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
The amendment filed 01/26/2026 has been entered. Claim 2 is cancelled, and claims 1, 3-15, and 19 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objection and 112(b) rejections previously set forth in the Final Office Action mailed 12/03/2025.
Allowable Subject Matter
The indicated allowability of claim 2 (now incorporated into claim 1) is withdrawn in view of the newly discovered reference(s) to Putha. Rejections based on the newly cited reference(s) follow.
Claim Objections
Claim 1 is objected to because of the following informalities:
The claim should be rearranged such that the claim is easier to read. The current iteration of the claim lumps the limitations into a bulky paragraph. The limitations should be delineated in a fashion more-consistent with accepted patent practice.
Appropriate correction is required.
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, 3-15, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is a method claim that recites a judicial exception (abstract idea).
The “acquiring…images…” step in claim 1 does not specify how to acquire lung images. The physician can print and view a lung image 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 “selecting…a ROI…” step in claim 1 does not specify how to select the ROI in each lung image. The physician can view a lung image and draw (select) a ROI in the lung image 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 “segmenting…ROI in order to select…markers…” step in claim 1 does not specify how to segment the ROI to select the markers. The physician can view a lung image and draw (segment/select) an ROI and markers in the ROI in the lung image 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 “calculating…a parameter relating to the images…” step in claim 1 does not specify how to calculate a parameter. The subsequent calculating steps also does not specify how to calculate the average white lung percentage in lung images. The physician can view lung images and calculate these values from the lung images 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 “calculating a…parameter representing the severity of the pneumonia…” step in claim 1 does not specify how to calculate a parameter. The subsequent calculating steps (“a first step” and “a second step”) also does not specify how to calculate the pneumonia score and average pneumonia score. The physician can view lung images and calculate these values from the lung images 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”, “transducers”) 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 3 is a method claim that recites a judicial exception (abstract idea).
The “calculating…a diagnostic parameter…and a plurality of further parameters…” step in claim 3 does not specify how to calculate a parameter. The subsequent calculating steps also does not specify how to calculate the severity of pneumonia or whole lung disease. The physician can view lung images and calculate these values from the lung images 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 4 is a method claim that recites a judicial exception (abstract idea).
The “modification of first diagnostic parameter value is increased…” step in claim 4 does not specify how to calculate the white lung percentage or whole volume of consolidations in lung images, or how to increase the parameter value. The physician can view lung images and calculate these values from the lung images 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 5 is a method claim that recites a judicial exception (abstract idea).
Claim 5 specifies classifying the diagnostic parameter based on the severity of pneumonia in the lung image. However, the claim does not specify how to classify the diagnostic parameter based on the severity of pneumonia in the lung image. The physician can view lung images and determine pneumonia severity in the lung images 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 6 is a method claim that recites a judicial exception (abstract idea).
The “parameter value is determined by using a classification neural network…” step in claim 6 does not specify how to classify the parameter value. The physician can view lung images and a parameter value of a lung image 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., “neural network”) 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 7 is a method claim that recites a judicial exception (abstract idea).
Claim 7 specifies outputting the probability of belonging to each class. However, the claim does not specify how to output a probability. The physician can view lung images and determine probability of pneumonia severities in the lung images 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., “neural network”) 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 8 is a method claim that recites a judicial exception (abstract idea).
Claim 8 specifies calculating a parameter value (pneumonia score) by using a regression function. However, the claim does not specify how to use a regression function to calculate a score. The physician can view lung images and determine a pneumonia score in the lung images 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 9 is a method claim that recites a judicial exception (abstract idea).
Claim 9 specifies calculating a parameter value (pneumonia score) by using a regression neural network. However, the claim does not specify how to use a regression neural network to calculate a score. The physician can view lung images and determine a pneumonia score in the lung images 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., “neural network”) 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 10 is a method claim that recites a judicial exception (abstract idea).
Claim 10 specifies calculating a parameter value (pneumonia score) by using a classification neural network. However, the claim does not specify how to use a classification neural network to calculate a score. The physician can view lung images and determine a pneumonia score in the lung images 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., “neural network”) 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 11 is a method claim that recites a judicial exception (abstract idea).
The “extracting…the portion corresponding to a relative segment…” step in claim 11 does not specify how to extract a portion of a signal. The subsequent calculating steps also does not specify how to calculate the severity of pneumonia or whole lung disease. The physician can print and view a signal and extract (draw) a portion of the signal 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 “extracting…parameters…in the frequency domain…” step in claim 11 does not specify how to extract parameters in the frequency domain. The subsequent calculating steps also does not specify how to calculate the pneumonia score or average pneumonia score of each image. The physician can print and view a signal and extract (draw) values from the signal, and print and view a lung image and determine scores of the image 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., “transducers”) 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 12 is a method claim that recites a judicial exception (abstract idea).
The “filtering each signal…” step in claim 12 does not specify how to filter the signals. The physician can print and view a signal, then filter (draw) the signal 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 13 is a method claim that recites a judicial exception (abstract idea).
Claim 13 specifies different acquisition positions. However, the claim does not specify how to acquire the different acquisition positions. The physician can view and determine the acquisition positions of the lung images 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 14 is a method claim that recites a judicial exception (abstract idea).
Claim 14 specifies calculating the probability that pneumonia is caused by Sars-Cov-2 virus as a function of information and parameters. However, the claim does not specify how to calculate the probability that pneumonia is caused by Sars-Cov-2 virus. The physician can view and determine a probability value in the lung images 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 15 is a method claim that recites a judicial exception (abstract idea).
The “acquiring…information…” step in claim 15 does not specify how to acquiring patient anamnestic information. The subsequent calculating steps also does not specify how to calculate the Covid Indexes as a function of anamnestic information. The physician can view and determine a Covid Index value in the lung images 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.
For claim 19, general system elements (i.e., “ultrasound device”, “computing devices”, “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 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, 3-15 and 19 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.
For claims 1, 3, and 11, phrases such as “(100) acquiring…”, “(200) selecting…” are indefinite. It is unclear what values such as (100) and (200) indicate. For the purpose of advancing prosecution, the examiner assumes the values indicates method steps, and should be recited as “step (100) of acquiring…” and “step (200) of selecting…” for clarity.
For claim 1, the limitation “ultrasound markers” is indefinite. It is unclear what is an ultrasound marker. For the purpose of advancing prosecution, the examiner assumes an ultrasound marker is an anatomical feature in the ROI.
For claim 1, the limitation “wherein each feature is a numeric descriptive parameter relating to an ultrasound marker individuated on the ultrasound image” is indefinite. It is unclear what is “individuated” on the image, and what is a “numeric descriptive parameter”, and how a “feature” is different from a “marker” in the image. For the purpose of advancing prosecution, the examiner assumes the “feature” and “marker” are the same.
For claim 1, the limitation “further parameter” is indefinite. It is unclear if the further parameter is the same as the “numeric descriptive parameter”. For the purpose of advancing prosecution, the examiner assumes the further parameter and numeric descriptive parameter are the same parameters.
For claim 1, the limitation “white lung tissue” is indefinite. It is unclear what is considered white lung tissue. For the purpose of advancing prosecution, the examiner assumes white lung tissue is an area of the lung that appears white in an ultrasound lung image.
For claim 1, the limitation “increase by one unit the value of said diagnostic parameter value …” is indefinite. It is unclear what is the baseline value of the diagnostic parameter value in order to increase by one unit. For the purpose of advancing prosecution, the examiner assumes the baseline value is zero.
For claim 1, the limitation “a function of the values assumed by said features” is indefinite. It is unclear what is meant by values being “assumed”. For the purpose of advancing prosecution, the examiner assumes the limitation should be “a function of the values of said features” for clarity.
For claim 1, the limitation “in which an average of the first values calculated for each image acquired at step (100) is modified as a function of said at least further parameter… said modification the diagnostic parameter value is increased if an average percentage of "white lung" tissue relating to all the acquired images is greater than a predetermined threshold” is indefinite. It is unclear what is the “first values” and “further parameters” in order to “modify” the first values. It is unclear what is the “diagnostic parameter value” in order to increase its value. For the purpose of advancing prosecution, the examiner assumes the first values, further parameters, and diagnostic parameter value are the same values.
Claims 3-15 and 19 are dependent of claim 1, and therefore rejected under these 112(b) rejections as well.
For claim 3, the limitation “a diagnostic parameter associated to each acquisition position and a plurality of further parameters comprising the percentage of "white lung" tissue” is indefinite. It is unclear if the diagnostic parameter or further parameters are the percentage of white lung tissue. For the purpose of advancing prosecution, the examiner assumes the diagnostic parameter and further parameters are the same parameters (percentage of white lung tissue).
For claim 3, “whole lung disease” is indefinite. It is unclear what is considered whole lung disease. For the purpose of advancing prosecution, the examiner assumes a whole lung disease is a disease that affects the whole lung.
For claim 3, the limitation “a first diagnostic parameter value is assigned as a function of the diagnostic parameter values” is indefinite. It is unclear what is the difference between a “first diagnostic parameter value” and “diagnostic parameter values”. For the purpose of advancing prosecution, the examiner assumes the values are the same.
For claim 3, the limitation “first diagnostic parameter value is modified as a function of said further parameters” is indefinite. It is unclear what is the difference between a first diagnostic parameter and further parameters. For the purpose of advancing prosecution, the examiner assumes the parameters are the same.
Claims 4 and 13-15 are dependent of claim 3, and therefore rejected under these 112(b) rejections as well.
For claim 4, the limitation “the whole volume of all the consolidations individuated” is indefinite. It is unclear what is considered a “whole volume” of consolidations “individuated”. For the purpose of advancing prosecution, the examiner assumes the limitation refers to identifying consolidations in the lung.
For claim 13, the numbering of the acquisition steps (i.e., “1. right lung back…; 2. right lung back…; 14. left lung front…”) should be removed to recite “right lung back…; 2. right lung back…; and left lung front…” for clarity.
Claims 14-15 are dependent of claim 13, and therefore rejected under this 112(b) rejection as well.
For claim 14, the limitation “anamnestic information” is indefinite. It is unclear what is considered anamnestic information. For the purpose of advancing prosecution, the examiner assumes anamnestic information is any information received from a patient.
Claim 15 is dependent of claim 14, and therefore rejected under this 112(b) rejection as well.
For claim 15, the limitation “possible opportunities for Covid contagion occurred recently” is indefinite. It is unclear what is considered “possible opportunities”, and what is considered “recently”. For the purpose of advancing prosecution, the examiner assumes the limitation reads as “Covid contagion occurred”.
For claim 15, the limitation “by summing…” is indefinite. It is unclear the second partial Covid Index value is a function of “the analysis carried out at steps (100) to (550)”, anamnestic information, a function of “the analysis carried out at steps (100) to (550)” and anamnestic information, or a summation of both “the analysis carried out at steps (100) to (550)” and anamnestic information. For the purpose of advancing prosecution, the examiner assumes the second partial Covid Index value is a function of “the analysis carried out at steps (100) to (550).
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, 8, and 19 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 J. Liu et al, "Protocol and Guidelines for Point-of-Care Lung Ultrasound in Diagnosing Neonatal Pulmonary Diseases Based on International Expert Consensus", Journal of Visualized Experiments, no. 145, pp.1-20, Mar. 2019, Mehanian et al. (US 20200054306 A1, published February 20, 2020), and Putha et al. (US 20210327055 A1, published October 21, 2021 with a priority date of June 1, 2020), hereinafter referred to as Carrer, Liu, Mehanian, and Putha, respectively.
Regarding claim 1, Carrer teaches a method for calculating a diagnostic parameter indicating the stage of a pneumonia, implementable by means of a computer program loaded on computing devices associated to an ultrasound device, comprising the steps of:
(100) acquiring, by means of an ultrasound device provided with a probe comprising an array of CMUT (Capacitive Micromachined Ultrasonic Transducer) or piezoelectric transducers (inherent) each configured to emit an ultrasonic impulse directed to lung tissues and to receive raw ultrasonic signal reflected by the tissues of the patient 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."),
a plurality of ultrasound images of the lung of a patient with probe positioned by the user, 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) selecting, inside each image acquired at step (100), a Region of Interest (ROI) comprising the area 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 each of said regions of interest (ROI) in order to select a plurality of ultrasound markers (Ci,..., Cn) therein (see pg. 2208, col. 2, para. 4- "The first part aims at detecting on each image Iv€ I the pleural line by first discriminating it from the background and then reconstructing it by means of a combination of HMM and the VA. 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." Segmenting ROI between background, pleural line, and area beneath pleural line);
(400) calculating for each of said ultrasound markers (Ci,..., Cn) a plurality of features (Cii,..., Cim, Cni,..., Cnm), wherein each feature is a numeric descriptive parameter relating to an ultrasound marker individuated on the ultrasound image; (450) calculating at least a further parameter relating to the images acquired at step (100) (see pg. 2210, col. 2, para. 7 "For each image, the feature vector F is defined as F = [ f1, f2, f3, f4, 5, 6, f7, f8]. Features f1, f2, f3 are related to the pleural line intensity, while features f4, f5, f6, f7, f8 extract relevant statistical information on the intensity of I(j, k) below the pleural line.");
(500) calculating a diagnostic parameter representing the severity of the pneumonia in a portion of lung tissue as a function of the values assumed by said features calculated at step (400), wherein said ultrasound markers comprise pleural line (C1), A-Lines (C2), B-lines (C3) wherein said at least further parameter comprises the percentage of pleural line interested by "white lung" (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 [pleural line]..."; see pg. 2212, col. 1, para. 1 "The feature f2 quantifies phenomena such as white lungs and consolidations [white lung percentage]."; 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)."), and
wherein said diagnostic parameter is calculated with a two steps procedure:
- a first step, in which to each ultrasound image acquired at step (100) a first value of Pneumonia Score is assigned as a function of the following features: number of A-line (C21), the number and configuration of B-lines (C31) and number of pleural line interruptions (C14) (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. 2212, col. 1, para. 2- "High values of f3 correspond to strong variations in the pleural line possibly indicating large pleural disruptions [number of pleural line interruptions]." 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).");
- a second step, in which
Carrer teaches acquiring a plurality of ultrasound images of a lung with a probe, but does not explicitly teach acquiring lung images with the probe positioned parallel to ribs, and acquiring lung images with the probe positioned perpendicular to the ribs.
Whereas, Liu, in an analogous field of endeavor, teaches a plurality of ultrasound images of the lung of a patient in a first acquisition position with probe positioned parallel to ribs, in which it is visible at least the pleural line and a portion of lung below it, and a plurality of images with the probe positioned by the user perpendicular to the ribs (see pg. 4, 5. Scanning methods - "1. Perpendicular scanning 1. Place the transducer perpendicular to the ribs and slide it from the midline to the lateral side along the wide axis to perform the perpendicular scanning. 2. Parallel scanning 1. Rotate the transducer 90° after finishing the perpendicular scanning. Keep the transducer parallel to the ribs and slide it along the narrow axis to realize the parallel scanning.").
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 acquiring a plurality of ultrasound images of a lung with a probe, as disclosed in Carrer, by acquiring lung images with the probe positioned parallel to ribs, as disclosed in Liu. One of ordinary skill in the art would have been motivated to make this modification in order to detect mild lung lesions (i.e., pathological changes involving only 1-2 intercostal spaces and limited to the subpleural areas) or identify the "lung point" when a mild-moderate pneumothorax is suspected, as taught in Liu (see pg. 17, Discussion, para. 2).
Carrer in view of Liu teaches calculating a pneumonia score as a function of features including A lines, B-lines, pleural lines, and white lung, but does not explicitly teach where the pneumonia score is calculated via a regression function or a trained neural network.
Whereas, Mehanian, in the same field of endeavor, teaches where the Pneumonia Score is assigned by means of a regression function or by means of a trained neural network, as a function of the following features: number of A-line (C21), the number and configuration of B-lines (C31), and number of pleural line interruptions (C14) (Fig. 9, diagnosis 180 (score) as a function of A-lines detections 231, B-line detections 232, and pleural line detections 233 (number of pleural line interruptions); see para. 0087 "At a step 170, the ultrasound system 130 (FIG. 8) executes an algorithm (e.g., a CNN or a rule-based algorithm) to analyze the detected and categorized features and severities yielded as the outputs 250 255 for the purpose of rendering a diagnosis. At a step 180, the ultrasound system 130 (FIG. 8) yields a likely diagnosis (e.g., likely pneumonia, likely pneumothorax), based on the features, classifications, and severities yielded as the outputs 250-255.").
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 pneumonia score as a function of features including A-lines, B-lines, pleural lines, and white lung, as disclosed in Carrer in view of Liu, by having the pneumonia score calculated via a regression function or a trained neural network, as disclosed in Mehanian. One of ordinary skill in the art would have been motivated to make this modification in order to further improve the accuracy of calculating the pneumonia score.
Carrer in view of Liu and Mehanian teaches calculating a pneumonia score, but does not explicitly teach calculating average white lung percentage values in all lung images.
Whereas, Putha, in an analogous field of endeavor, teaches in that at step (500) said diagnostic parameter is calculated with a procedure providing: -calculating the average of the percentage of "white lung" tissue in all the acquisitions with probe parallel to the ribs; - calculating the average of the percentage of "white lung" tissue in all the acquisitions with probe perpendicular to the ribs; - calculating an average said two averages; - increase by one unit the value of said diagnostic parameter value if the average percentage of "white lung" tissue relating to all the acquired images is greater than a predetermined threshold (see para. 0092 – “The ROI generator uses the chest X-ray at full resolution and the corresponding anatomy segmentation masks to output a set of ROIs that are most relevant for detecting a particular abnormality…The abnormality detector produces outputs including a low-resolution probability map per ROI, a list of confidence scores, one per ROI, and a confidence score for the entire chest X-ray exam by combining the list of per ROI confidence scores.” it would be inherent to identify white lung tissue (abnormality) percentage values in all acquired lung images, combine (average) the white lung tissue percentage values, then determine a score/value (“unit”) based on the combined white lung tissue percentage values by using pen, paper, and the human mind).
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 pneumonia score, as disclosed in Carrer in view of Liu and Mehanian, by having the pneumonia score calculated by using average white lung percentage values in all lung images, as disclosed in Putha. One of ordinary skill in the art would have been motivated to make this modification in order to further improve the accuracy of calculating the pneumonia score.
Furthermore, regarding claim 8, Mehanian further teaches wherein said first diagnostic parameter value is calculated using a regression function associating to a set of features calculated at step (400) a numeric value of the Pneumonia Score (see para. 0073 "Furthermore, other algorithms that the ultrasound machine 125 (FIG. 8) can use in place of the CNN include a feature extractor followed by one or more of a logistic-regression algorithm [regression function], a support vector machine, a k-nearest neighbor algorithm, and one or more other types of neural networks." Where using a regression function is a known alternative for the skilled person).
Furthermore, regarding claim 19, Carrer further teaches an ultrasound device comprising computing devices 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.").
The motivation for claim 8 was shown previously in claim 1.
Claims 3-4, 6-7, 9-10, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Carrer in view of Liu, Mehanian, and Putha, as applied to claim 1 above, and in further view of J. Chen et al, "Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia with Neural Networks", IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol 68, no. 7, pp. 2507-2515, April 2021, hereinafter referred to as Chen.
Regarding claim 3, Carrer in view of Liu, Mehanian, and Putha teaches all of the elements disclosed in claim 1 above, and
Carrer further teaches wherein, after step (500), comprising further the steps of:
(510) repeating the acquisition of step (100) and the analysis of steps (200)to (450) for a plurality of acquisition positions, and calculating a diagnostic parameter associated to each acquisition position and a plurality of further parameters comprising the percentage of "white lung" tissue (see pg. 2208, col. 2, para. 3 "The proposed method analyzes, in an automatic way, LUS [lung ultrasound] videos to detect and characterize the pleural line, on the basis of a scoring system specifically defined for LUS data obtained on COVID-19patients. This is done by processing the video following an image-by-image approach."; see pg. 2212, col. 1, para. 1 "The feature f2 quantifies phenomena such as white lungs and consolidations.");
(550) calculating a diagnostic parameter representing the severity of the pneumonia as a function of the features calculated at step (400) and of said plurality of further parameters calculated at step (510) (see pg. 2210, col. 2, para. 4 "Indeed, the score value is directly connected to the presence of structures linked to COVID-19 and its severity.") by means of a two steps procedure:
a first step in which to the whole lung disease a first diagnostic parameter value is assigned as a function of the diagnostic parameter values associated to each acquisition position (see pg. 2210, col. 2, para. 3 "The automatic determination of the position and intensity characteristics of the pleural line described by p allows us to quantitatively analyze its properties and to define a scoring procedure based on the classification approach...'");
a second step in which said first diagnostic parameter value is modified as a function of said further parameters calculated at step (510) (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 scores." known in the art white lung tissue is associated with pneumonia, so it is inherent that a pneumonia score would be higher if the presence of white lung tissue in the ultrasound image is above a certain threshold).
Carrer in view of Liu, Mehanian, and Putha teaches acquiring a plurality of acquisition positions, but does not explicitly teach where the plurality of acquisition positions are different lung areas.
Whereas, Chen, in the same field of endeavor, teaches a plurality of acquisition positions relating to different lung areas (see pg. 2508, col. 1, para. 5 "All patients underwent LUS [lung ultrasound] examinations for 12 standard fields [lung areas] on both hemithorax, including the upper and lower halves of the anterior, lateral, and posterior fields [28].").
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 acquiring a plurality of acquisition positions, as disclosed in Carrer in view of Liu, Mehanian, and Putha, by acquiring the plurality of acquisition positions at different lung areas, as disclosed in Chen. One of ordinary skill in the art would have been motivated to make this modification in order to assignee lung ultrasound scores automatically with high accuracy, as taught in Chen (see Abstract).
Furthermore, regarding claim 4, Carrer further teaches wherein said modification of first diagnostic parameter value is increased if the average percentage of "white lung" tissue relating to all the acquired images is greater than a predetermined threshold, or if the whole volume of all the consolidations individuated in all the acquisition positions is greater than a predetermined threshold (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 scores." known in the art white lung tissue is associated with pneumonia, so pneumonia score would be higher if the presence of white lung tissue in the ultrasound image is above a certain threshold).
Furthermore, regarding claim 6, Chen further teaches wherein said first diagnostic parameter value is determined by using a classification neural network, configured to receive in input the values calculated for said features and to provide in output a vector containing the values of probability of belonging to each one of said classes and trained by using a set of parameters calculated relating to lung ultrasound images of a plurality of patients whose disease advancement stage is known and for whom the severity class has been defined by skilled operators as a function of the ultrasound scan analysis, and/or as a function of information derived from other diagnostic examinations (see pg. 2510, col. 1, para. 4- "With the features extracted from the ROI as input, we applied one- and two-layer fully connected neural networks to learn the data with a set of different number of hidden nodes..." where a neural network is a known alternative if sufficient training data is available).
Furthermore, regarding claim 7, Chen further teaches wherein said classification neural network provides in output a vector containing the probability of belonging to each class, wherein to each one of said classes (see pg. 2511, col. 1, para. 1 "5) In Step (5), the deep learning model for automated lung ultrasound scoring was developed." where a neural network is a known alternative ifsufficient training data is available).
Furthermore, regarding claim 9, Chen further teaches wherein said first diagnostic parameter value is calculated by using a regression neural network, configured to receive in input the values of said features and to provide in output a Pneumonia Score parameter value and trained by using a set of parameters relating to lung ultrasound images of a plurality of patients whose disease advancement stage is known, and for whom the Pneumonia Score has been defined by skilled operators as a function of an ultrasound scan analysis, and/or as a function of information derived from other diagnostic examinations (see pg. 2510, col. 1, para. 4 "With the features extracted from the ROI as input, we applied one- and two-layer fully connected neural networks to learn the data with a set of different number of hidden nodes..." where a neural network is a known alternative if sufficient training data is available).
Furthermore, regarding claim 10, Chen further teaches wherein said first diagnostic parameter value is calculated by using a classification neural network trained by using a set of parameters relating to lung ultrasound images of patients whose disease advancement stage is known, and for whom the severity class has been defined by skilled operators as a function of an ultrasound scan analysis, and/or as a function of information derived from other diagnostic examinations, and subsequently a regression neural network, receiving as input the output vector of the classification neural network (see pg. 2511, col. 1, para. 1 "5) In Step (5), the deep learning model for automated lung ultrasound scoring was developed." where a neural network is a known alternative if sufficient training data is available).
Furthermore, regarding claim 12, Carrer further teaches wherein, after step (410) and before step (420), comprising further the steps of: (415) filtering each signal extracted at step (410) with a band-pass filter (see Fig. 4 "Examples of results obtained by applying the circular filter with radius W1 to a convex LUS image.").
Furthermore, regarding claim 13, Chen further teaches wherein said plurality of acquisition positions of step (510) comprises one or more of the following ones (see pg. 2508, col. 1, para. 5 "All patients underwent LUS [lung ultrasound] examinations for 12 standard fields [lung areas] on both hemithorax, including the upper and lower halves of the anterior, lateral, and posterior fields [28]."):
1. right lung back portion scan, lower quadrant;
2. right lung back portion scan, middle quadrant;
3. right lung back portion scan, higher quadrant;
4. left lung back portion scan, lower quadrant;
5. left lung back portion scan, middle quadrant;
6. left lung back portion scan, higher quadrant;
7. right lung sub-axillary/lateral portion scan, lower quadrant;
8. right lung sub-axillary/lateral portion scan, higher quadrant;
9. left lung sub-axillary/lateral portion scan, lower quadrant;
10. left lung sub-axillary/lateral portion scan, higher quadrant;
11. right lung front portion scan, lower quadrant;
12. right lung front portion scan, higher quadrant;
13. left lung front portion scan, lower quadrant;
14. left lung front portion scan, higher quadrant.
Furthermore, regarding claim 14, Mehanian further teaches the calculation of a statistical parameter indicating the probability that the pneumonia is caused by Sars-Cov-2 virus (Covid Index), as a function of anamnestic information provided by the patient and of the value of the diagnostic parameters calculated for each acquisition position at step (500) (Fig. 9, anamnestic information includes lung sliding and pleural effusion; see para. 0060 "Next, at astep170, the ultrasound machine 125 executes a diagnosis algorithm that evaluates the classified lung features to render a lung pathology diagnosis 180. For example, such a pathology diagnosis is in response to the determined likelihood of the presence, and the respective severities, of conditions such as less-than-normal, or absence of, lung sliding, A-line, B-line, pleural effusion, consolidation, and merged B-line.").
Furthermore, regarding claim 15, Mehanian further teaches wherein said statistical parameter is calculated:
by acquiring from the patient anamnestic information relating to the presence of symptoms and possible opportunities for Covid contagion occurred recently (Fig. 9, anamnestic information includes lung sliding 250 and pleural effusion severity 255);
- by calculating a first partial Covid Index value as a function of said anamnestic information (see para. 0087 "Ata step 170, the ultrasound system130 (FIG. 8) executes an algorithm (e.g., a CNN or a rule-based algorithm) to analyze the detected and categorized features and severities yielded as the outputs 250-255 [anamnestic information] for the purpose of rendering a diagnosis.");
- by summing to said first partial Covid Index value, as a function of said anamnestic information, a second partial Covid Index value as a function of the analysis carried out at steps (100) to (550) (see para. 0087 "Ata step180, the ultrasound system 130 (FIG. 8) yields a likely diagnosis (e.g., likely pneumonia, likely pneumothorax), based on the features, classifications, and severities yielded as the outputs 250 255.").
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Carrer in view of Liu, Mehanian, and Putha, as applied to claim 1 above, and in further view of Z. Hu et al, "Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images", BioMedical Engineering OnLine, vol. 20, no. 27, pp. 1-15, March 2021, hereinafter referred to as Hu.
Regarding claim 5, Carrer in view of Liu, Mehanian, and Putha teaches all of the elements disclosed in claim 1 above, and
Carrer further teaches wherein said diagnostic parameter is expressed by means of the classification of the pneumonia in a severity class, the first one of said classes corresponding to the absence of disease (see pg. 2210, col. 2, para. 4 "Indeed, the score value is directly connected to the presence of structures linkedtoCOVID-19 and its severity. In this method, we consider the scoring system recalled in Section I and described in [24], which consists of four possible scores S ranging from 0 to 3. Therefore, each score value can be seen as a class.").
Carrer in view of Liu, Mehanian, and Putha teaches classifying severity of pneumonia in lung ultrasound images, but does not explicitly teach classifying severity of pneumonia in lung ultrasound image between five or more classes.
Whereas, Hu, in the same field of endeavor, teaches wherein said diagnostic parameter is expressed by means of the classification of the pneumonia in a severity class chosen between five or more increasing severity classes, the first one of said classes corresponding to the absence of disease (see pg. 12, para. 5 to pg. 13, para. 1 "We predicted the patient's per part ultrasound video of multiple examinations through the trained MCRFNet and classified and scored sonograms according to the paper [37].. After the classification result is quantified, the sum is divided by all the frames to obtain the final lung function severity score, which is 0 to 4.").
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 classifying severity of pneumonia in lung ultrasound images, as disclosed in Carrer in view of Liu, Mehanian, and Putha, classifying severity of pneumonia in lung ultrasound image between five or more classes, as disclosed in Hu. One of ordinary skill in the art would have been motivated to make this modification in order to provide a more precise diagnosis of ventilation loss, as taught in Hu (see pg. 12, para. 5).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Carrer in view of Liu, Mehanian, and Putha, as applied to claim 1 above, and in further view of F. Corradi et al, "Computer-Aided Quantitative Ultrasonography for Detection of Pulmonary Edema in Mechanically Ventilated Cardiac Surgery Patients", Chest, vol. 150, no. 3, pp. 640-651, Sept. 2016, hereinafter referred to as Corradi.
Regarding claim 11, Carrer in view of Liu, Mehanian, and Putha teaches all of the elements disclosed in claim 1 above, and
Carrer further teaches, after step (400) and before step (500), comprising further the following steps of:
(410) extracting from each raw ultrasonic signal received by each one of said CMUT or piezoelectric transducers of step (100), the portion corresponding to a relative segment of ultrasound image contained inside a consolidation individuated at step (300) (see pg. 2212, col. 1, para. 1 "The feature f2 quantifies phenomena such as white lungs and consolidations."), and
characterized in that at step (500) said diagnostic parameter is calculated with a two steps procedure:
a first step, in which to each ultrasound image acquired at step (100) a first Pneumonia Score value is assigned as a function of the number of A- lines, of the number and configuration of B-lines and of the pleural line continuity (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. 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 [pleural line]..."; see pg. 2212, col. 1, para. 3 "The area below the pleural line is of particular importance as it contains several indicators ofthe pathological conditions (e.g., A and B lines).");
a second step, in which the average of the first values calculated for each image acquired at step (100) is modified as a function of said parameters calculated at step (450) and of said set of parameters characteristic of the average spectrum relating to consolidations (see pg. 2215, col. 1, para. 1 "The individual distribution of the predicted scores (i.e., one video) is a potential indicator of the average health status of the patient in the anatomical region where the video has been acquired."; see pg. 2211, col. 2, para. 2 "The second feature quantifies the average intensity of the portion of the image I(j, k) below the pleural line with respect to the intensity value of the pleural line I(p(k), k) for each k…The feature f2 quantifies phenomena such as white lungs and consolidations.").
Carrer in view of Liu, Mehanian, and Putha teaches extracting a set of parameters from lung ultrasound images, but does not explicitly teach extracting a parameter of a lung ultrasound image in the frequency domain.
Whereas, Corradi, in the same field of endeavor, teaches (420) carrying out an analysis in the frequency domain of each raw ultrasonic signal extracted at step (410) by extracting a set of parameters characteristic of the signal in the frequency domain (Fig. 1 and 2; see pg. 642, col. 1, para. 1- "Afterwards, a pixel-per-pixel algorithm was used to calculate the frequency distribution of 255 different echo intensities (Gy units) of each areas considered. The mean value of echo intensity of each of the eight regions ofinterest was determined, and their mean was retained for analysis.").
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 extracting a set of parameters from lung ultrasound image, as disclosed in Carrer in view of Liu, Mehanian, and Putha, by extracting a parameter of a lung ultrasound image in the frequency domain, as disclosed in Corradi. One of ordinary skill in the art would have been motivated to make this modification in order to provide a better diagnostic accuracy and being independent of operator perception, as taught in Corradi (see Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wang et al. (US 20220092788 A1, published March 24, 2022 with a priority date of January 18, 2021) discloses the plurality of pneumonia sign images are combined to obtain a pneumonia comprehensive sign image.
Blackbourne et al. (US 20160239959 A1, published August 18, 2016) discloses identification of internal traumas are based on statistical classifications of image features, including A-line, B-line, lung sliding, barcode, sky, seashore, and beach patterns.
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/N.C./Examiner, Art Unit 3798