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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more. The system of claim 1 is directed to a machine, which
is one of the statutory categories of invention, and passes Step 1: Statutory Category- MPEP § 2106.03.
However, the following limitations of Claim 1 recite steps that can be performed in the human mind or
with pen and paper, therefore failing Step 2A Prong One. These limitations constitute mental processes
because they describe acts of observation, evaluation, and judgement that can practically be performed in
the human mind, or by a human using pen and paper as a physical aid.
process the plurality of ultrasound images to automatically classify two or more features selected from the group of: A-lines, B-lines, pleural lines, and rib shadows comprised within the acquired plurality of ultrasound images;
automatically assess a clinical quality of the plurality of ultrasound images based on the two or more automatically classified features;
and output an indication of the assessed clinical quality to a user.
Claim 1 fails Step 2A Prong Two because the additional elements beyond the judicial exception do not integrate the judicial exception into a practical application. The claim does not recite a specific asserted improvement in computer technology (MPEP § 2106.05(a)), and, instead, uses a processor, to apply the abstract idea on a computer (MPEP § 2106.05(f)). Furthermore, the claim does not impose meaningful limits on the computer components such that the method is tied to a particular machine; the additional elements are described at a high level of generality and can be implemented on any generic computing system (MPEP § 2106.05(b)). Claim 1 also fails Step 2B, as these additional elements are well-understood, routine, and conventional (WURC), adding nothing significantly more than the abstract idea itself (MPEP § 2106.07(a)((III)); a processor is a generic computer element that is WURC (see MPEP § 2106.05(d)). As claims 2 and 20 contain this identical ineligible subject matter, they are also rejected.
Claims 3-7, 10-11, and 13-16 recite steps that can be performed in the human mind or with pen and paper, therefore failing Step 2A Prong One. These limitations constitute mental processes because they describe acts of observation, evaluation, and judgement that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. These claims, alongside claims 8, 9, 12, and 17-19, also fail Step 2A Prong Two and Step 2B because the additional elements beyond the judicial exception do not integrate the judicial exception into a practical application and are WURC (see claim 1 analysis above). The only other additional element is a machine learning model which also does not integrate the judicial exception into a practical application (see claim 1 analysis above) and is WURC (see Section III. Overview of Machine Learning (ML) of Brattain et. al, “Machine Learning for Medical Ultrasound: Status, Methods, and Future Opportunities”).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8 and 13-20 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Daks et. al (US 2023/0012014 A1).
Regarding Claim 1, Daks teaches an ultrasound imaging system configured for conducting a diagnostic procedure on a subject, the system comprising: an ultrasound imaging probe (Fig. 22 (shown below));
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a computing system (Fig. 22 (shown above));
and a computer-readable storage medium, storing instructions that, when executed by a processor of the computing system cause the ultrasound imaging system to:
Paragraph [0006]: “According to an aspect of the present disclosure, at least one non-transitory computer-readable storage medium is provided storing processor-executable instructions that, when executed by at least one processor on a processing device in operative communication with an ultrasound device, cause the processing device to…”
obtain a plurality of ultrasound images of at least a portion of a lung of a subject (Fig. 7 (shown below));
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Paragraph [0113]: “The selected ultrasound image may be an image of lungs…”
process the plurality of ultrasound images to automatically classify two or more features selected from the group of: A-lines, B-lines, pleural lines, and rib shadows comprised within the acquired plurality of ultrasound images;
Paragraph [0051]: “Pleural lines, ribs, lungs, heart, and liver are examples of anatomical structures that may be identified in some embodiments. For example, an ultrasound image of the lungs may be deemed clinically usable for certain purposes when the ultrasound image includes two ribs, the pleural line, and A lines.”
Paragraph [0053]: “For example, B-lines may be identified. The identification of pathology or other imaging features may be performed automatically in some embodiments. In some embodiments. identification is performed automatically using a statistical model or a machine learning model.”
automatically assess a clinical quality of the plurality of ultrasound images based on the two or more automatically classified features;
Paragraph [0051]: “In some embodiment, more landmarks being present in an ultrasound image corresponds to a higher quality and fewer landmarks being present in an ultrasound image corresponds to a lower quality.”
Paragraph [0056]: “In some embodiments, the ultrasound image with the highest score for clinical usability together with whether either B-lines or A-lines are present is selected as the highest quality image.”
and output an indication of the assessed clinical quality to a user.
Paragraph [0050]: “The statistical model may be trained on multiple ultrasound images, each set of training imaging data labeled with the fraction of the group of individuals who would classify the imaging data as clinically usable… Based on the training, the statistical model may learn to calculate a prediction of the fraction of the group of medical professionals skilled in interpreting ultrasound images who would classify a new ultrasound image of the lungs as clinically usable for evaluating the lung surface for the presence of B-lines.”
Regarding Claim 2, Daks teaches all of the limitations of claim 1 because claim 2 is a method claim that performs substantially the same steps as claim 1.
Regarding Claim 3, Daks teaches the ultrasound imaging system of claim 1, wherein the plurality of ultrasound images is classified based on three or more features selected from the group of A-lines, B-lines, pleural lines, and rib shadows comprised within the acquired plurality of ultrasound images.
Paragraph [0051]: “Pleural lines, ribs, lungs, heart, and liver are examples of anatomical structures that may be identified in some embodiments. For example, an ultrasound image of the lungs may be deemed clinically usable for certain purposes when the ultrasound image includes two ribs, the pleural line, and A lines…in some embodiment, more landmarks being present in an ultrasound image corresponds to a higher quality and fewer landmarks being present in an ultrasound image corresponds to a lower quality.”
Paragraph [0055]: “In some embodiments, the processing device may use a combination of a prediction of a collective opinion of a group of individuals regarding the usability of the ultrasound image, a determination of the presence or absence of landmarks in the ultrasound image to determine the quality of the ultrasound device, and a quality of one or more landmarks in the ultrasound image. Such combination may include any one or more of the listed factors.”
Regarding Claim 4, Daks teaches the ultrasound imaging system of claim 3, wherein the plurality of ultrasound images is classified based on each of the of A-lines, B-lines, pleural lines, or rib shadows present within the acquired plurality of ultrasound images.
Paragraph [0051]: “Pleural lines, ribs, lungs, heart, and liver are examples of anatomical structures that may be identified in some embodiments. For example, an ultrasound image of the lungs may be deemed clinically usable for certain purposes when the ultrasound image includes two ribs, the pleural line, and A lines… in such embodiments, the processing device may use a statistical model trained to determine the locations of particular landmarks as depicted in ultrasound images.”
Paragraph [0053]: “For example, B-lines may be identified. The identification of pathology or other imaging features may be performed automatically in some embodiments. In some embodiments. identification is performed automatically using a statistical model or a machine learning model.”
Regarding Claim 5, Daks teaches the ultrasound imaging system of claim 1, further comprising selecting a diagnostic procedure and acquiring the plurality of ultrasound images, wherein the assessed clinical quality comprises an assessment of the suitability of the acquired images for the selected diagnostic procedure.
Paragraph [0051]: “For example, an ultrasound image of the lungs may be deemed clinically usable for certain purposes when the ultrasound image includes two ribs, the pleural line, and A lines. As another example, an ultrasound image of Morison's pouch may be deemed clinically usable when the ultrasound image includes the liver and kidney.”
Regarding Claim 6, Daks teaches the ultrasound imaging system of claim 1, further comprising determining based on the assessed clinical quality whether the obtained images comprise images of a normal lung or an abnormal lung; and providing the user with an indication of whether one or more of the obtained images are of a normal lung or an abnormal lung.
Paragraph [0050]: “For example, if the ultrasound image is of the lungs, the prediction may be a prediction of the fraction of a group of medical professionals skilled in interpreting ultrasound images who would classify the ultrasound images as clinically usable for evaluating the lung surface for the presence of B-lines.”
Explanation: B-lines are abnormal indicators, showing the differentiation between normal vs abnormal lung patterns.
Regarding Claim 7, Daks teaches the ultrasound imaging system of claim 1, further comprising assessing an overall clinical quality of an image clip comprising the plurality of images and automatically saving the image clip in a memory of the ultrasound imaging system based on detection that the overall quality of the image clip is at least a threshold quality and a length of the image clip is at least a threshold length.
Paragraph [0052]: “For example, in an ultrasound image of the lungs, the quality may be related to the height of the pleural line in the ultrasound image (where the pleural line may be a landmark) … the processing device may measure the height of the pleural line in multiple ultrasound images and determine the quality of the ultrasound image to be proportional to the pleural line height.”
Paragraph [0054]: “In some embodiments, the determination of quality of an image or series of images is based on just one of the clinical usability of the image, the presence of an anatomical feature or landmark, or the quality of a landmark.”
Paragraph [0056]: “In some embodiments, the ultrasound image with the highest score for clinical usability together with whether either B-lines or A-lines are present is selected as the highest quality image.”
Regarding Claim 8, Daks teaches the ultrasound imaging system of claim 7, wherein the image clip length threshold and the image clip quality threshold are a minimum length and minimum quality that are clinically acceptable for completion of the selected diagnostic procedure.
Paragraph [0051]: “For example, an ultrasound image of the lungs may be deemed clinically usable for certain purposes when the ultrasound image includes two ribs, the pleural line, and A lines.”
Regarding Claim 13, Daks teaches the ultrasound imaging system of claim 1, further comprising alerting the user to an absence from one or more of the plurality of images of one or more landmark features, the one or more landmark features comprising a pleural line and/or a rib shadow.
Paragraph [0051]: “In some embodiments, when determining the quality of an ultrasound image, the processing device may determine the presence or absence of landmarks in the ultrasound image. Landmarks may be any type of anatomical feature, such as an anatomical region or structure, that when present in an ultrasound image, may be viewed as an indication that the ultrasound image is clinically usable. Pleural lines, ribs, lungs, heart, and liver are examples of anatomical structures that may be identified in some embodiments.”
Regarding Claim 14, Daks teaches the ultrasound imaging system of claim 1, wherein the indication is provided in real time during a diagnostic procedure and the processing comprises providing the plurality of ultrasound images as input to a machine learning model.
Paragraph [0050]: “The statistical model may be trained on multiple ultrasound images… based on the training, the statistical model may learn to calculate a prediction of the fraction of the group of medical professionals skilled in interpreting ultrasound images who would classify a new ultrasound image of the lungs as clinically usable…”
Paragraph [0053]: “In some embodiments, identification is performed automatically using a statistical model or a machine learning model.”
Regarding Claim 15, Daks teaches the ultrasound imaging system of claim 1, wherein the assessment comprises: automatically determining which lung zone is being scanned and/or the assessment of the clinical quality is based at least in part on the lung zone being imaged.
Paragraph [0048]: “Due to the ultrasound images being collected at different elevational steering angles, the ultrasound images may be collected along different imaging planes relative to the subject.”
Paragraph [0063]: “Each orientation may correspond to a particular imaging plane.”
Explanation: Zone inference is supported by orientation and imaging plane logic, where the zone equals the imaging plane differentiation.
Regarding Claim 16, Daks teaches the ultrasound imaging system of claim 15, wherein the lung zone being imaged is a lower lung zone, and the assessment is based at least in part on a presence, absence, or visibility of one or more alternate organs or alternate features comprised in the plurality of images.
Paragraph [0051]: “As another example, an ultrasound image of Morison's pouch may be deemed clinically usable when the ultrasound image includes the liver and kidney.”
Explanation: This directly supports lower anatomy used for classification.
Regarding Claim 17, Daks teaches the ultrasound imaging system of claim 16, wherein the one or more alternate organs or features comprise a spleen, a liver, a kidney, a spine, a curtain sign, and/or combinations thereof.
Paragraph [0051]: “As another example, an ultrasound image of Morison's pouch may be deemed clinically usable when the ultrasound image includes the liver and kidney.”
Regarding Claim 18, Daks teaches the ultrasound imaging system of claim 15, wherein the machine learning model is trained with training data comprising one or more images annotated with information about rib spacing, A-lines, B-lines, rib shadows, respiratory mode, clinical quality, and/or combinations thereof.
Paragraph [0050]: “The statistical model may be trained on multiple ultrasound images, each set of training imaging data labeled with the fraction of the group of individuals who would classify the imaging data as clinically usable.”
Paragraph [0051]: “In such embodiments, the processing device may use a statistical model trained to determine the locations of particular landmarks as depicted in ultrasound images…Each set of input training data may be an ultrasound image depicting one or more landmarks. Each set of output training data may include multiple segmentation masks for each of the landmarks.”
Regarding Claim 19, Daks teaches the ultrasound imaging system of claim 13, wherein the machine learning model is trained with training data comprising one or more images annotated with information about a health status of a lung the training image.
Paragraph [0050]: “For example, if the ultrasound image is of the lungs, the prediction may be a prediction of the fraction of a group of medical professionals skilled in interpreting ultrasound images who would classify the ultrasound images as clinically usable for evaluating the lung surface for the presence of B-lines…the statistical model may be trained on multiple ultrasound images, each set of training imaging data labeled with the fraction of the group of individuals who would classify the imaging data as clinically usable.”
Regarding Claim 20, Daks teaches all of the limitations of claim 1 because claim 20 recites a non-transitory computer-readable medium that performs substantially the same steps as claim 1. Daks teaches a non-transitory computer-readable medium with processor-executable instructions (see paragraph [0006] (shown above)).
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.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Daks et. al in view of Boussuges et. al (“Ultrasound Assessment of Diaphragm Thickness and Thickening: Reference Values and Limits of Normality When in a Seated Position”).
Regarding Claim 9, Daks teaches the ultrasound imaging system of claim 8, but fails to teach that the minimum image clip length comprises at least a full respiration cycle.
However, Boussuges teaches acquiring and analyzing ultrasound measurements across complete breathing cycles and explains that reliable and clinically meaningful ultrasound assessment requires observing diaphragm thickness and motion during end-expiration, end-inspiration, and deep breathing across the respiratory cycle, stating that “the diaphragm thickness was measured… at the end of expiration (functional residual capacity), at the end of inspiration during quiet breathing at tidal volume (Figure 2), and after deep breathing at total lung capacity (TLC)…measurements were averaged from at least three different breathing cycles” (Ultrasound Examinations). Boussuges further explains that assessment of diaphragmatic function and reliability of ultrasound parameters depends on observing changes over the breathing cycle, because respiratory maneuver quality and image quality directly affect measurement accuracy. The reference emphasizes that variability in ultrasound results can arise from respiratory maneuver quality and therefore teaches structuring ultrasound acquisition to encompass complete respiratory behavior to obtain clinically valid measurements.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to modify the ultrasound imaging system of Daks such that the minimum image clip length comprises at least a full respiration cycle because Boussuges teaches that capturing and evaluating ultrasound data over complete breathing cycles improves the validity and quality of clinical ultrasound assessment. A person of ordinary skill in the art would have been motivated to apply this known acquisition principle to improve the accuracy and clinical adequacy of ultrasound image clips, yielding the expected benefit of more reliable clinical evaluation.
Regarding Claim 10, Daks in view of Boussuges the ultrasound imaging system of claim 9, and Daks further teaches that the image clip is assessed in real time, during performance of the diagnostic procedure.
Paragraph [0065]: “For example, the processing device may monitor the current orientation of the ultrasound device (as determined by the orientation sensors) and provide instructions for fanning the ultrasound device such that its orientation becomes nearer to the orientation at which the ultrasound image selected based on its quality was collected.”
Explanation: This shows real-time interaction.
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Daks et. al in view of Ginghina et. al (“Respiratory maneuvers in echocardiography: a review of clinical applications”).
Regarding Claim 11, Daks teaches the ultrasound imaging system of claim 6, but does not teach that it further comprises automatically detecting that a different mode of respiration would improve the clinical quality of a subsequently acquired plurality of ultrasound images and instructing the user to have the patient perform the different respiratory mode during the diagnostic procedure.
However, Ginghina teaches that different modes of respiration directly improve clinical quality, depending on the imaging context, stating that “expiration will give a better parasternal and often apical access to the heart by lungs deflation...in contrast, in the subxiphoid area inspiration will bring the diaphragm down improving access to the heart” (Influence of normal respiration on echocardiographic parameters). Ginghina also teaches instructing the patient to perform specific breathing maneuvers to improve recording quality, stating that “once the Doppler cursor is aligned optimally and the Doppler tissue preset activated the patient should be asked to breathe in, breathe out, and then hold their breath at the end of expiration” (Doppler echocardiography).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to modify the ultrasound imaging system of Daks to include detecting when a different respiratory mode improves clinical quality and instructing the patient accordingly, because Ginghina explains that doing so improves image quality and measurement accuracy. Incorporating this technique represents the predictable use of a known clinical optimization technique to improve the performance and reliability of the ultrasound assessment.
Regarding Claim 12, Daks in view of Ginghina teaches the ultrasound imaging system of claim 11, and Ginghina further teaches that the different mode of respiration comprises a full exhalation while preventing an inhalation, a full inhalation while preventing an exhalation, a full exhalation with a partial exhalation while inhibiting further inhalation or exhalation, a partial inhalation while inhibiting further inhalation or exhalation, or a full inhalation (see claim 11 above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen (US 12446861 B2) discloses an ultrasound based diagnostic system that acquires lung ultrasound image sequences, automatically identifies clinically relevant features such as A-lines, B-lines, and pleural abnormalities using a machine learning model, and outputs computer-generated clinical assessments to a user via a processor-based system. It would also render a 102 rejection.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM ADU-JAMFI whose telephone number is (571)272-9298. The examiner can normally be reached M-T 8:00-6:00.
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/WILLIAM ADU-JAMFI/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677