DETAILED ACTION
This Office action is responsive to communications filed on 05/19/2026. Claims 1, 4, 7, 9, 11-12, 17-18, 20 have been amended. Claims 2 & 19 are canceled. Presently, Claims 1, 3-18, 20 remain pending and are hereinafter examined on the merits.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/19/2026 has been entered.
Response to Arguments
Previous rejections under 35 USC § 112(a) are withdrawn in view of the amendments filed on 05/19/2026.
The Applicant’s arguments with respect to rejections under 35 USC § 101 have been fully, considered, but are not persuasive.
The 35 USC § 101 rejection did not assert the claim ultrasonic transmission or signal acquisition steps themselves as a mental process, please refer to the 35 USC § 101 rejection filed herein and in the final rejection filed on page 4 for reference. In the rejection, it identifies the recited calculations and evaluations as mathematical concepts and mental processes under Step 2A, Prong One. The claims recites calculating frequency dispersion distribution diagram, calculating a viscosity parameter based on slops, differences or ratio of phase velocities, obtaining a quality control characteristic based on the calculated dispersion information, and performing quality control based on the calculated information. These limitations were identified as mathematical relationships, mathematical calculations, and evaluative determinations derived from the mathematical analysis of data, as it was explained in the previous office action and herein. Applicant’s argument that a human mind cannot physical transmit ultrasonic waves, acquire echoes signals, or directly process raw ultrasound data does not address the identified abstract limitations recited in the claims. The fact that data is obtained form an ultrasound system does not remove the subsequently performed mathematical calculations form the mathematical concepts grouping. In fact, the premise of the invention is just to derive through mathematical manipulation and output desired results. Accordingly, the claims continue to recite mathematical concepts and mental processes under Step 2A, Prong One.
The Applicant additional argues that the claims involve filtering, applying, beamforming, complex, extensive calculations to generate the required images and parameters. The claims don’t require this or any other particular technological implementation. These arguments are directed to features and details not recited in the claims and therefore cannot establish eligibility.
Applicant’s arguments regarding improved ultrasonic diagnostic outcomes are not persuasive under Step 2A, Prong Two. The claims don’t recite any improvement to ultrasound acquisition, hardware, or signal processing technology or even computer functionality, or image generation. Rather, the claims merely require receiving ultrasound data, perform mathematical analysis on that data, derive parameters and quality metrics, and display desired results. This alleged improvement is not an improvement to the technology itself. Therefore, the claims do not integrate the judicial exception into a practical application.
The Applicant’s reliance that the claimed method are applied in the field of ultrasound and thus a practical application is present, is unpersuasive. Limiting the use of an abstract idea to a particular technological environment or field of use does not integrate the exception into a practical application. Its just the environment for the abstract idea, here the mathematical analysis to be performed in.
The Applicant argues that the calculating steps and displaying information form an integrated unconventional technical mechanism that provides reliable viscosity information. These limitations correspond to the mathematical calculations and evaluation steps identified in Step 2A, Prong One.
Accordingly, the 35 USC § 101 rejection is maintained.
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on the 35 USC § 103 rejection Mischi et al (US 2020/0121288 A1) in view of Bhatt et al ("Reconstruction of Viscosity Maps in Ultrasound Shear Wave Elastography," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 66, no. 6, pp. 1065-1078, June 2019), applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The new grounds of rejection now relies on a 35 USC § 102 rejection as anticipated by Berry et al (Shear wave dispersion measures liver steatosis. Ultrasound Med Biol. 2012 Feb).
Claim Objections
The following claims are objected to because of the following informalities and should recite:
Claim 4: “[[a]]the frequency dispersion characteristic graph”.
Claim 7: “[[a]]the value”.
Consistent claim language is required when referring to the same term. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-18, 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth 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.
Claim 1: line 13 “a ratio” vs line 26, “a single-to-noise ratio”. It is unclear if the terms refers to or is separate from each other. For examination purposes, the Examiner assumes these recitations are different. Clarity is needed. Similarly, the same applied to claims 12 & 18 for reciting substantially the same limitations.
The dependent claims of the above rejected claims are rejected due to their dependency.
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, 3-18, 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 1, 3-11, 20 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Claim 12-18 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One:
Claim 1 recite (“sets forth” or “describes”) the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), & the abstract idea of “mathematical concepts” (MPEP 2106.04(a)(2).I.), substantially as follows:
“ calculating, [...], a frequency dispersion distribution diagram according to the ultrasonic echo signals, wherein the frequency dispersion distribution diagram represents a relationship between a shear wave propagation velocity and a shear wave frequency;
calculating, [...], a viscosity parameter and/or a viscosity parameter distribution diagram according to the frequency dispersion distribution diagram, wherein the viscosity parameter comprises a slope of a frequency dispersion curve of the shear wave propagation velocity with respect to the shear wave frequency in the frequency dispersion distribution diagram, a difference between phase velocities of the shear waves of at least two different frequencies in the frequency dispersion distribution diagram, or a ratio between the phase velocities of the shear waves of at least two different frequencies in the frequency dispersion distribution diagram;
obtaining, [...], a viscosity quality control characteristic of the viscosity parameter according to the frequency dispersion distribution diagram; and
[...]
wherein the viscosity quality control characteristic comprises one or more of characteristic quantities:
an effective frequency range for calculating the viscosity parameter;
a degree of matching when performing model fitting for the viscosity parameter according to the frequency dispersion distribution diagram which represents the shear wave propagation velocity versus the shear wave frequency;
a signal-to-noise ratio of the frequency dispersion distribution diagram; and
the shear waves in multiple patterns in the frequency dispersion distribution diagram.”
Claim 12 recite (“sets forth” or “describes”) the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), & the abstract idea of “mathematical concepts” (MPEP 2106.04(a)(2).I.), substantially as follows:
“ calculating, [...] a frequency dispersion distribution diagram according to the ultrasonic echo signals;
calculating, [...] a viscosity parameter and/or a viscosity parameter distribution diagram according to the frequency dispersion distribution diagram; and
performing, [...] quality control on the viscosity parameter according to the frequency dispersion distribution diagram,
wherein the calculating, [...] the viscosity parameter according to the frequency dispersion distribution diagram comprises:
calculating a slope of a shear wave propagation velocity with respect to a shear wave frequency based on a frequency dispersion curve in the frequency dispersion distribution diagram as the viscosity parameter,
or
calculating a difference between phase velocities of the shear waves of at least two different frequencies in the frequency dispersion distribution diagram as the viscosity parameter,
or
calculating a ratio between the phase velocities of the shear waves of at least two different frequencies in the frequency dispersion distribution diagram as the viscosity parameter.”
For each claim (1, 12), the above recited steps are mathematical concepts, which is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. Specifically, the step of calculating a frequency dispersion distribution diagram” involves generating a graph or dataset reflecting the relationship between frequency and phase velocity of shear waves, which constitutes mathematical process of data derived from a datasheet of signals. The step of calculating a viscosity parameter” comprises the derivation of the quantitative value (e.g., using the viscoelasticity or Voigt model equation) to arrive at a mathematical model or formulas applied to the frequency dispersion data. Similarly, ‘obtaining a viscosity control characteristic” entails further mathematical or statistical analysis of the derived viscosity parameter. In relation to the frequency dispersion diagram, such as determining a measure of accuracy, reliability or consistency, that if further based on mathematical operations. The addition, performing quality control on the viscosity parameter according to the frequency dispersion diagram, remains rooted in abstract idea of mathematical concepts. It amounts to applying mathematical rules or a predetermined threshold and comparing to a previously calculated parameter, based on the mathematical analysis of the frequency distribution dispersion diagram. This involves nothing more than statistical comparison. With respect to the added recitations, these limitations merely define additional evaluative metrics derived from already calculated dispersion data. Determining an effective frequency range entails identifying numerical boundaries within a data set based on predetermined criteria. Assessing a degree of matching when performing model fitting required computing a goodness fit value (i.e., correlation coefficient) between observed data and a selected mathematical model. Calculating a signal-to-noise ration involves computing a ratio between signal magnitude and noise magnitude based on observed data analysis. Identifying shear waves in multiple patterns within the diagram entails recognizing and categorizing patterns based on their mathematical represented characteristics in the dispersion plot. Each of these operations depends on mathematical relationships and statistical calculation, the premise of the instant application with respect to ultrasound image techniques. These limitations specify additional mathematical analysis performed on the data. Similarly, the further recited limitations of claim 12 reinforce that the viscosity parameter itself is derived exclusively through mathematical manipulation of dispersion data. Calculating a slop of frequency dispersion curve is a mathematical concept itself. Calculating a slope of frequency dispersion curve with respect to shear wave frequency and propagation velocity constitutes determining a rate of change (i.e., linear regression-mathematical). Alternatively, calculating the viscosity parameter according the phase velocities at two or more frequencies requires computing differences from data points. These recitations further dividing the calculation of the viscosity parameter are quintessential mathematical concepts.
Each of these steps involves mathematical relationships, formulas, or calculations that are not merely incidental to the claim invention, but are central to the method and its results. In fact, these limitations viewed collectively and as a whole, recite a series of mathematical operations that amount to nothing more than a build-up of mathematical concepts layered upon other mathematical concepts.
The grouping of “mathematical concepts” in the 2019 PEG includes “mathematical calculations” as an exemplar of an abstract idea. 2019 PEG Section I, 84 Fed. Reg. at 52. Thus, limitation falls into the “mathematical concept” grouping of abstract ideas. The grouping of “mathematical concepts” in the 2019 PEG includes “mathematical calculation”
Therefore, each of the above steps are grouped as mathematical and mental concepts, hence an abstract idea.
Prong Two: Claims 1 and 12 do not include additional elements that integrate the mental process into a practical application.
This judicial exception is not integrated into a practical application. In particular, the claims recites (1) additional steps of transmitting, by a ultrasound probe, ultrasonic waves for detecting shear waves to obtain ultrasonic echo signals, the shear waves being propagated in a region of interest; (claim 1, 12); and (2) further an addition step of outputting displaying viscosity quality control information about the viscosity parameter according to the viscosity quality control characteristic. (claim 1)
The steps in (1) represent merely data gathering or pre-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality with conventionally used tools (see below Step IIB for further details). Data gathering and mere instructions to implement an abstract idea on a computer do not integrate a judicial exception into a practical application (MPEP 2106.05 (f and g)).
The step in (2) represents merely outputting information outputting by a display as a post-solution activity and is recited at a high level of generality.
Lastly, Regarding the limitations of claim 1 & 12, directed to the “by a processor”/”by the processor” is treated as a generic computer implementation, which falls under mere instructions to apply the abstract idea on a computer and therefore does not place the abstract idea into a practical application that solves a technological solution in a meaningful way or improve the functionality of the technology or generic computer “itself”. Simply, it’s a generic computer implementation of a mental process rather than a meaningful limitation. Regarding the processor language written at such a high level of generality of structural limitations, the processor language amounts to a generic computer component with mere instructions to implement the abstract idea on a computer.
As a whole, the additional elements merely serve to gather and feed information to the abstract idea and to output a notification based on the abstract idea, while generically implementing it on conventionally used tools. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the estimated bio-information is not outputted in any way such that a practical benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, there is no evidence of record that would support the assertion that this step is an improvement to a computer or technological solution to a technological problem. Ultimately, the Applicant’s describe improvement in the process of using shear-wave techniques, but this is not an improvement in the function of a computer or other technology (See MPEP 2106.05(a)(ii); “the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology”; See MPEP 2106.04(d)(1); 2106.05(a); and 2106.05(f)). The claims are directed to the abstract idea. Also, there does not appear to be any particular structure or machine, treatment or prophylaxis, transformation, or any other meaningful application that would render the claim eligible at step 2A, prong 2.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1 and 12 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims recite additional steps of transmitting ultrasonic waves for detecting shear waves to obtain ultrasonic echo signals, the shear waves being propagated in a region of interest. These steps represents mere data gathering, data outputting or pre/post/extra-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality. Furthermore, as discussed above, limitations with respect to the display languages/terms, respectively, amount to mere instructions to implement the abstract idea on a computer. As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than insignificant extra solution activity and mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept. The data gathering steps that were considered insignificant extra-solution activity in Step 2A Prong Two, have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field.
As an evidence, Vignon et al (US 2022/0192640 A1) discloses,
¶0038, ‘the ultrasonic imaging system 400 may include an ultrasound probe 412 includes a transducer array 414 for transmitting ultrasound signals (e.g., ultrasonic beams) toward a target region of a subject and receiving echo signals responsive to the ultrasound signals. A variety of transducer arrays are known in the art, e.g., linear arrays, convex arrays or phased arrays. ‘
For these reasons, there is no inventive concept. The claim is not patent eligible. Even when viewed as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent Claims
The following dependent claims merely further define the abstract idea and, therefore, recite an abstract idea for similar reasons:
Defining wherein the plotting of the viscosity quality control characteristic on the frequency dispersion distribution diagram to generate a frequency dispersion characteristic graph comprises at least one of: marking the effective frequency range on the frequency dispersion distribution diagram when the viscosity quality control characteristic comprises the effective frequency range, or marking the effective frequency range and a target frequency range used for calculating the viscosity parameter on the frequency dispersion distribution diagram; obtaining a fitted line of the shear wave propagation velocity versus the shear wave frequency in the frequency dispersion distribution diagram in calculation of the viscosity parameter when the viscosity quality control characteristic comprises the degree of matching, and plotting the fitted line on the frequency dispersion distribution diagram; or extracting a frequency dispersion curve for each of the shear waves in the multiple patterns and plotting an extracted dispersion curve on the frequency dispersion distribution diagram when the viscosity quality control characteristic comprises the shear waves in the multiple patterns in the frequency dispersion distribution diagram. (Claim 4)
Defining wherein displaying the viscosity quality control information about the viscosity parameter by a value characterizing the viscosity quality control characteristic comprises: when the viscosity quality control characteristic comprises the effective frequency range, displaying a value of the effective frequency range, or displaying a value of the effective frequency range and a value of a target frequency range used for calculating the viscosity parameter, or calculating and displaying an overlapping degree of the effective frequency range and a target frequency range; and/or calculating a fitted line the shear wave propagation velocity versus the shear wave frequency in the frequency dispersion distribution diagram and a degree of fit between data used for fitting when the viscosity quality control characteristic comprises the degree of matching, and displaying the degree of fit comprising an average absolute difference value, a mean square error, a root mean square error, a coefficient of determination R2 or a correlation coefficient; and/or calculating and displaying a proportion of continuous or discontinuous segments in the frequency dispersion curve when the viscosity quality control characteristic comprises the continuity of the frequency dispersion curve in the frequency dispersion distribution diagram; and/or calculating and displaying a signal-to-noise ratio of the frequency dispersion distribution diagram when the viscosity quality control characteristic comprises the signal-to-noise ratio of the frequency dispersion distribution diagram; and/or when the viscosity quality control characteristic comprises the different shear waves in multiple patterns in the frequency dispersion distribution diagram, calculating and displaying a number of the different shear waves in multiple patterns in the frequency dispersion distribution diagram, or, determining a main shear wave and calculating and displaying a degree of influence of other pattern waves on the main shear wave, wherein the degree of influence of other pattern waves on the main shear wave comprises a proportion of energy of other pattern waves or main shear wave. (claim 7) & Defining wherein a value characterizing the characteristic quantities is a proportion of continuous or discontinuous segments in the frequency dispersion curve when the viscosity quality control characteristic comprises the continuity of the frequency dispersion curve in the frequency dispersion distribution diagram.-(claim 21). Regarding the displaying information although it cannot performed in the human mind; hence, it is not part of the abstract idea. However, it is not a practical application either. It is merely an insignificant pre/post-solution activity. In addition, the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the determined information is not outputted in any way such that the practical benefit is realized.
Defining wherein displaying the viscosity quality control information about the viscosity parameter according to the viscosity quality control characteristic comprises: calculating a viscosity quality control score according to the viscosity quality control characteristic; and displaying the viscosity quality control information about the viscosity parameter by the viscosity quality control score. (claim 8). Regarding the displaying information although it cannot performed in the human mind; hence, it is not part of the abstract idea. However, it is not a practical application either. It is merely an insignificant pre/post-solution activity. In addition, the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the determined information is not outputted in any way such that the practical benefit is realized.
Defining wherein calculating a viscosity quality control score according to the viscosity quality control characteristic comprises: performing weighted summation on each value characterizing the characteristic quantities to obtain the viscosity quality control score; wherein, the value characterizing the characteristic quantities is an overlapping degree of the effective frequency range and a target frequency range when the viscosity quality control characteristic comprises the effective frequency range, or the value characterizing the characteristic quantities is a fitted line of the viscosity parameter and a degree of fit between data used for fitting when the viscosity quality control characteristic comprises the degree of matching, a degree of fit between a fitted line of the shear wave propagation velocity versus the shear wave frequency in the frequency dispersion distribution diagram and data used for fitting when the viscosity quality control characteristic comprises the degree of matching, or the value characterizing the characteristic quantities is a proportion of continuous or discontinuous segments in the frequency dispersion curve when the viscosity quality control characteristic comprises a continuity of the frequency dispersion curve in the frequency dispersion distribution diagram, or the value characterizing the characteristic quantities is a signal-to-noise ratio of the frequency dispersion distribution diagram when the viscosity quality control characteristic comprises the signal-to-noise ratio of the frequency dispersion distribution diagram; or the value characterizing the characteristic quantities is a degree of influence of other pattern waves on a main shear wave when the viscosity quality control characteristic comprises different shear waves in multiple patterns in the frequency dispersion distribution diagram, the degree of influence of other pattern waves on a main shear wave comprises a proportion of energy of other pattern waves or main shear wave. (claim 9)
Defining wherein displaying the viscosity quality control information about the viscosity parameter by the viscosity quality control score comprises: generating a viscosity quality control distribution diagram of the region of interest according to the viscosity quality control score of each point in the region of interest; and displaying the viscosity quality control distribution diagram. (claim 10) Regarding the displaying information although it cannot performed in the human mind; hence, it is not part of the abstract idea. However, it is not a practical application either. It is merely an insignificant pre/post-solution activity. In addition, the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the determined information is not outputted in any way such that the practical benefit is realized.
Defining wherein the method further comprises: displaying the viscosity parameter distribution diagram, wherein the viscosity parameter distribution diagram is generated based on the viscosity parameter of each point in the region of interest. (claim 11) Regarding the displaying information although it cannot performed in the human mind; hence, it is not part of the abstract idea. However, it is not a practical application either. It is merely an insignificant pre/post-solution activity. In addition, the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the determined information is not outputted in any way such that the practical benefit is realized.
Defining wherein performing quality control on the viscosity parameter according to the frequency dispersion distribution diagram comprises: performing quality control on the viscosity parameter by displaying the frequency dispersion distribution diagram. (claim 13) Regarding the displaying information although it cannot performed in the human mind; hence, it is not part of the abstract idea. However, it is not a practical application either. It is merely an insignificant pre/post-solution activity. In addition, the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the determined information is not outputted in any way such that the practical benefit is realized.
Defining wherein performing quality control on the viscosity parameter according to the frequency dispersion distribution diagram comprises: obtaining a viscosity quality control characteristic of the viscosity parameter according to the frequency dispersion distribution diagram; plotting the viscosity quality control characteristic on the frequency dispersion distribution diagram to generate a frequency dispersion characteristic graph; and performing quality control on the viscosity parameter by displaying the frequency dispersion characteristic graph. (claim 14) Regarding the displaying information although it cannot performed in the human mind; hence, it is not part of the abstract idea. However, it is not a practical application either. It is merely an insignificant pre/post-solution activity. In addition, the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the determined information is not outputted in any way such that the practical benefit is realized.
Defining wherein performing quality control on the viscosity parameter according to the frequency dispersion distribution diagram comprises: obtaining a viscosity quality control characteristic of the viscosity parameter according to the frequency dispersion distribution diagram; and performing quality control on the viscosity parameter by displaying a value characterizing the viscosity quality control characteristic. (claim 15). Regarding the displaying information although it cannot performed in the human mind; hence, it is not part of the abstract idea. However, it is not a practical application either. It is merely an insignificant pre/post-solution activity. In addition, the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the determined information is not outputted in any way such that the practical benefit is realized.
Defining wherein performing quality control on the viscosity parameter according to the frequency dispersion distribution diagram comprises: obtaining a viscosity quality control characteristic of the viscosity parameter according to the frequency dispersion distribution diagram; calculating a viscosity quality control score according to the viscosity quality control characteristic; and displaying viscosity quality control information about the viscosity parameter by the viscosity quality control score. Regarding the displaying information although it cannot performed in the human mind; hence, it is not part of the abstract idea. However, it is not a practical application either. It is merely an insignificant pre/post-solution activity. In addition, the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the determined information is not outputted in any way such that the practical benefit is realized.
Defining wherein the method further comprises: displaying the viscosity parameter distribution diagram, wherein the viscosity parameter distribution diagram is generated based on the viscosity parameter of each point in the region of interest. (claim 17)
Defining wherein the viscosity quality control characteristic comprises one or more of the following characteristic quantities: an effective frequency range for calculating the viscosity parameter; a degree of matching when performing model fitting for the viscosity parameter according to the frequency dispersion distribution diagram which represents the shear wave propagation velocity versus the shear wave frequency; a continuity of a frequency dispersion curve in the frequency dispersion distribution diagram; a signal-to-noise ratio of the frequency dispersion distribution diagram; and the shear waves in multiple patterns in the frequency dispersion distribution diagram. (claim 18).
Defining wherein the calculating, by the processor, the viscosity parameter according to the frequency dispersion distribution diagram comprises: calculating a slope of a frequency dispersion curve in the frequency dispersion distribution diagram with respect to a shear wave frequency and shear wave propagation velocity as the viscosity parameter, or calculating the viscosity parameter according to phase velocities of the shear waves of at least two different frequencies in the frequency dispersion distribution diagram, or calculating the viscosity parameter according to the phase velocities of the shear waves of the at least two different frequencies in the frequency dispersion distribution diagram and corresponding frequencies – (claim 20).
The following dependent claims merely further describe the extra-solution activities and therefore, do not amount to significantly more than the judicial exception or integrate the abstract idea into a practical application for similar reasons:
Describing wherein displaying viscosity quality control information about the viscosity parameter according to the viscosity quality control characteristic comprises: plotting the viscosity quality control characteristic on the frequency dispersion distribution diagram to generate a frequency dispersion characteristic graph; and displaying the frequency dispersion characteristic graph. (claim 3).
Describing performing foreground feature enhancement or background fading process on the frequency dispersion distribution diagram before plotting the viscosity quality control characteristic on the frequency dispersion distribution diagram. (claim 5)
Describing displaying the viscosity quality control information about the viscosity parameter according to the viscosity quality control characteristic comprises: displaying the viscosity quality control information about the viscosity parameter by a value characterizing the viscosity quality control characteristic (claim 6)
Regarding the displaying information although it cannot performed in the human mind; hence, it is not part of the abstract idea. However, it is not a practical application either. It is merely an insignificant pre/post-solution activity. In addition, the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the determined information is not outputted in any way such that the practical benefit is realized.
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
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, 3-4, 6, 8, 12-16, & 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Berry et al (Shear wave dispersion measures liver steatosis. Ultrasound Med Biol. 2012 Feb).
Claim 1: Barry discloses, A viscosity quality control method, applied to ultrasonic elasticity imaging, the method comprising: ([Introduction / pg. 3], ‘We hypothesize that increasing amounts of fat in the normal liver will increase the dispersion (that is, the frequency dependence or slope) of the speed and attenuation of shear waves, while slightly reducing the speed of sound. Figure 1 illustrates our hypothesis. This is simply the consequence of adding a viscous (and highly lossy) component to the liver’; [Methods / pg.5], ‘Sonoelastographic images gathered from frequencies generated between 100–400 Hz (depending on the size, stiffness and attenuation of the sample) provide the basis for assessing the dispersion of shear velocity estimates.’)
transmitting, by an ultrasonic probe, ultrasonic waves for detecting shear waves to obtain ultrasonic echo signals, the shear waves being propagated in a region of interest; ([Introduction / pg. 3], ‘In this study, we employ crawling waves (CrWs), which are an interference pattern of shear waves, in the liver. The CrWs can be imaged by a Doppler ultrasound scanner, with high signal-to-noise over a large region-of-interest (ROI)’; [Methods / pg 4], ‘An ultrasound transducer (M12L; GE Healthcare, Milwaukee, WI, USA) was connected to the ultrasound machine and was placed on top of the phantom. It is a linear array probe with band width of 5–13 MHz.’)
calculating, by a processor, ([Theory / pg. 4], ‘Using MAT-LAB computational software (The Mathworks, Inc., Natick, MA, USA)’; [Methods / pg 4], ‘A GE Logic 9 ultrasound machine (GE Healthcare, Milwaukee, WI, USA) was modified to show vibrational sonoelastographic images in the color-flow mode.’) a frequency dispersion distribution diagram according to the ultrasonic echo signals, wherein the frequency dispersion distribution diagram represents a relationship between a shear wave propagation velocity and a shear wave frequency; (FIG. 1, pg . 10, ‘A two parameter assessment of liver viscoelastic properties and their dependence on steatosis and fibrosis: (a) shear velocity (vertical axis) is measured over an accessible bandwidth and plotted as a function of frequency (horizontal axis). Regardless of the precise viscoelastic model, the first order fit yields the slope and reference intercept of the dispersion; (b) the results can be placed in a two parameter plot of dispersion slope (vertical axis) and shear velocity at a reference frequency (horizontal axis). Our hypothesis is that increasing fat content in liver will increase the slope of the dispersion measurement.’; [Methods / pg. 5], ‘From the model parameters, a wavelength value and attenuation was derived. Sonoelastographic images gathered from frequencies generated between 100–400 Hz (depending on the size, stiffness and attenuation of the sample) provide the basis for assessing the dispersion of shear velocity estimates. The useful bandwidth and the reference shear wave frequency within the band, depends on the size of and loss in the specimens. Therefore, the reference frequency for shear velocity has value only for comparisons within a similar band of measurements.’)
calculating, by the processor, a viscosity parameter and/or [emphasis added] a viscosity parameter distribution diagram according to the frequency dispersion distribution diagram, wherein the viscosity parameter comprises a slope of a frequency dispersion curve of the shear wave propagation velocity with respect to the shear wave frequency in the frequency dispersion distribution diagram, ([Introduction / pg. 3], ‘We hypothesize that increasing amounts of fat in the normal liver will increase the dispersion (that is, the frequency dependence or slope) of the speed and attenuation of shear waves,’; FIG. 1a demonstrates a first order fit yields the slope and reference intercept of the dispersion from the plot of the shear velocity versus frequency, See FIG. 1.)
a difference between phase velocities of the shear waves of at least two different frequencies in the frequency dispersion distribution diagram, ([Theory / pg. 3], ‘Δω is the frequency difference, Δk is wave number difference between the two waves,’ Additionally, calculating the slope (i.e., the rate of change) of the shear wave propagation velocity with respect to the frequency requires calculating the difference between the shear wave velocities (i.e., phase velocities) at those different frequencies to establish the first order fit, FIG. 1)
obtaining, by the processor, a viscosity quality control characteristic of the viscosity parameter according to the frequency dispersion distribution diagram; and (FIG. 1) wherein the viscosity quality control characteristic comprises one or more [emphasis added] of characteristic quantities: (FIG. 1)
an effective frequency range for calculating the viscosity parameter; (FIG. 1, pg . 10, ‘A two parameter assessment of liver viscoelastic properties and their dependence on steatosis and fibrosis: (a) shear velocity (vertical axis) is measured over an accessible bandwidth and plotted as a function of frequency (horizontal axis). Regardless of the precise viscoelastic model, the first order fit yields the slope and reference intercept of the dispersion; (b) the results can be placed in a two parameter plot of dispersion slope (vertical axis) and shear velocity at a reference frequency (horizontal axis). Our hypothesis is that increasing fat content in liver will increase the slope of the dispersion measurement.’; [Methods / pg 5], ‘The useful bandwidth and the reference shear wave frequency within the band, depends on the size of and loss in the specimens. Therefore, the reference frequency for shear velocity has value only for comparisons within a similar band of measurements.’ -identifying this useful/accessible bandwidth map constitutes to obtaining an effective frequency range for the viscosity parameter.)
a degree of matching when performing model fitting for the viscosity parameter according to the frequency dispersion distribution diagram which represents the shear wave propagation velocity versus the shear wave frequency; (FIG. 1 / 3, [Results/ pg 5], Barry teaches calculating the statistical fit/degree of matching for the dispersion slope by its derivation of the 95% confidence intervals for the linear fit to slope and reference intercept of the crawling wave data.)
a signal-to-noise ratio of the frequency dispersion distribution diagram; ([Introduction / pg. 3], ‘In this study, we employ crawling waves (CrWs), which are an interference pattern of shear waves, in the liver. The CrWs can be imaged by a Doppler ultrasound scanner, with high signal-to-noise over a large region-of-interest (ROI)’) and
the shear waves in multiple patterns in the frequency dispersion distribution diagram. (The entire and premise of the method of Barry relies on generating the crawling waves which are interfering shear wave patterns mapped from two opposing sources, [Introduction / pg. 3], ‘In this study, we employ crawling waves (CrWs), which are an interference pattern of shear waves, in the liver. The CrWs can be imaged by a Doppler ultrasound scanner, with high signal-to-noise over a large region-of-interest (ROI)’; [Theory / pg. 3], ‘Two shear wave sources are placed on the two opposite sides of a sample, driven by sinusoidal signals with slightly offset frequencies. The shear waves from the two sources interact to create interference patterns, which are visualized by the vibration sonoelastography technique. Estimations of local shear velocity can be made from the shear wave propagation pattern and, thus, the shear modulus.’)
displaying, by a display, viscosity quality control information about the viscosity parameter according to the viscosity quality control characteristic, (FIG. 1 & 3)
Claim 3: Barry discloses all the elements above in claim 1, Barry discloses: wherein the displaying of the viscosity quality control information about the viscosity parameter according to the viscosity quality control characteristic comprises: (FIG. 1 & FIG. 3)
plotting the viscosity quality control characteristic on the frequency dispersion distribution diagram to generate a frequency dispersion characteristic graph; and displaying the frequency dispersion characteristic graph. (As previously discussed FIG. 1a is a frequency dispersion distribution diagram of a plot of the shear wave velocity versus frequency. The shear velocity (vertical axis) is measured over the accessible bandwidth and plotted as a function of frequency (horizontal axis)). FIG. 1a includes plots of discrete measured points “x’ marks alongside the calculated “first order fit” (i.e., the solid red line). By plotting both the raw gathered data and the fitted line together on the same graph, there is then a display of the variance or residuals between the data and the model. Under the broadest reasonable interpretation, this visual representation of the raw data points against the linear fit is a graphical plot of the “degree matching” effectively turning FIG. 1a into the frequency dispersion characteristic graph)
Claim 4: Barry discloses all the elements above in claim 3, Barry discloses: wherein the plotting of the viscosity quality control characteristic on the frequency dispersion distribution diagram to generate a frequency dispersion characteristic graph comprises at least one of: [emphasis added] (As previously discussed FIG. 1a is a frequency dispersion distribution diagram of a plot of the shear wave velocity versus frequency. The shear velocity (vertical axis) is measured over the accessible bandwidth and plotted as a function of frequency (horizontal axis)). FIG. 1a includes plots of discrete measured points “x’ marks alongside the calculated “first order fit” (i.e., the solid red line). By plotting both the raw gathered data and the fitted line together on the same graph, there is then a display of the variance or residuals between the data and the model. Under the broadest reasonable interpretation, this visual representation of the raw data points against the linear fit is a graphical plot of the “degree matching” effectively turning FIG. 1a into the frequency dispersion characteristic graph)
obtaining a fitted line of the shear wave propagation velocity versus the shear wave frequency in the frequency dispersion distribution diagram in calculation of the viscosity parameter when the viscosity quality control characteristic comprises the degree of matching, and plotting the fitted line on the frequency dispersion distribution diagram; (FIG. 1, obtaining a first order fit to calculate the slope (i.e., dispersion, viscosity parameter) of the measured data. The data is derived from discrete shear wave frequencies between 100-400 Hz, Methods & FIG. 1. As previously discussed FIG. 1a is a frequency dispersion distribution diagram of a plot of the shear wave velocity versus frequency. The shear velocity (vertical axis) is measured over the accessible bandwidth and plotted as a function of frequency (horizontal axis)). FIG. 1a includes plots of discrete measured points “x’ marks alongside the calculated “first order fit” (i.e., the solid red line). By plotting both the raw gathered data and the fitted line together on the same graph, there is then a display of the variance or residuals between the data and the model. Under the broadest reasonable interpretation, this visual representation of the raw data points against the linear fit is a graphical plot of the “degree matching” effectively turning FIG. 1a into the frequency dispersion characteristic graph. On this diagram, the fitted line “red line” confirms that this line represented the first order fit that yields the slope and reference intercept of this dispersion.)
Claim 6: Barry discloses all the elements above in claim 1, Barry discloses: wherein the displaying of the viscosity quality control information about the viscosity parameter according to the viscosity quality control characteristic comprises: displaying the viscosity quality control information about the viscosity parameter by a value characterizing the viscosity quality control characteristic. (As previously discussed, [Introduction & Methods], the viscosity parameter calculated is represented by the dispersion (that is the frequency dependence or slope) of the speed of the attenuation of shear waves. The quality control characteristic is the statistical reliability of the degree matching of the calculated viscosity parameter, FIG. 1 / 3, [Results]. These quality control characteristics are displayed as explicitly numerical values (i.e., standard deviation, P-values, confidence internals and box plots), see Methods & Results)
Claim 8: Barry discloses all the elements above in claim 1, Barry discloses: wherein the displaying of the viscosity quality control information about the viscosity parameter according to the viscosity quality control characteristic comprises: calculating a viscosity quality control score according to the viscosity quality control characteristic; and displaying the viscosity quality control information about the viscosity parameter by the viscosity quality control score. (As previously discussed, [Introduction & Methods], the viscosity parameter calculated is represented by the dispersion (that is the frequency dependence or slope) of the speed of the attenuation of shear waves. The quality control characteristic as previously discussed refers to the statistic variance reliability or degree of matching of the calculated dispersion data, [Results], FIG 1. Under the broadest reasonable interpretation a score is a numerical value or grade that represent the quality. In the context of Barry, the modeling and data fitting, and explicitly calculated statistic values such as standard deviation, confidence intervals, p-values function as a score that quantifies the quality and reliability of the calculated dispersion parameter, FIG. 1 / 3, [Results]. See the Error bars giving the 95% confidence interval, FIG. 3. See the standard deviation displayed in FIG. 5b & 7.)
Claim 12: Barry discloses, A viscosity quality control method, applied to ultrasonic elasticity imaging, the method comprising: ([Introduction / pg. 3], ‘We hypothesize that increasing amounts of fat in the normal liver will increase the dispersion (that is, the frequency dependence or slope) of the speed and attenuation of shear waves, while slightly reducing the speed of sound. Figure 1 illustrates our hypothesis. This is simply the consequence of adding a viscous (and highly lossy) component to the liver’; [Methods / pg.5], ‘Sonoelastographic images gathered from frequencies generated between 100–400 Hz (depending on the size, stiffness and attenuation of the sample) provide the basis for assessing the dispersion of shear velocity estimates.’)
transmitting, by an ultrasonic probe, ultrasonic waves for detecting shear waves to a region of interest to obtain ultrasonic echo signals, the shear waves being propagating in the region of interest; ([Introduction / pg. 3], ‘In this study, we employ crawling waves (CrWs), which are an interference pattern of shear waves, in the liver. The CrWs can be imaged by a Doppler ultrasound scanner, with high signal-to-noise over a large region-of-interest (ROI)’; [Methods / pg 4], ‘An ultrasound transducer (M12L; GE Healthcare, Milwaukee, WI, USA) was connected to the ultrasound machine and was placed on top of the phantom. It is a linear array probe with band width of 5–13 MHz.’)
calculating, by a processor, ([Theory / pg. 4], ‘Using MAT-LAB computational software (The Mathworks, Inc., Natick, MA, USA)’; [Methods / pg 4], ‘A GE Logic 9 ultrasound machine (GE Healthcare, Milwaukee, WI, USA) was modified to show vibrational sonoelastographic images in the color-flow mode.’) a frequency dispersion distribution diagram according to the ultrasonic echo signals; (FIG. 1, pg . 10, ‘A two parameter assessment of liver viscoelastic properties and their dependence on steatosis and fibrosis: (a) shear velocity (vertical axis) is measured over an accessible bandwidth and plotted as a function of frequency (horizontal axis). Regardless of the precise viscoelastic model, the first order fit yields the slope and reference intercept of the dispersion; (b) the results can be placed in a two parameter plot of dispersion slope (vertical axis) and shear velocity at a reference frequency (horizontal axis). Our hypothesis is that increasing fat content in liver will increase the slope of the dispersion measurement.’; [Methods / pg. 5], ‘From the model parameters, a wavelength value and attenuation was derived. Sonoelastographic images gathered from frequencies generated between 100–400 Hz (depending on the size, stiffness and attenuation of the sample) provide the basis for assessing the dispersion of shear velocity estimates. The useful bandwidth and the reference shear wave frequency within the band, depends on the size of and loss in the specimens. Therefore, the reference frequency for shear velocity has value only for comparisons within a similar band of measurements.’)
calculating, by the processor, a viscosity parameter and/or a viscosity parameter distribution diagram according to the frequency dispersion distribution diagram; and ([Introduction / pg. 3], ‘We hypothesize that increasing amounts of fat in the normal liver will increase the dispersion (that is, the frequency dependence or slope) of the speed and attenuation of shear waves,’; FIG. 1a demonstrates a first order fit yields the slope and reference intercept of the dispersion from the plot of the shear velocity versus frequency, See FIG. 1.)
performing, by the processor, quality control on the viscosity parameter according to the frequency dispersion distribution diagram, (FIG. 1) wherein the calculating, by the processor, the viscosity parameter according to the frequency dispersion distribution diagram comprises: calculating a slope of a shear wave propagation velocity with respect to a shear wave frequency based on a frequency dispersion curve in the frequency dispersion distribution diagram as the viscosity parameter, ([Introduction / pg. 3], ‘We hypothesize that increasing amounts of fat in the normal liver will increase the dispersion (that is, the frequency dependence or slope) of the speed and attenuation of shear waves,’; FIG. 1a demonstrates a first order fit yields the slope and reference intercept of the dispersion from the plot of the shear velocity versus frequency, See FIG. 1.) (FIG. 1, pg . 10, ‘A two parameter assessment of liver viscoelastic properties and their dependence on steatosis and fibrosis: (a) shear velocity (vertical axis) is measured over an accessible bandwidth and plotted as a function of frequency (horizontal axis). Regardless of the precise viscoelastic model, the first order fit yields the slope and reference intercept of the dispersion; (b) the results can be placed in a two parameter plot of dispersion slope (vertical axis) and shear velocity at a reference frequency (horizontal axis). Our hypothesis is that increasing fat content in liver will increase the slope of the dispersion measurement.’; [Methods / pg. 5], ‘From the model parameters, a wavelength value and attenuation was derived. Sonoelastographic images gathered from frequencies generated between 100–400 Hz (depending on the size, stiffness and attenuation of the sample) provide the basis for assessing the dispersion of shear velocity estimates. The useful bandwidth and the reference shear wave frequency within the band, depends on the size of and loss in the specimens. Therefore, the reference frequency for shear velocity has value only for comparisons within a similar band of measurements.’) or calculating a difference between phase velocities of the shear waves of at least two different frequencies in the frequency dispersion distribution diagram as the viscosity parameter, ([Theory / pg. 3], ‘Δω is the frequency difference, Δk is wave number difference between the two waves,’ Additionally, calculating the slope (i.e., the rate of change) of the shear wave propagation velocity with respect to the frequency requires calculating the difference between the shear wave velocities (i.e., phase velocities) at those different frequencies to establish the first order fit, FIG. 1)
Claim 13: Barry discloses all the elements of claim 12, Barry discloses, wherein the performing of the quality control on the viscosity parameter according to the frequency dispersion distribution diagram comprises: performing the quality control on the viscosity parameter by displaying the frequency dispersion distribution diagram. (FIG. 1 & FIG. 3) (As previously discussed FIG. 1a is a frequency dispersion distribution diagram of a plot of the shear wave velocity versus frequency. The shear velocity (vertical axis) is measured over the accessible bandwidth and plotted as a function of frequency (horizontal axis)). FIG. 1a includes plots of discrete measured points “x’ marks alongside the calculated “first order fit” (i.e., the solid red line). By plotting both the raw gathered data and the fitted line together on the same graph, there is then a display of the variance or residuals between the data and the model. Under the broadest reasonable interpretation, this visual representation of the raw data points against the linear fit is a graphical plot of the “degree matching” effectively turning FIG. 1a into the frequency dispersion characteristic graph)
Claim 14: Barry discloses all the elements of claim 12, Barry discloses, wherein the performing of the quality control on the viscosity parameter according to the frequency dispersion distribution diagram comprises: (FIG. 1 & FIG. 3)
obtaining a viscosity quality control characteristic of the viscosity parameter according to the frequency dispersion distribution diagram; (FIG. 1 & FIG. 3)
plotting the viscosity quality control characteristic on the frequency dispersion distribution diagram to generate a frequency dispersion characteristic graph; and performing the quality control on the viscosity parameter by displaying the frequency dispersion characteristic graph. (As previously discussed FIG. 1a is a frequency dispersion distribution diagram of a plot of the shear wave velocity versus frequency. The shear velocity (vertical axis) is measured over the accessible bandwidth and plotted as a function of frequency (horizontal axis)). FIG. 1a includes plots of discrete measured points “x’ marks alongside the calculated “first order fit” (i.e., the solid red line). By plotting both the raw gathered data and the fitted line together on the same graph, there is then a display of the variance or residuals between the data and the model. Under the broadest reasonable interpretation, this visual representation of the raw data points against the linear fit is a graphical plot of the “degree matching” effectively turning FIG. 1a into the frequency dispersion characteristic graph)
Claim 15: Barry discloses all the elements of claim 12, Barry discloses, wherein the performing of the quality control on the viscosity parameter according to the frequency dispersion distribution diagram comprises: obtaining a viscosity quality control characteristic of the viscosity parameter according to the frequency dispersion distribution diagram; and performing the quality control on the viscosity parameter by displaying a value characterizing the viscosity quality control characteristic. (As previously discussed, [Introduction & Methods], the viscosity parameter calculated is represented by the dispersion (that is the frequency dependence or slope) of the speed of the attenuation of shear waves. The quality control characteristic is the statistical reliability of the degree matching of the calculated viscosity parameter, FIG. 1 / 3, [Results]. These quality control characteristics are displayed as explicitly numerical values (i.e., standard deviation, P-values, confidence internals and box plots), see Methods & Results).
Claim 16: Barry discloses all the elements of claim 12, Barry discloses, wherein the performing of the quality control on the viscosity parameter according to the frequency dispersion distribution diagram comprises: obtaining a viscosity quality control characteristic of the viscosity parameter according to the frequency dispersion distribution diagram; calculating a viscosity quality control score according to the viscosity quality control characteristic; and displaying viscosity quality control information about the viscosity parameter by the viscosity quality control score. (The viscosity parameter is the dispersion, which the slope of the shear wave velocity with respect to the frequency, FIG. 1, [Methods & Results]. As previously discussed FIG. 1a is a frequency dispersion distribution diagram of a plot of the shear wave velocity versus frequency. The shear velocity (vertical axis) is measured over the accessible bandwidth and plotted as a function of frequency (horizontal axis)). FIG. 1a includes plots of discrete measured points “x’ marks alongside the calculated “first order fit” (i.e., the solid red line). Under the broadest reasonable interpretation a score is a numerical value or grade that represent the quality. In the context of Barry, the modeling and data fitting, and explicitly calculated statistic values such as standard deviation, confidence intervals, p-values function as a score that quantifies the quality and reliability of the calculated dispersion parameter, FIG. 1 / 3, [Results]. See the Error bars giving the 95% confidence interval, FIG. 3. See the standard deviation displayed in FIG. 5b & 7.)
Claim 18: Barry discloses all the elements of claim 14, Barry discloses, wherein the viscosity quality control characteristic comprises one or more of characteristic quantities:
an effective frequency range for calculating the viscosity parameter; (FIG. 1, pg . 10, ‘A two parameter assessment of liver viscoelastic properties and their dependence on steatosis and fibrosis: (a) shear velocity (vertical axis) is measured over an accessible bandwidth and plotted as a function of frequency (horizontal axis). Regardless of the precise viscoelastic model, the first order fit yields the slope and reference intercept of the dispersion; (b) the results can be placed in a two parameter plot of dispersion slope (vertical axis) and shear velocity at a reference frequency (horizontal axis). Our hypothesis is that increasing fat content in liver will increase the slope of the dispersion measurement.’; [Methods / pg 5], ‘The useful bandwidth and the reference shear wave frequency within the band, depends on the size of and loss in the specimens. Therefore, the reference frequency for shear velocity has value only for comparisons within a similar band of measurements.’ -identifying this useful/accessible bandwidth map constitutes to obtaining an effective frequency range for the viscosity parameter.)
a degree of matching when performing model fitting for the viscosity parameter according to the frequency dispersion distribution diagram which represents the shear wave propagation velocity versus the shear wave frequency; (FIG. 1 / 3, [Results/ pg 5], Barry teaches calculating the statistical fit/degree of matching for the dispersion slope by its derivation of the 95% confidence intervals for the linear fit to slope and reference intercept of the crawling wave data.)
a continuity of the frequency dispersion curve in the frequency dispersion distribution diagram; (FIG. 1a is a frequency dispersion distribution diagram of a plot of the shear wave velocity versus frequency. The shear velocity (vertical axis) is measured over the accessible bandwidth and plotted as a function of frequency (horizontal axis)). FIG. 1a includes plots of discrete measured points “x’ marks alongside the calculated “first order fit” (i.e., the solid red line) that connects the data points thus constituting a display of continuity of the frequency dispersion curve.)
a signal-to-noise ratio of the frequency dispersion distribution diagram; and ([Introduction / pg. 3], ‘In this study, we employ crawling waves (CrWs), which are an interference pattern of shear waves, in the liver. The CrWs can be imaged by a Doppler ultrasound scanner, with high signal-to-noise over a large region-of-interest (ROI)’)
the shear waves in multiple patterns in the frequency dispersion distribution diagram. (The entire and premise of the method of Barry relies on generating the crawling waves which are interfering shear wave patterns mapped from two opposing sources, [Introduction / pg. 3], ‘In this study, we employ crawling waves (CrWs), which are an interference pattern of shear waves, in the liver. The CrWs can be imaged by a Doppler ultrasound scanner, with high signal-to-noise over a large region-of-interest (ROI)’; [Theory / pg. 3], ‘Two shear wave sources are placed on the two opposite sides of a sample, driven by sinusoidal signals with slightly offset frequencies. The shear waves from the two sources interact to create interference patterns, which are visualized by the vibration sonoelastography technique. Estimations of local shear velocity can be made from the shear wave propagation pattern and, thus, the shear modulus.’)
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 7 rejected under 35 U.S.C. 103 as being unpatentable over Berry et al (Shear wave dispersion measures liver steatosis. Ultrasound Med Biol. 2012 Feb), as applied to claim 6, in further view of Bhatt et al ("Reconstruction of Viscosity Maps in Ultrasound Shear Wave Elastography," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 66, no. 6, pp. 1065-1078, June 2019).
Claim 7: Barry discloses all the elements above in claim 6, Barry discloses: wherein the displaying of the viscosity quality control information about the viscosity parameter by a value characterizing the viscosity quality control characteristic comprises at least one of: (As previously discussed FIG. 1a is a frequency dispersion distribution diagram of a plot of the shear wave velocity versus frequency. The shear velocity (vertical axis) is measured over the accessible bandwidth and plotted as a function of frequency (horizontal axis)). FIG. 1a includes plots of discrete measured points “x’ marks alongside the calculated “first order fit” (i.e., the solid red line). By plotting both the raw gathered data and the fitted line together on the same graph, there is then a display of the variance or residuals between the data and the model. Under the broadest reasonable interpretation, this visual representation of the raw data points against the linear fit is a graphical plot of the “degree matching” effectively turning FIG. 1a into the frequency dispersion characteristic graph)
calculating a fitted line of the shear wave propagation velocity versus the shear wave frequency in the frequency dispersion distribution diagram and a degree of fit between data used for fitting when the viscosity quality control characteristic comprises the degree of matching, (FIG. 1, obtaining a first order fit to calculate the slope (i.e., dispersion, viscosity parameter) of the measured data. The data is derived from discrete shear wave frequencies between 100-400 Hz, Methods & FIG. 1. As previously discussed FIG. 1a is a frequency dispersion distribution diagram of a plot of the shear wave velocity versus frequency. The shear velocity (vertical axis) is measured over the accessible bandwidth and plotted as a function of frequency (horizontal axis)). FIG. 1a includes plots of discrete measured points “x’ marks alongside the calculated “first order fit” (i.e., the solid red line). By plotting both the raw gathered data and the fitted line together on the same graph, there is then a display of the variance or residuals between the data and the model. Under the broadest reasonable interpretation, this visual representation of the raw data points against the linear fit is a graphical plot of the “degree matching” effectively turning FIG. 1a into the frequency dispersion characteristic graph. On this diagram, the fitted line “red line” confirms that this line represented the first order fit that yields the slope and reference intercept of this dispersion.)
Barry fails to disclose: and displaying the degree of fit comprising an average absolute difference value, a mean square error, a root mean square error, a coefficient of determination R2 or a correlation coefficient;
However, Bhatt in the context of reconstruction of viscosity maps in ultrasound shear wave elastography discloses: displaying the degree of fit comprising an average absolute difference value, a mean square error, a root mean square error, a coefficient of determination R2 or a correlation coefficient;
-Bhatt discloses, using a coefficient of determination, referred to as the R2-stastic wave, [pg. 1069/Evaluation of Goodness Fit], to evaluate the “quality of fit” or degree of matching of the proposed model to the experimental data. Bhatt also uses F-statistic scores to assess the statistical confidence of this match, [pg. 1069/Evaluation of Goodness Fit]. The degree of matching is calculated for the fit of the Gamma distribution model to the amplitude spectrum (i.e., frequency distribution) of the propagating shear waves, [pg. 1067/ 2) Frequency-Shift Method]. “The closer the R2 -statistic is to 1, the better is the model fit to the SW amplitude spectrum data.”, [pg. 1069/Evaluation of Goodness Fit]. This model fitting is part of the frequency-shift method that estimates the shear wave attenuation. This attenuation is then combined with phase velocity to calculate the viscosity, [pg. 1067/ 2) Frequency-Shift Method]. The model fit matches the experimental frequency spectrum between 92% and 98% at 9 out of 10 measured points, [pg. 1071/Results from Statistical Analyses/ 1) Validation of the Proposed Model Fit], FIG. 8.
-Bhatt teaches in the process of obtaining a fitted model, evaluating its quality via the degree of matching, and plotting this fit against the frequency dispersion. Specifically, by obtaining a fitting line where the “frequency spectrum” of the shear wave is modeled using the Gamma distribution, [pg. 1067/ 2) Frequency-Shift Method]. To calculate the viscosity via the attenuation coefficient (α), the method analyses how the spectrum changes over distance. Specifically, that a parameter of the model is “fitted to a straight line” over the distance, where the slop of the this line yields the attenuation coefficient used to compute viscosity, [pg. 1067/ 2) Frequency-Shift Method – 3) Estimation of the Loss Modulus].
-Bhatt, FIG. 8, presents the plots of the fitted model overlaid on the experimental data (i.e., the frequency dispersion). FIG. 8 is referred to as the amplitude spectrum. This plot visually demonstrates the degree of matching, between the model and the actual frequency distribution of the shear waves, the basis for the viscosity calculation.
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the display of the viscosity quality control characteristic of modified Barry to include the teachings of Bhatt. The motivation to do this yield predictable results such as to validate the accuracy and reliability of the mathematical model used to reconstruct the viscosity, as suggested by Bhatt [pg. 1069/Evaluation of Goodness Fit].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Berry et al (Shear wave dispersion measures liver steatosis. Ultrasound Med Biol. 2012 Feb), as applied to claim 8, in further view of Bhatt et al ("Reconstruction of Viscosity Maps in Ultrasound Shear Wave Elastography," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 66, no. 6, pp. 1065-1078, June 2019) in view of Samsung Medison Co. Ltd. ("S-Shearwave™ Elastography Liver Evaluation: Recommended Values" White Paper, 2017).
Claim 9: Barry discloses all the elements above in claim 8, Barry discloses:
wherein the calculating of the viscosity quality control score according to the viscosity quality control characteristic comprises: (As previously discussed, [Introduction & Methods], the viscosity parameter calculated is represented by the dispersion (that is the frequency dependence or slope) of the speed of the attenuation of shear waves. The quality control characteristic as previously discussed refers to the statistic variance reliability or degree of matching of the calculated dispersion data, [Results], FIG 1. Under the broadest reasonable interpretation a score is a numerical value or grade that represent the quality. In the context of Barry, the modeling and data fitting, and explicitly calculated statistic values such as standard deviation, confidence intervals, p-values function as a score that quantifies the quality and reliability of the calculated dispersion parameter, FIG. 1 / 3, [Results]. See the Error bars giving the 95% confidence interval, FIG. 3. See the standard deviation displayed in FIG. 5b & 7.)
Barry fails to disclose:
performing a mathematical modeling on at least one value characterizing the characteristic quantities to obtain the viscosity quality control score;
wherein, the at least one value characterizing the characteristic quantities comprises at least one of:
a degree of fit between a fitted line of the shear wave propagation velocity versus the shear wave frequency in the frequency dispersion distribution diagram and data used for fitting when the viscosity quality control characteristic comprises the degree of matching,
However, Bhatt in the context of reconstruction of viscosity maps in ultrasound shear wave elastography discloses performing a mathematical model on at least one value characterizing the characteristic quantities to obtain the viscosity quality control score; wherein, the at least one value characterizing the characteristic quantities comprises at least one of: a degree of fit between a fitted line of the shear wave propagation velocity versus the shear wave frequency in the frequency dispersion distribution diagram and data used for fitting when the viscosity quality control characteristic comprises the degree of matching,
-Bhatt discloses, using a coefficient of determination, referred to as the R2-stastic wave, [pg. 1069/Evaluation of Goodness Fit], to evaluate the “quality of fit” or degree of matching of the proposed model to the experimental data. Bhatt also uses F-statistic scores to assess the statistical confidence of this match, [pg. 1069/Evaluation of Goodness Fit]. The degree of matching is calculated for the fit of the Gamma distribution model to the amplitude spectrum (i.e., frequency distribution) of the propagating shear waves, [pg. 1067/ 2) Frequency-Shift Method]. “The closer the R2 -statistic is to 1, the better is the model fit to the SW amplitude spectrum data.”, [pg. 1069/Evaluation of Goodness Fit]. This model fitting is part of the frequency-shift method that estimates the shear wave attenuation. This attenuation is then combined with phase velocity to calculate the viscosity, [pg. 1067/ 2) Frequency-Shift Method]. The model fit matches the experimental frequency spectrum between 92% and 98% at 9 out of 10 measured points, [pg. 1071/Results from Statistical Analyses/ 1) Validation of the Proposed Model Fit], FIG. 8.
-Bhatt teaches in the process of obtaining a fitted model, evaluating its quality via the degree of matching, and plotting this fit against the frequency dispersion. Specifically, by obtaining a fitting line where the “frequency spectrum” of the shear wave is modeled using the Gamma distribution, [pg. 1067/ 2) Frequency-Shift Method]. To calculate the viscosity via the attenuation coefficient (α), the method analyses how the spectrum changes over distance. Specifically, that a parameter of the model is “fitted to a straight line” over the distance, where the slop of the this line yields the attenuation coefficient used to compute viscosity, [pg. 1067/ 2) Frequency-Shift Method – 3) Estimation of the Loss Modulus].
-Bhatt, FIG. 8, presents the plots of the fitted model overlaid on the experimental data (i.e., the frequency dispersion). FIG. 8 is referred to as the amplitude spectrum. This plot visually demonstrates the degree of matching, between the model and the actual frequency distribution of the shear waves, the basis for the viscosity calculation.
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the calculation of the viscosity quality control score of Barry to include the teachings of Bhatt. The motivation to do this yield predictable results such as to validate the accuracy and reliability of the mathematical model used to reconstruct the viscosity, as suggested by Bhatt [pg. 1069/Evaluation of Goodness Fit].
Barry as modified fails to disclose that the mathematical model fitting is a weighted summation
However, Samsung Medison Co. Ltd in the context of elastography liver evaluation discloses: a weighted summation ([Methodology, pg 3], ‘The average of the measurements is used to estimate the degree of liver stiffness (Fig. 3). Additionally, the Reliability Measurement Index (RMI) and Variation Range (VR) are provided in the S-Shearwave Profile. The RMI (reliability of the measurement) is a quality control parameter that is calculated by the weighted sum of two factors: the residual of the wave equation, and the magnitude of the shearwave. Therefore, high RMI values are strongly correlated with reproducible measurements. An RMI of 0.0 would indicate significant error, whereas an RMI of 1.0 would indicate no error). While in the S-Shearwave Profile display, the user can easily deselect any unreliable measurements depending on its RMI.’)-Thus Samsung Medison Co. Ltd teaches the required limitation of weighted summation.
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modifying the mathematical model fitting of modified Barry to further including a weighting summation for quality control as taught by Samsung Medison Co. Ltd for the advantage of providing an improved method with such a method being able to providing reproducible and reliable measurements, as suggested by Samsung Medison Co. Ltd, [Methodology, pg 3].
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Berry et al (Shear wave dispersion measures liver steatosis. Ultrasound Med Biol. 2012 Feb), as applied to claim 8, in further view of Yoshikawa (US 2017/0333004 A1).
Claim 10: Barry discloses all the elements above in claim 8, Barry fails to disclose: wherein the displaying of the viscosity quality control information about the viscosity parameter by the viscosity quality control score comprises: generating a viscosity quality control distribution diagram of the region of interest according to the viscosity quality control score of each point in the region of interest; and displaying the viscosity quality control distribution diagram.
However, Yoshikawa in the context of ultrasound elasticity evaluation methods discloses, wherein the displaying of the viscosity quality control information about the viscosity parameter by the viscosity quality control score comprises: generating a viscosity quality control distribution diagram of the region of interest according to the viscosity quality control score of each point in the region of interest; and displaying the viscosity quality control distribution diagram. (FIG. 8B8C, an quality control distribution at least according to the viscosity parameter, “deriving an elasticity evaluation index of the test object by using the variation of a plurality of obtained velocity measurement results.” [See Claim 14 of Yoshikawa]; Also see Fig 8B of Yoshikawa above.).
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the viscosity quality control information of modified Barry to comprise teachings as taught by Yoishikawa. The motivation to do this yields predictable results such as providing high degrees of accuracy and reproducibility by accounting for variation of an acoustic characteristic, ¶0014 of Yoshikawa. The modified combination would disclose wherein displaying the viscosity quality control information about the viscosity parameter by the viscosity quality control score comprises: generating a viscosity quality control distribution diagram of the region of interest according to the viscosity quality control score of each point in the region of interest; and displaying the viscosity quality control distribution diagram.
Claim 5, 11, 17, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Berry et al (Shear wave dispersion measures liver steatosis. Ultrasound Med Biol. 2012 Feb), as applied to claim 1, 3, & 12 & 18 respectively, in further view of Mischi et al (US 2020/0121288 A1).
Claim 5: Barry discloses all the elements above in claim 3, Barry fails to disclose: further comprising: performing foreground feature enhancement or background fading process on the frequency dispersion distribution diagram before the plotting of the viscosity quality control characteristic on the frequency dispersion distribution diagram.
However, Mischi in the context of shear wave viscoelasticity imaging discloses: further comprising: performing foreground feature enhancement or background fading process on the frequency dispersion distribution diagram before the plotting of the viscosity quality control characteristic on the frequency dispersion distribution diagram.
Mischi teaches simulated datasets used to generate the results in FIG. 4A are processed according to Sections II-A to II-D. Within the II-B. Pre-Processing section, it is stated that: “the axial velocity maps were spatially filtered using a 2D Gaussian kernel with a standard deviation of 1.2 samples in both the axial and lateral direction.”,¶0041-Pre-Processing. This spatial filtering with a Gaussian kernal serves as a noise suppression technique smoothing the axial velocity maps before they are used for the results shown in FIG. 4A. Reducing random noise and smoothing out irregularities is enhances features by making them more discernible.
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method of Barry such that it further comprises: performing foreground feature enhancement or background fading process on the frequency dispersion distribution diagram before the plotting of the viscosity quality control characteristic on the frequency dispersion distribution diagram as taught by Mischi for the advantage of providing an improved method for assisting clinicians in medical diagnosis, for detecting, localizing, and characterizing abnormalities in tissue, such as canner or liver diseases, as suggested by Mischi, ¶0077, ¶0092.
Claim 11: Barry discloses all the elements above in claim 1, Barry discloses: wherein the method further comprises: ([Introduction / pg. 3], ‘We hypothesize that increasing amounts of fat in the normal liver will increase the dispersion (that is, the frequency dependence or slope) of the speed and attenuation of shear waves,’; FIG. 1a demonstrates a first order fit yields the slope and reference intercept of the dispersion from the plot of the shear velocity versus frequency, See FIG. 1.)
Barry fails to disclose: displaying the viscosity parameter distribution diagram, wherein the viscosity parameter distribution diagram is generated based on the viscosity parameter of each point in the region of interest.
However, Mischi in the context of shear wave viscoelasticity imaging discloses: displaying the viscosity parameter distribution diagram, wherein the viscosity parameter distribution diagram is generated based on the viscosity parameter of each point in the region of interest. (FIG 6E-6H), ¶0033, ¶0074, ¶0077, ¶0087-0088).
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method of Barry to incorporate the teachings of Mischi for the advantage of providing an improved method for assisting clinicians in medical diagnosis, for detecting, localizing, and characterizing abnormalities in tissue, such as canner or liver diseases, as suggested by Mischi, ¶0077, ¶0092.
Claim 17: Barry discloses all the elements above in claim 12, Barry discloses: further comprising: displaying the viscosity parameter distribution diagram, wherein the viscosity parameter distribution diagram is generated based on the viscosity parameter of each point in the region of interest. (FIG 6E-6H), ¶0033, ¶0074, ¶0077, ¶0087-0088).
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method of Barry to incorporate the teachings of Mischi for the advantage of providing an improved method for assisting clinicians in medical diagnosis, for detecting, localizing, and characterizing abnormalities in tissue, such as canner or liver diseases, as suggested by Mischi, ¶0077, ¶0092.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Berry et al (Shear wave dispersion measures liver steatosis. Ultrasound Med Biol. 2012 Feb), as applied to claim 18, in further view of
Claim 20: Barry discloses all the elements of claim 18, Barry fails to disclose: wherein a value characterizing the characteristic quantities is a proportion of continuous or discontinuous segments in the frequency dispersion curve when the viscosity quality control characteristic comprises the continuity of the frequency dispersion curve in the frequency dispersion distribution diagram.
However, Mischi in the context of shear wave viscoelasticity imaging discloses: wherein a value characterizing the characteristic quantities is a proportion of continuous or discontinuous segments in the frequency dispersion curve when the viscosity quality control characteristic comprises the continuity of the frequency dispersion curve in the frequency dispersion distribution diagram.
-Mitchi discloses that Fig. 4A is a mathematical framework that demonstrates a continuity of the frequency dispersion curve. These graphs show smooth and continuous lines for both the ‘True’ phase velocities and the ‘estimate’ phase velocities across the depicted frequency range. The visual overlap of these smooth lines signifies a continuous and accurate representation of the dispersion.
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method of Barry to incorporate the teachings of Mischi for the advantage of providing an improved method for assisting clinicians in medical diagnosis, for detecting, localizing, and characterizing abnormalities in tissue, such as canner or liver diseases, as suggested by Mischi, ¶0077, ¶0092.
Conclusion
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/N.A.R./
Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798
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