spectra relating to ROIs of the same type (Fig. 2; see pg. 4130, col. 1, para. 3-5 – “The region of interest (ROI) started at the pleural line fit and ended at a second parallel line which intercepts the maximum possible depth on the ultrasonic image to create a trapezoidal block per frame…The blocks had a width of 1.5 mm (i.e., 20 adjacent lines) and were axially divided into shorter Subblocks of 1 mm length with 50% overlap as shown in Fig 2. The power spectra of the sub-blocks were estimated and averaged using a Hanning window as the gating function.”).
Furthermore, regarding claim 11, Zenteno further teaches wherein at point (470) the comparison occurs by calculating the coefficient of correlation, on the whole frequency range, between each spectrum extracted at point (450) and said reference spectra relating to patients suffering from pneumonia at various advancement stages (see pg. 4130, col. 1, para. 5 – “Afterwards, the spectral information was correlated to its depth in a 2D structure as shown in Fig. 3a. and Fig. 3b.”; see Figure 3 – “Figure 3. Spectral information correlated to the depth of the data in a bi-dimensional matrix a) pneumonic patient b) healthy patient.”).
Furthermore, regarding claim 12, Zenteno further teaches wherein said plurality of parameters characteristic of the correlation of said at least one spectrum with said reference spectra comprises the coefficient of correlation of said average spectrum representing ROI calculated at point (460) with each one of said reference spectra, in that to each one of said classes corresponding to patients suffering from pneumonia at various advancement stages an interval of variability is associated of the diagnostic parameter between a lower end and an upper end, and in that said diagnostic parameter is calculated as a function of the ends of the first and second class for the value of coefficient of correlation, weighted as a function of the respective coefficients of correlation (see pg. 4130, col. 1, para. 5 – “Afterwards, the spectral information was correlated to its depth in a 2D structure as shown in Fig. 3a. and Fig. 3b.”; see Figure 3 – “Figure 3. Spectral information correlated to the depth of the data in a bi-dimensional matrix a) pneumonic patient b) healthy patient. The values obtained the slope in MHz/cm c) pneumonic patient d) healthy patient.”).
Furthermore, regarding claim 13, Zenteno further teaches wherein for each spectrum extracted at point (450) the coefficient of correlation is calculated with each of said reference spectra, in that each spectrum extracted is then defined as healthy, initial, intermediate, advanced or peak spectrum depending on which one is the maximum coefficient of correlation between the various calculated coefficients of correlation, and in that said plurality of parameters characteristic of the correlation of said at least one spectrum with said reference spectra comprises the percentage value of the spectra of each type (healthy spectra, initial spectra, intermediate spectra, advanced spectra, peak spectra) with respect to the whole spectra extracted at point (450) (see pg. 4130, col. 1, para. 5 – “Afterwards, the spectral information was correlated to its depth in a 2D structure as shown in Fig. 3a. and Fig. 3b.”; see Figure 3 – “Figure 3. Spectral information correlated to the depth of the data in a bi-dimensional matrix a) pneumonic patient b) healthy patient. The values obtained the slope in MHz/cm c) pneumonic patient d) healthy patient.”).
The motivation for claims 9-13 was shown previously in claim 1.
Furthermore, regarding claim 14, Carrer further teaches an ultrasound device comprising computing means on which computer programs are loaded, configured to carry out the method according to claim 1 (see claim 1 above; see pg. 2216, col. 2, para. 2 — “The method has been implemented in MATLAB environment without any particular optimization.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Halmann etal. (US 20220061813 A1, published March 3, 2022 with a priority date of August 27, 2020) discloses scoring each ultrasound image based on a percentage (or magnitude) of pleural irregularities in the image.
Chaganti et al. (US 20210304408 A1, published September 30, 2021 with a priority date of April 1, 2020) discloses a classifier trained with imaging data as well as other patient data, such as, e.g., clinical data, genetic data, lab testing, demographics, DNA data, symptoms, epidemiological factors, etc. to detect the presence of COVID-19 (Fig. 2).
Sharma et al. (US 20210330269 A1, published October 28, 2021 with a priority date of April 28, 2020) discloses encoding the one or more normalized extracted imaging features and the patient data into features using a trained machine learning based encoder network, then predicting risk for a medical event associated with evaluating or treating the patient for covid19 based on the encoded features.
Mehanian et al. (US 20200054306 A1, published February 20, 2020) discloses classifying each of the detected features in a lung image as A-line, B-line, pleural line, consolidation, and pleural effusion.
Parker (US 20220207719 A1, published June 30, 2022 with a priority date of September 25, 2020) discloses where the lung ROI is isolated and processed using image segmentation tools, and comparing the instant ROI to an earlier ROI taken in the same place on the same patient.
Shine (US 20230285002 A1, published September 14, 2023 with a priority date of July 31, 2020) discloses the one or more features of the frequency-domain arterial Doppler signal are compared to a library, and the target patient is screened for the medical disorder or disease, such as COVID-19, based on the comparison (Fig. 2).
Soldati et al, “Proposal for International Standardization of the Use of Lung Ultrasound for Patients With COVID-19”, Journal of Ultrasound in Medicine, vol. 39, no. 7, pp. 1413-1419, April 2020 discloses scanning fourteen areas (3 posterior, 2 lateral, and 2 anterior) of the torso (Fig. 1) for lung ultrasound examination of a patient, and a scoring system for severity classification of COVID-19.
Caro et al. (US 20090171231 A1, published July 2, 2009) discloses different frequency spectra corresponding to different disease states (Fig. 6-7).
Blackbourne et al. (US 20160239959 A1, published August 19, 2016) discloses statistical classifications of image features, including A-line, B-line, lung sliding, barcode, sky, seashore, and beach patterns for identifying internal trauma in a patient.
Pernisa et al. (US 20140155748 A1, published June 5, 2014) discloses comparing a frequency spectrum with at least one reference spectrum that is associated with at least one healthy subject-model and/or one pathological subject-model, and calculating a diagnostic parameter on the basis of the comparison.
Al-Abed et al. (US 20130046181 A1, published February 21, 2013) discloses analyzing the received ultrasonic pulses to determine whether or not an airway of the patient is partially or fully occluded.
Quatieri et al. (US 20210315517 A1, published October 14, 2021 with a priority date of April 9, 2020) discloses a system that receives the signal and preforms any “low level” feature extraction to extract/compute features; processes the low-level features to determine high-level features, which include correlation structure features of the low-level features; and processes the high-level features according to predetermined logic, a statistical decision model, and/or a machine learning element (e.g., artificial neural network, ANN) to determine an output, which may represent a categorical decision (e.g., a condition is present or absent) or a quantitative output (e.g., a score or a probability representing a certainty that a condition is present).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nyrobi Celestine whose telephone number is 571-272-0129. The examiner can normally be reached on Monday - Thursday, 7:00AM - 5:00PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pascal Bui-Pho can be reached on 571-272-2714. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Nyrobi Celestine/Examiner, Art Unit 3798