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 .
Response to Amendment
Claims 1-14 remain pending in the application in response to the applicant’s amendments to the rejections previously set forth in the Non-Final Office Action mailed 07/30/2025.
Response to Arguments
Applicant's arguments filed 10/01/2025 have been fully considered but they are not persuasive.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Carrer teaches segmenting an ultrasound image, while Zenteno teaches extracting frequency spectra correlated to healthy and pneumonic patients.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “the present invention are used to map the corresponding segment of RF signals that are subsequently submitted to a dedicated spectral analysis” (see pg. 4, para. 5 of applicant’s remarks)) 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).
Claim 1 does not recite segmenting RF signals. Therefore, under broadest reasonable interpretation, Carrer teaches segmenting an ultrasound image, while Zenteno teaches extracting frequency spectra correlated to healthy and pneumonic patients.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “One spectra reference library associated to many stages of illness” (see pg. 5, para. 3 of applicant’s remarks)) 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).
Claim 1 does not recite a “spectra reference library” associated with stages of “illness”. Therefore, under broadest reasonable interpretation, Carrer teaches segmenting an ultrasound image, while Zenteno teaches extracting frequency spectra correlated to healthy and pneumonic patients.
For claim 1, the applicant argues “Teaching of Zenteno is limited to a binary classification, not to a progressive staging” (see pg. 5 of applicant’s remarks), and the examiner disagrees. The claim does not specify the number of stages of pneumonia. Therefore, under reasonable interpretation, the stages of pneumonia include “healthy” (no pneumonia, which is a stage of pneumonia), and “pneumonia”. Even if the claim was to recite a number of stages of pneumonia in addition to no pneumonia, it would be inherent to provide additional thresholds to the parameters to correlate to different stages of pneumonia.
Claim Objections
Claims 1-14 are objected to because of the following informalities:
Claims 1-14 should be rearranged such that the claim is easier to read. The current iteration of the claims lumps the limitations into a bulky paragraph. The limitations should be delineated in a fashion more-consistent with accepted patent practice.
In claims 1-14, phrases such as “point (100)” should be “step (100)” for clarity.
In claim 1, “an array of CMUT” should be “an array of capacitive micromachined ultrasound transducers (CMUTs)” for clarity. Appropriate correction is required.
In claim 1, the space between “consolidations” and “(C4)” should be removed.
In claim 1, “the progression stage” should be “a progression stage” for clarity.
In claim 7, “as in the previous claims” should be removed for clarity.
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 claims 1-14, it is unclear what phrases such as “(100) acquiring…” and “(300) segmenting…” represents. For the purpose of advancing prosecution, the examiner assumes the phrases refers to steps, such as “step (100) of acquiring…” and “step (300) segmenting…”.
For claim 1, “individuate a set of ultrasound markers therein, relating to the pleural line (Ci), A-lines (C2), B-lines (C3), consolidations” is indefinite. It is unclear what is meant by markers are “individuate”. It is also unclear what “therein” refers to (i.e., segmenting, region under the pleural line, ultrasound markers, something else) It is also unclear what is related to the pleural line, B-lines, consolidations. For the purpose of advancing prosecution, the examiner assumes the ultrasound markers are segmented in the image.
Claims 2-14 are dependent of claim 1, and therefore rejected under these 112(b) rejections as well.
For claim 2, the Pneumonia score function is unclear. It is unclear what values the parameters “Cor1a”, “Cor1e”, “Cor4a”, and “Cor4e” represent. For the purpose of advancing prosecution, the examiner assumes the parameters represent “correlation parameter values” (see claim 3).
For claim 13, “…(an healthy patient spectra…peak stage disease spectra)…” should be “of a health patient spectra…and peak stage disease spectra,…” for clarity.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 14 recites a judicial exception (abstract idea). The “acquiring…”, “segmenting…”, “extracting frequency spectra…”, “comparing…”, and “calculating…” steps do not specify how to acquire the ultrasound image, segment the image, extract frequency spectra, compare extracted spectra with reference spectra, and calculate a parameter. The physician can print and view an ultrasound image, draw (segment) the image into regions, extract frequency spectra, compare extracted spectra with reference spectra, and calculate a parameter using their mind and a pen (see MPEP 2106, section III, step 2A of subject matter eligibility test flowchart). This judicial exception is not integrated into a practical application because the step in the claim can be considered as processes that can be performed in the human mind (see MPEP 2106.04(a)(2)(III)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
General system elements (i.e., ultrasound device, probe, computing device, computer programs) related to the insignificant extra solution activity steps that do not integrate the abstract idea into a practical application as it does not impose any meaningful limits on practicing the abstract idea.
Claim 2 merely specifies what is considered a “regressor” and an equation. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 3 recites a judicial exception (abstract idea). The “…parameter is calculated by using a regression neural network…” step does not specify how to calculate the parameter by using a regression neural network. The physician can calculate the parameter using their mind and a pen (see MPEP 2106, section III, step 2A of subject matter eligibility test flowchart). This judicial exception is not integrated into a practical application because the step in the claim can be considered as processes that can be performed in the human mind (see MPEP 2106.04(a)(2)(III)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claims 4-5 merely specifies the continuity of the pleural line, the visibility of the A-line, and what is considered the ROI. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claims 6-7 merely specifies the continuity of the pleural line, the visibility of the A-line and B-lines, and what is considered an ROI. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 8 merely specifies the consolidation is “individuated”. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 9 merely specifies what is considered a “reference spectra”. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 10 recites a judicial exception (abstract idea). The “calculating the average…” step does not specify how to calculate the average of extracted spectra. The physician can calculate the average of extracted spectra using their mind and a pen (see MPEP 2106, section III, step 2A of subject matter eligibility test flowchart). This judicial exception is not integrated into a practical application because the step in the claim can be considered as processes that can be performed in the human mind (see MPEP 2106.04(a)(2)(III)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 11 recites a judicial exception (abstract idea). The “calculating the coefficient of correlation…” step does not specify how to calculate the coefficient of correlation. The physician can calculate the coefficient of correlation using their mind and a pen (see MPEP 2106, section III, step 2A of subject matter eligibility test flowchart). This judicial exception is not integrated into a practical application because the step in the claim can be considered as processes that can be performed in the human mind (see MPEP 2106.04(a)(2)(III)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 12 merely specifies what is considered a “coefficient of correlation”. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 13 merely specifies correlating the spectra to different disease stages. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
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 using an ultrasound device provided with a probe comprising an array of CMUT or piezoelectric transducers 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, at least an ultrasound image of the lung of a patient, in which at least a pleural line and a portion of lung below the pleural line is visible in the ultrasound image (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.");
(200) segmenting, inside said at least one image acquired at point (100), the region under the pleural line (see pg. 2208, col. 2, para. 5 "To efficiently detect and characterize the pleural line in LUS data, anautomatic detection method should possess the following requirements: 1) it should discriminate between the pleural line and other LUS data features such as the ribs...");
(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).");
segmenting further comprises the steps of:
(310) segmenting at least a region of interest (ROI) as a function of a 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.
(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 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 using 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 (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 lb.").
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 Figure3 "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 (an healthy patient spectra, initial stage disease spectra, intermediate stage disease spectra, advanced stage disease spectra, peak stage disease 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 a computing device 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:
Gutierrez (US 20190371460 A1, published December 5, 2019) discloses ventilator data and airway signals were captured continuously and displayed as 2.5-minute epochs for classification by clinicians experienced in mechanical ventilation and asynchrony identification. These data, along with the frequency spectra of airway signals, formed a database used to train Random Forest classifier algorithms to grade its severity.
Kaffas (US 20210077073 A1, published March 18, 2021) discloses the entire spectrum of data or a specific subset of the spectrum can be used as a feature set to train an ML model to evaluate and classify tissues in ultrasound images for screening, diagnostics and treatment monitoring applications. The spectrum-based features as is, or normalized (using standard methods) can be used as is, or combined with conventional parameters from linear regression or mathematical models.
Shine (US 20230285002 A1, published September 13, 2023 with a priority date of July 31, 2020) discloses the one or more features of the frequency-domain arterial Doppler signal for the target patient is the spectrum of frequencies and corresponding amplitudes the frequency-domain arterial Doppler signal for the target patient (e.g., frequency components and amplitudes). This information can be compared to the respective spectra of frequencies and amplitudes of the arterial Doppler waveforms for the historical patients, which are associated with specific medical disorders or diseases, stored in the library. The comparison can yield a probability score for a presence of the medical disorder or disease in the target patient.
Pernisa et al. (US 20140155748 A1, published June 5, 2014) discloses defining a region of interest of bone region on sonographic image; calculating frequency spectra on the basis of raw reflected signals coming from bone region or, possibly, from region of interest; and 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.
Sathyanarayana (US 20090270731 A1, published October 29, 2009) discloses the entire frequency spectrum of a sample region, or only a portion of the frequency spectrum, can be used as the input spectrum for the tissue classifier; and, from the tissue classifer, generate a characterization output and, optionally, a confidence level for that tissue characterization.
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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/N.C./Examiner, Art Unit 3798
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