DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s response to the Non-Final Office Action dated 08/14/2025, filed with the office on 11/14/2025, has been entered and made of record.
Status of Claims
Claims 1-2 and 4-11 are pending. Claim 3 is cancelled.
Response to Amendments
In light of Applicant’s amendments of the specification, the objections of record with respect to drawings is withdrawn. Additionally, the amendments to the specification have been considered and placed in the file wrapper.
Claim Objections
Claim 1 is objected to because of the following informality:
Claim 1 recites “…current value on the basis of based on a trained…” should recite “…current value based on a trained…”
Appropriate corrections are required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpretated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are, “scanning module configured to perform…” “user interface module configured to select …” “a trained machine learning system (32; 210, 225) having a configuration determined…”, and “control module configured to obtain…” in claim 11.
Because of these claim limitations being interpretated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Response to Arguments
Applicant’s amendments of independent claims 1, 9, and 11, which has altered the scope of the claims of the instant application, has necessitated the new ground(s) of rejection presented in this office action with respect to claims of the instant application. Accordingly, with respect to Applicant’s arguments that are merely directed to the amended portion of the claims, new analyses have been presented below, which make Applicant’s arguments moot.
Consequently, THIS ACTION IS MADE FINAL.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-2, and 5-7 and 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Xia et al. (US 2022/0130520 A1) in view of Fan et al. (US 2017/0209105 A1), in view of Rupcich et al. (US 2019/0099148 A1), in further view of Malkus (“A method to extract image noise level from patient images in CT” – Published 2017).
Regarding claim 1, Xia teaches “A method for obtaining a tube current value, comprising ("Xia paragraph [0006] "The present disclosure relates to a system, apparatus, method, and non-transitory computer-readable storage medium for patient-specific imaging protocol optimization"):
obtaining a scanning protocol including scan parameters and image reconstruction parameters (Xia paragraph [0048] "initial set of scan acquisition parameters and image reconstruction parameters");
performing a scout scan to obtain a scout image of a subject under examination (Xia paragraph [0047] "scout scan data of a patient can be acquired from a CT scanner at step 106 of method 100. The scout scan data may be data acquired by a 2D scout scan or a 3D scout scan");
and
obtaining a tube current value (Xia paragraph [0041] "imaging protocol optimization that is body part-, task-, disease-, and otherwise patient-specific while maximizing image quality for diagnosis and minimizing radiation exposure to a patient") on the basis of based on a trained machine learning model (Xia paragraph [0041] "The approach is a machine learning-based approach that utilizes neural networks trained according to clinician evaluations of medical images"), wherein the model receives as input according to the scout image (Xia paragraph [0083] "During training, the CNN receives training data, or, for instance, a scout scan, as an input"), the extracted features, the scanning protocol (Xia paragraph [0042] "from acquired scout scan information and scout scan conditions" and paragraph [0061] "tube current and X-ray beam energy may be modified according to a noise map of the patient generated from the acquired scout scan data"), and , and wherein the machine learning model is trained using a clinical dataset including scout images (Xia paragraph [0083] "During training, the CNN receives training data, or, for instance, a scout scan, as an input"), scanning protocols, (Xia paragraph [0060] "the scan acquisition parameters include, but are not limited to, pitch, rotation speed. X-ray beam energy (i.e., tube voltage), tube current, collimation thickness, calibrated field of view, a bowtie filter, sampling frequency, and whether photon-counting is used").”
However, Xia does not explicitly teach “extracting one or more features from the scout image, the features including at least one of total attenuation, peak attenuation, water-equivalent diameter, and tissue composition metrics” and “a preset image noise parameter including a global noise index representative of desired image quality”.
Fan teaches “extracting one or more features from the scout image (Fan paragraph [0046] "The optimized theoretical scan parameters and theoretical image reconstruction parameters may be based on a variety of patient-specific factors, including patient size, as may be determined from the acquired scout scan"), the features including at least one of (Fan paragraph [0037] "the method may automatically determine the patient size, for example, by performing a scout scan of the
patient, and calculating the patient size based on projection data acquired during the scout scan. For example, patient size may be expressed in terms of a water-equivalent diameter Dw, which may be calculated based on CT numbers of projection data from the scout scan"), and ” and
“preset image noise parameter (Fan paragraph [0047] "parameter describing noise level ( e.g., image pixel standard deviation) is often used as the image quality index in automatic tube current modulation. In contrast, the image quality index utilized in method 300 is based on a plurality of factors Qtask which may impact the image quality") .
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine an
scout scan, and scanning protocol to get a tube current value using machine learning as taught by Xia to include an image noise parameter as taught by Fan.
The suggestion/motivation for doing so would have been “individual radiologists may have different preferences regarding the appearance of an image. For example, some radiologists may prefer smooth images, and therefore may typically use a large dose to obtain a smoother image with a better signal-to-noise ratio; meanwhile, other radiologists may prefer sharper images, and so may typically use a different dose and reconstruction method to obtain a sharper image. Such preferences may be captured through user feedback, and training the optimization model, as described herein” as noted by the Fan disclosure in paragraph 58.
However, the combination of Xia and Fan does not explicitly teach features extracted from a scout image “including at least one of total attenuation, peak attenuation […”, and tissue composition metrics”.
Rupcich teaches features extracted from a scout image “including at least one of total attenuation, peak attenuation […”, and tissue composition metrics (Rupcich paragraph [0049] "Scout scans provide information for determining a density,
a size, and a shape of the person. For example, total projection attenuation may include information relating to a density and a size of the person, and an amplitude and width of the projection from the scout scan may include information relating to a shape of the person)”.
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention of the instant application to combine an scout scan, scanning protocol, and image noise parameter, to get a tube current value using machine learning as taught by Xia and Fan to include features such as attenuation and tissue composition, such as density information as taught by Rupcich.
The suggestion/motivation for doing so would have been "Image quality, however, can be a function of several factors, including noise, contrast, spatial resolution, and/or noise texture. Each of these factors can be influenced by one or more adjustable parameters of the CT imaging system. To assist the operator in achieving sufficient image quality, manufacturers have developed several features, such as automated control systems that automatically adjust operation of the imaging system" as noted by the Rupcich paragraph 4.
However, the combination of Xia, Fan, and Rupcich does explicitly teach the noise parameter “including a global noise index representative of desired image quality”.
Malkus teaches the noise parameter “including a global noise index representative of desired image quality (Malkus page 3 right hand column paragraph 2 "From a collection of standard deviation values from an image slice, which we define as Ω, the noise level can be characterized using the mode of that distribution which we denoted as the traditional global noise metric tradGNI and the mean which we denote as meanGNI. These metrics will either be applied to ROIs representing water or air. The combination of the two metric types and ROI locations results in four combinations of noise metrics. The meanGNI and tradGNI metrics should produce the same results given a normal distribution of image noise across all ROI locations").”
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention of the instant application to combine an scout scan, scanning protocol, and image noise parameter, to get a tube current value using machine learning as taught by Xia, Fan, and Rupcich to include a global noise index as taught by Malkus.
The suggestion/motivation for doing so would have been "Several groups have devised automated tools for measuring image noise from CT scans of patients. These types of image quality assessment are timely given the current trend of nonlinear image reconstruction algorithms and the increased pressure to reduce patient doses in CT" as noted by the Malkus disclosure in page 1 left hand column paragraph 1.
Therefore, it would have been obvious to combine the disclosure of Xia, Fan, and Rupcich with the Malkus disclosure to obtain the invention as specified in claim 1 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Consider claim 2, the combination of Xia, Fan, Rupcich and Malkus teaches “The method of claim 1, wherein the scan parameter includes at least one of a scan field of view, a diagnostic purpose, a ray bowtie filter, a collimation width, an exposure voltage, a rack rotation speed, a slice thickness and a helical pitch (Xia paragraph [0060] "the scan acquisition parameters include, but are not limited to, pitch, rotation speed. X-ray beam energy (i.e., tube voltage), tube current, collimation thickness, calibrated field of view, a bowtie filter, sampling frequency, and whether photon-counting is used"), and the image reconstruction parameter includes at least one of a convolution kernel size for reconstruction, a display field of view, and an image post-processing parameter (Xia paragraph [0069] "The image reconstruction parameters may include, among others, reconstruction method, reconstruction kernel, noise reduction filter, slice thickness, and a system matrix that simulates the scanning process").”
Consider claim 5, the combination of Xia, Fan, Rupcich and Malkus teaches The method of claim 1, wherein the machine learning model is trained by means of a clinical data set, the clinical data set comprising clinical data of a plurality of subjects under examination (Xia paragraph [0041] "The approach is a machine learning-based approach that utilizes neural networks trained according to clinician evaluations of medical images"), and each piece of clinical data comprising a scout image (Xia paragraph [0046] "The artificial neural network may be selected as one of a variety of artificial neural networks trained on a subset of images or image datasets that may be representative of specific disease types, patient demographics, regions of interest, and the like"), a scanning protocol, an image noise parameter of a medical image, and a tube current value of actual scanning (Fan paragraph [0063] "The user feedback regarding the image and all of the relevant parameters used to generate the image (including but not limited to the patient-specific inputs, the system specific inputs, the clinical task selection, the image quality selection, the optimized dose level, the optimized scan protocol, and so on) may be used as input features for the neural network").”
The proposed combination as well as the motivation for combining Xia, Fan, Rupcich and Malkus references presented in the rejection of claim 1, applies to claim 5. Finally the method recited in claim 5 is met by Xia, Fan, Rupcich and Malkus.
Consider claim 6, the combination of Xia, Fan, Rupcich and Malkus teaches “The method of claim 5, wherein the training comprises: obtaining a clinical scout image (Xia paragraph [0046] "a subset of images or image datasets that may be representative of specific disease types, patient demographics, regions of interest, and the like"), a scanning protocol, an image noise parameter, and a tube current value of actual scanning of each subject under examination (Fan paragraph [0063] "The user feedback regarding the image and all of the relevant parameters used to generate the image (including but not limited to the patient-specific inputs, the system specific inputs, the clinical task selection, the image quality selection, the optimized dose level, the optimized scan protocol, and so on) may be used as input features for the neural network"); and
performing training using the clinical scout images (Xia paragraph [0046] "The artificial neural network may be selected as one of a variety of artificial neural networks trained on a subset of images or image datasets that may be representative of specific disease types, patient demographics, regions of interest, and the like"), scanning protocols and image noise parameters of medical images as an input and using the tube current values as an output, so as to obtain the machine learning model (Fan paragraph [0063] "The user feedback regarding the image and all of the relevant parameters used to generate the image (including but not limited to the patient-specific inputs, the system specific inputs, the clinical task selection, the image quality selection, the optimized dose level, the optimized scan protocol, and so on) may be used as input features for the neural network").”
The proposed combination as well as the motivation for combining Xia, Fan, Rupcich and Malkus references presented in the rejection of claim 1, applies to claim 6. Finally the method recited in claim 6 is met by Xia, Fan, Rupcich and Malkus.
Consider claim 7, the combination of Xia, Fan, Rupcich and Malkus teaches “The method of claim 1, further comprising: extracting a feature from the scout image, and inputting the extracted feature into the machine learning model to obtain the tube current value (Fan paragraph [0046] "The optimized theoretical scan parameters and theoretical image reconstruction parameters may be based on a variety of patient-specific factors, including patient size, as may be determined from the acquired scout scan").”
The proposed combination as well as the motivation for combining Xia, Fan, Rupcich and Malkus references presented in the rejection of claim 1, applies to claim 7. Finally the method recited in claim 7 is met by Xia, Fan, Rupcich and Malkus.
Regarding claim 9, the combination of Xia, Fan, Rupcich and Malkus teaches “A CT scanning method, comprising: determining a scanning protocol including a scan parameter and an image reconstruction parameter (Xia paragraph [0048] "initial set of scan acquisition parameters and image reconstruction parameters");
performing a scout scan to obtain a scout image of a subject under examination
(Xia paragraph [0047] "scout scan data of a patient can be acquired from a CT scanner at step 106 of method 100. The scout scan data may be data acquired by a 2D scout scan or a 3D scout scan");
extracting one or more features from the scout image (Fan paragraph [0046] "The optimized theoretical scan parameters and theoretical image reconstruction parameters may be based on a variety of patient-specific factors, including patient size, as may be determined from the acquired scout scan"), the features including at least one of total attenuation, peak attenuation, water-equivalent diameter
(Fan paragraph [0037] "the method may automatically determine the patient size, for example, by performing a scout scan of the patient, and calculating the patient size based on projection data acquired during the scout scan. For example, patient size may be expressed in terms of a water-equivalent diameter Dw, which may be calculated based on CT numbers of projection data from the scout scan")
, and tissue composition metrics (Rupcich paragraph [0049] "Scout scans provide information for determining a density, a size, and a shape of the person. For example, total projection attenuation may include information relating to a density and a size of the person, and an amplitude and width of the projection from the scout scan may include information relating to a shape of the person”);
obtaining a tube current value on the basis of a trained machine learning model (Xia paragraph [0041] "The approach is a machine learning-based approach that utilizes neural networks trained according to clinician evaluations of medical images"), wherein the model receives as input according to the scout image (Xia paragraph [0083] "During training, the CNN receives training data, or, for instance, a scout scan, as an input"), the extracted features, the scanning protocol (Xia paragraph [0042] "from acquired scout scan information and scout scan conditions" and paragraph [0061] "tube current and X-ray beam energy may be modified according to a noise map of the patient generated from the acquired scout scan data") (Xia paragraph [0042] "from acquired scout scan information and scout scan conditions." and paragraph [0061] "tube current and X-ray beam energy may be modified according to a noise map of the patient generated from the acquired scout scan data"), and a preset image noise parameter (Fan paragraph [0047] "parameter describing noise level ( e.g., image pixel standard deviation) is often used as the image quality index in automatic tube current modulation. In contrast, the image quality index utilized in method 300 is based on a plurality of factors Qtask which may impact the image quality") including a global noise index representative of desired image quality, and wherein the machine learning model is trained using a clinical dataset including scout images (Xia paragraph [0083] "During training, the CNN receives training data, or, for instance, a scout scan, as an input"), scanning protocols, image noise parameters, and corresponding tube current values from clinical data (Xia paragraph [0060] "the scan acquisition parameters include, but are not limited to, pitch, rotation speed. X-ray beam energy (i.e., tube voltage), tube current, collimation thickness, calibrated field of view, a bowtie filter, sampling frequency, and whether photon-counting is used");
updating the scanning protocol based on the obtained tube current value (Xia paragraph [0044] "The optimized protocol, based on the acquired scout scan information and scout scan conditions, can be implemented within a full CT scan of the patient" - for clarity the optimized protocol includes the tube current value per Xia paragraph [0061] "tube current and X-ray beam energy may be modified according to a noise map of the patient generated from the acquired scout scan data"); and
performing a CT scan on the basis of the updated scanning protocol, to obtain a medical image of the subject under examination (Xia paragraph [0044] "The optimized protocol, based on the acquired scout scan information and scout scan conditions, can be implemented within a full CT scan of the patient").”
The proposed combination as well as the motivation for combining Xia, Fan, Rupcich and Malkus references presented in the rejection of claim 1, applies to claim 9. Finally the method recited in claim 9 is met by Xia, Fan, Rupcich and Malkus.
Regarding claim 10, the combination of Xia, Fan, Rupcich and Malkus teaches “A medical imaging system, comprising a processor (Xia paragraph [0141] " by processing circuitry"), the processor being configured to perform the method for obtaining a tube current value of any one of claims 1.”
The limitations of claim 10 are similar in scope to the limitations of claim 1 therefore the motivation for combining Xia, Fan, Rupcich and Malkus references presented in the rejection of claim 1, applies to claim 10. Finally the system recited in claim 10 is met by Xia, Fan, Rupcich and Malkus.
Claim 11 recites a system with functional tasks corresponding to the steps
recited in method claim 1. Therefore, the recited tasks of this claim are mapped to the proposed combination in the same manner as the corresponding steps of method
claim 1. Additionally, the rationale and motivation to combine the Xia, Fan, Rupcich and Malkus references, presented in rejection of claim 1 apply to this claim. Finally, the combination of Xia and Fan discloses a scanning module (Xia, Paragraph [0047] “scout scan data of a patient can be acquired from a CT scanner at step 106 of method 100”), a user interface module, and a control module (Xia, Paragraph [0129] “The memory 1162 is connected to a system controller 1160 through a data/control bus 1161, together with a reconstruction device 1164, input device 1165, and display 1166. The system controller 1160 controls a current regulator 1163 that limits the current to a level sufficient for driving the CT system”).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Xia, Fan, Rupcich and Malkus, in view of Nasteski (“An overview of the supervised machine learning methods” – Published 2017).
The combination of Xia, Fan, Rupcich and Malkus teaches the method of claim 1. The combination of Xia, Fan, Rupcich and Malkus does not teach “wherein the machine learning model comprises a linear regression mode”.
However, Nasteski teaches wherein the machine learning model comprises a linear regression model (Nasteski page 8 paragraph 2 "Linear regression [11] also belongs to the category of supervised learning algorithms. It means we train the model on a set of labeled data (training data) and then use the model to predict labels on unlabeled data").
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention of the instant application to combine an scout scan, scanning protocol, and image noise parameter, to get a tube current value using machine learning as taught by Xia, Fan, Rupcich and Malkus to a linear regression model as taught by Nasteski.
The suggestion/motivation for doing so would have been " the model (red line) is calculated using training data (blue points) where each point has a known label (𝑦 axis) to fit the points as accurately as possible by minimizing the value of a chosen loss function. We can then use the model to predict unknown labels (we only know 𝑥 value and want to predict 𝑦 value)” as noted by the Nasteski disclosure in page 8 paragraph 3.
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Therefore, it would have been obvious to combine the disclosure of Xia, Fan, Rupcich and Malkus with the Nasteski disclosure to obtain the invention as specified in claim 4 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Xia, Fan, Rupcich and Malkus, in view of Hammer (“X-Ray Physics: X-Ray Interaction with Matter, X-Ray Contrast, and Dose” – Published 2014).
The combination of Xia, Fan, Rupcich and Malkus teaches “The method of claim 7, wherein the extracted features further includes at least one of and a width of a human body contour (Fan paragraph [0046] "The optimized theoretical scan parameters and theoretical image reconstruction parameters may be based on a variety of patient-specific factors, including patient size, as may be determined from the acquired scout scan, disease type, and region of interest in view of image quality assessment of the simulated image, as informed by a clinician, and radiation exposure"), and wherein tissue composition metrics (Rupcich paragraph [0049] "Scout scans provide information for determining a density, a size, and a shape of the person. For example, total projection attenuation may include information relating to a density and a size of the person, and an amplitude and width of the projection from the scout scan may include information relating to a shape of the person) include
The combination of Xia, Fan, Rupcich and Malkus does not explicitly teach the “tissue composition metrics include a proportion of low attenuation tissue, a proportion of medium attenuation tissue, and a proportion of high attenuation tissue”.
However, Hammer teaches “tissue composition metrics include a proportion of low attenuation tissue, a proportion of medium attenuation tissue, and a proportion of high attenuation tissue” (Hammer page 4 figure and page 3 paragraph 7 "In soft tissues, the dominant elements (e.g. C, H, O, and N) have very low K-edges, in the range of a few keV. While these elements do contribute to the photoelectric effect and attenuate low energy x-rays, there is no relevant k-edge with its substantial change in attenuation. However, the elements iodine and barium have K-edges around 30-40 keV, right in the middle of the x-ray beam spectrum. Thus, soft tissues with even a small amount of iodine will have a much stronger x-ray stopping power than those without").
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Hammer page 4 Figure
It would have been obvious to a person having ordinary skill in the art before effective filing date of the claimed invention of the instant application to combine an scout scan, scanning protocol, and image noise parameter, to get a tube current value using machine learning as taught by Xia, Fan, Rupcich and Malkus to include extracted features of tissue composition metrics as taught by Hammer because such a modification is the result of applying a known technique to a known device ready for improvement to yield predictable results. More specifically, extracting information of attenuation tissue permits the generation of clear images containing important structural information, as suggested by Hammer page 3 paragraph 5 “If all different types of tissue stopped x-rays in the same way, then we would have no picture - just a gray blob on our screen. The difference in x-ray penetration between different tissues represents the contrast in the image”. Therefore, it would have been recognized that modifying the method of optimizing tube current value as taught by Xia, Fan, Rupcich and Malkus to include extracted features of tissue composition metrics as taught by Hammer would have yielded predictable results because (i) the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate additional extracted features from a scout scan, specifically tissue composition metrics related to attenuation and (ii) the benefits of such a combination would have been recognized by those of ordinary skill in the art.
Therefore, it would have been obvious to combine the disclosure of Xia, Fan, Rupcich and Malkus with the Hammer disclosure to obtain the invention as specified in claim 8 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Publication 20150265224 A1 to Gerland discloses calculation of radiation in a region using a scout scan and extracted parameters.
US Publication 20180049714 A1 to Nett discloses reducing radiation dose while maintaining image quality using scan parameters and machine learning .
US Publication 20210045703 A1 to Crotty et al. discloses a scan parameter optimizer to reduce radiation dose.
US Publication 20230081601 A1 to Wang et al. discloses a method to control radiation dose automatically using machine learning.
International Publication WO 2013049818 A1 to Larson discloses system and method to control radiation dose using scan parameters obtained from scout scan.
International Publication CN 100563574 C to Hirokawa et al. discloses obtaining a CT image with desired image quality using scout scan.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JASPREET KAUR/Examiner, Art Unit 2662 /AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662