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 .
DETAILED ACTION
Status of Claims
Claims 1-20 are rejected under 35 USC §101 Rejection.
Claims 1, 5, 6, 12, 14, 15 and 17 are rejected under 35 USC §102 Rejection.
Claims 2-4, 7-11, 13, 15, 16 and 18-20 are rejected under 35 USC §103 Rejection.
Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more as addressed below.
The new 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (Vol. 84 No. 4, Jan 7, 2019 pp 50-57) has been applied and the claims are deemed as being patent ineligible.
The current 35 USC 101 analysis is based on the current guidance (Federal Register vol. 79, No. 241. pp. 74618-74633). The analysis follows several steps. Step 1 determines whether the claim belongs to a valid statutory class. Step 2A prong 1 identifies whether an abstract idea is claimed. Step 2A prong 2 determines whether an abstract idea is integrated into a practical application. If the abstract idea is integrated into a practical application the claim is patent eligible under 35 USC 101. Last, step 2B determines whether the claims contain something significantly more than the abstract idea. In most cases the existence of a practical application predicates the existence of an additional element that is significantly more.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a
statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C.
101: Process, machine, manufacture, or composition of matter. The below claim is considered to be in a statutory category (process).
Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a process claim.
Under Step 2A Prong 1, the independent claim 1 includes abstract ideas as highlighted (using a bold font) below.
“1. A computer-implemented method for preparing a computed tomography scan of a subject, the method comprising:
receiving scan-related information including subject-related information;
automatically adapting at least two imaging parameters via an optimization function in order to optimize image quality, wherein the optimization function includes the scan-related information as at least one constant and the at least two imaging parameters as variables to be optimized, and wherein optimizing of the image quality includes reducing scan artifacts of the computed tomography scan; and
providing the adapted at least two imaging parameters.”
The highlighted steps indicated as Abstract idea are considered to be equivalent to mathematical steps and fundamental aspect of mathematics or directed to mental processes performed in the human mind (including observation, evaluation and opinion).
Under step 2A prong 2,
The claim 1 is only limited to the broad field-of-use of computed tomography scanning.
The claim taken as a whole does not integrate the abstract idea into a particular practical application.
The steps of “receiving scan-related information including subject-related information” are just obtaining data, which is insignificant extra solution activity.
The step of “providing the adapted at least two imaging parameters” which is directed to the post solution activity equivalent to “providing”, which is insignificant additional step. Claim 1 does not recite what is provided with the parameters or what purpose is served by providing these parameters. Therefore claim 1 does not recite a complete, particular practical application, but only gives fragments of a particular practical application.
Under step 2B
Claim 1 does not comprise any additional elements or any physical hardware for measuring data.
The steps of “receiving scan-related information including subject-related information” are just obtaining data, which is insignificant extra solution activity.
The step of “providing the adapted at least two imaging parameters” which is directed to the post solution activity equivalent to “providing”, which is insignificant additional step.
The dependent claims 2-5, 8, 10, 16, 19 and 20 merely extend the details of the abstract idea of mathematical concepts, more particularly mathematical calculations or mental steps as accrued.
Claim 6, 7, 9, 12, 13, 17, and 18 additionally describe the type of the data.
Claim 8 just additionally recites receiving a camera image, which is insignificant data gathering.
Claim 14 and 15 comprise computer-readable medium storing computer-readable instructions, and a processor of a computed tomography system, which are just general-purpose processing components of a computer and software running on the computer. The computer is the general-purpose computer, which is not significantly more.
Therefore claims 2-20 are similarly rejected under 35 U.S.C. 101.
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.
Claims 1, 5, 6, 12, 14, 15 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bertram (US Pub.20120230470A1), hereinafter Bertram.
Regarding Claim 1, Bertram discloses a computer-implemented method for preparing a computed tomography scan of a subject, the method comprising:
receiving scan-related information including subject-related information (para [0037], where suitable task optimization criteria may indicate that the optimizer 212 should generate a set of acquisition protocol parameter values based on tissue of interest (e.g., bone, soft tissue, etc.), energy(s) of interest, system configuration (single or multi tube, single or multi layer detectors, etc.), etc. Suitable material-specific optimization criteria may indicate that the optimizer 212 should generate a set of acquisition protocol parameter values based on a contrast medium, a calcification, a tumor, etc.; para [0041], where one or more of the volumetric image data, segmented volumetric image data, a model of anatomy, a dose estimate, a dose map, a noise estimate, a noise map, a contrast-to-noise ratio, a contrast-to-noise map, e.g., model of anatomy corresponds to the scan-related information);
automatically adapting at least two imaging parameters via an optimization function in order to optimize image quality (Abstract, where estimated image noise based on the acquisition protocol parameter values; para [004], where automatically determined optimized acquisition protocol parameter values. For example, the computer implemented algorithm may not take into account a scanning system limitation (e.g., available kVp, mAs, etc. settings) of the CT system, i.e., the “optimized acquisition protocol parameter” equally to the optimization function), wherein the optimization function includes the scan-related information as at least one constant (para [0037], where suitable task optimization criteria may indicate that the optimizer 212 should generate a set of acquisition protocol parameter values based on tissue of interest (e.g., bone, soft tissue, etc.), e.g., tissue of interest corresponds to the constant information… different acquisition protocol parameter values may be better suited for the particular imaging procedure and/or patient.) and the at least two imaging parameters as variables to be optimized (para [003], where various acquisition protocol parameters such as tube current (mAs), tube voltage (kVp), pitch/exposure time (for helical scans), slice thickness and spacing (for axial scans), as well as patient size., para [004], where the computer implemented algorithm may not take into account a scanning system limitation (e.g., available kVp, mAs, etc. settings) of the CT system. different acquisition protocol parameter values may be better suited for the particular imaging procedure and/or patient, e.g., kVp and mVs corresponds to the parameter values measurements), and
wherein optimizing of the image quality includes reducing scan artifacts of the computed tomography scan (para [004], where protocol parameter values that is optimized to reduce radiation dose); and
providing the adapted at least two imaging parameters (para [0025], where generate optimized acquisition protocol parameter values).
Regarding Claim 5, Bertram discloses the computer-implemented method according to claim 1, further Bertram discloses wherein the optimization function comprises a dose response function, the dose response function (para [003], where, image noise is inversely proportional to radiation dose and, thus, reducing radiation dose increases noise, which reduces image quality), (para [0025], where models that can be used to estimated dose and/or image noise, determine dose and/or image noise profiles, and/or generate optimized acquisition protocol parameter values), . depending on the at least two imaging parameters(para [003], where various acquisition protocol parameters such as tube current (mAs), tube voltage (kVp), pitch/exposure time (for helical scans), slice thickness and spacing (for axial scans), as well as patient size., para 004, where the computer implemented algorithm may not take into account a scanning system limitation (e.g., available kVp, mAs, etc. settings) of the CT system. different acquisition protocol parameter values may be better suited for the particular imaging procedure and/or patient, e.g., kVp and mVs corresponds to the parameter values measurements).
Regarding Claim 6, Bertram discloses the computer-implemented method according to claim 1, wherein the subject is a patient, and the scan-related information comprises a size of the patient (para [003], where radiation dose deposited in the patient is based on various acquisition protocol parameters such as tube current (mAs), tube voltage (kVp), pitch/exposure time (for helical scans), slice thickness and spacing (for axial scans), as well as patient size).
Regarding Claim 12, Bertram discloses the computer-implemented method according to claim 1, wherein the at least two imaging parameters are scan parameters, and the scan parameters include at least one of a kV filter setting, a spectral filter setting, a collimation setting, a spiral pitch factor or a rotation time setting (para [003], where adiation dose deposited in the patient is based on various acquisition protocol parameters such as tube current (mAs), tube voltage (kVp), pitch/exposure time (for helical scans)).
Regarding Claim 14, Bertram discloses a non-transitory computer-readable medium storing computer-readable instructions (para [0018], where processor of the console 118 executes computer readable instructions on the console 118; para [0023], where processors and computer readable storage medium that stores computer executable instructions) that, when executed by a computer, cause the computer to carry out the computer- implemented method of claim 1(para [004], where the computer implemented algorithm).
Regarding Claim 15, Bertram discloses a computed tomography system comprising:
at least one processor configured to carry out the computer-implemented method according to claim 1 (para [004], where the computer implemented algorithm).
Regarding Claim 17, Bertram discloses the computer-implemented method according to claim 1, wherein the scan-related information comprises a size of the subject(para [003], where various acquisition protocol parameters such as tube current (mAs), tube voltage (kVp), pitch/exposure time (for helical scans), slice thickness and spacing (for axial scans), as well as patient size).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2, 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bertram, as applied above and further in view of Yang et al., (WO2023039096), hereinafter Yang.
Regarding Claim 2, Bertram discloses the computer-implemented method according to claim 1, wherein the optimization function (para [002], where a reconstructor processes the projection data and reconstructs volumetric image data; para [003], where, image noise is inversely proportional to radiation dose and, thus, reducing radiation dose increases noise, which reduces image quality), (para [0025], where models that can be used to estimated dose and/or image noise, determine dose and/or image noise profiles, and/or generate optimized acquisition protocol parameter values),
but Bertram does not disclose function comprises a linear combination of a plurality of sub-cost functions, each of the plurality of sub-cost functions representing another aspect of image quality.
Yang discloses function comprises a linear combination of a plurality of sub-cost functions, each of the plurality of sub-cost functions representing another aspect of [image quality] /(clustering process for metadata of distances)(para [0083], where defined as a linear combination of a sub-cost of each of the penalty terms; para [0055], where for each object i and clusters j = 1, ... ,J, the object- to-cluster gains gtjJ = 1, ... ,J can be determined by minimizing a cost function, where the cost comprises several penalty terms; para [0059], where a linear combination of the three sub-cost terms.
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where wP, wD and wN are the tunable coefficients of the corresponding sub-cost terms, Ed second term meagres the distance between object and cluster term En, measures the loss energy (para 0057), Ep measures the difference of original object position and reconstructed position of clusters (see para 0056)).
Yang discloses position correctness, distance of the object based on audio system by for clustering position associated with metadata into backet.
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide linear combination of a plurality of sub-cost functions as taught by Yang into optimization function of Bertram in order to balance multiple, often competing, goals simultaneously to prevent overfitting.
Regarding Claim 3, Bertram and Yang disclose the computer-implemented method according to claim 2, wherein Yang disclose at least one of the plurality of sub-cost functions is a penalty term, as recited in claim 2, but Bertram and Yang do not disclose the penalty term being weighted by a weighting parameter.
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to weighted a penalty term, as taught by Yang in optimization order to allows for the incorporation of various constraints into a single objective function.
Regarding Claim 16, Bertram and Yang disclose the computer-implemented method according to claim 2, Bertram does not discloses wherein at least one of the plurality of sub-cost functions is a penalty term.
Yang discloses at least one of the plurality of sub-cost functions is a penalty term (para [0083], where defined as a linear combination of a sub-cost of each of the penalty terms).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide a penalty term, as taught by Yang into Bertram in order to provides a clear direction for the optimization process and accelerates the convergence of the optimization process.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Bertram, in view of Yang as applied above and further in view of Bornefalk et al., (US Pub.20200261041A1), hereinafter Bornefalk.
Regarding Claim 4, Bertram and Yang disclose the computer-implemented method according to claim 3, but do not disclose wherein at least one penalty term relates at least a portion of the scan-related information to at least one of the imaging parameters.
Bornefalk discloses at least one penalty term relates at least a portion of the scan-related information to at least one of the imaging parameters (para [0088], where use the prior information is to obtain a more desirable tradeoff between image contrast-to-noise ratio and spatial resolution (and possibly aliasing) by adding a penalty term in the native resolution material basis decomposition, e.g., tradeoff between image contrast-to-noise ratio and spatial resolution corresponds to the portion of the scan-related information).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide a penalty term relates at least a portion of the scan-related information to at least one of the imaging parameters, as taught by Bornefalk in combination of Bertram and Yang in order to improve image quality.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Bertram, as applied above and further in view of Lou (US Pub.20160183905), hereinafter Lou.
Regarding Claim 7, Bertram discloses the computer-implemented method according to claim 1, but does not disclose wherein the scan-related information comprises a diameter of the subject at a region of interest that is to be scanned.
Lou discloses the scan-related information comprises a diameter of the subject at a region of interest that is to be scanned (Fig. 8, para [0043], where equivalent diameter may be taken as a kind of subject information. And thus, subjects having the same equivalent diameter may be viewed as of the same body size. Para [0046], where Table 1, the reference information samples having the same scanning protocol “ProtocalA” and the same subject information “equivalent diameter of 400 mm”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide a diameter of the subject at a region of interest that is to be scanned as taught by Lou into Bertram in order to provide accurate image reconstructing images and more effective dose management.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Bertram, as applied above and further in view of Teixeira (CN115251963A), hereinafter Teixeira.
Regarding Claim 8, Bertram disclose the computer-implemented method according to claim 6, further comprising:
Bertram does not disclose receiving a topogram or a camera image of the subject, wherein the topogram or the camera image has been acquired at a beginning of a CT examination in which a CT scan whose imaging parameters are adapted is to be acquired; and
deriving a size or diameter of the subject from the topogram or the camera image.
Teixeira discloses receiving a topogram or a camera image of the subject, wherein the topogram or the camera image has been acquired at a beginning of a CT examination in which a CT scan whose imaging parameters are adapted is to be acquired (Page 2, lines 19-20, where obtaining the location image will expose the patient to radiation) (page 2, lines 17-18, where determine/adjust the CT scanning parameter, especially the
radiation dose related parameters (such as tube current and tube potential/voltage of x-ray tube 1106); and
deriving a size or diameter of the subject from the topogram or the camera image (page 2, lines 510, where estimating patient size body x-ray attenuation property associated with the patient body and so on method is obtained before using CT scan of the locating image (topogram)).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to deriving a size or diameter of the subject from the topogram or the camera image, as taught by Teixeira into Bertram in order to provide accurate patient dose estimation.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Bertram, as applied above and further in view of Jiang (US Pub.20080170228A1), hereinafter Jiang.
Regarding Claim 9, Bertram discloses the computer-implemented method according to claim 1, but do not disclose wherein the at least two imaging parameters comprise a wedge filter setting.
Jiang discloses the at least two imaging parameters comprise a wedge filter setting (para 0034, where certain parameters should be known from the wedge filter 100 manufacturer, such as the length of the wedge filter 100; the width w of the wedge filter 100 he passing spectral range of the wedge filter 100 (e.g., 400 nm-1100 nm); and the passing wavelength versus location along the width w of the wedge filter 100).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide a wedge filter setting as taught by Jiang into Bertram in order to reduce the dynamic range and improves image quality.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bertram, as applied above and further in view of Boone et al., (US Pub.20180317867), hereinafter Boone.
Regarding Claims 10 and 20, Bertram and Yang disclose the computer-implemented method according to claim 1/ the computer-implemented method according to claim 5, wherein automatically adapting the at least two imaging parameters, as recited in claim 1.
Bertram and Yang do not disclose determining, depending on the scan-related information, a wedge filter to be used out of at least two available wedge filters.
Boone discloses automatically adapting the at least two imaging parameters comprises: determining, depending on the scan-related information, a wedge filter to be used out of at least two available wedge filters (para [0011], where selecting a bowtie-shaped filter among a plurality of bowtie-shaped filters based on the shape or size of the body part, selecting a wedge-shaped filter among a plurality of wedge-shaped filter based on the shape or size of the body part, and combining the selected bowtie-shaped filter and the selected wedge-shaped filter into the combined filter).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to a wedge filter to be used out of at least two available wedge filters, as taught by Boone into Bertram in order to increase the image quality.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Bertram, as applied above and further in view of Carmi (US Pub.20210327560A1), hereinafter Carmi.
Regarding Claim 13, Bertram discloses the computer-implemented method according to claim 1, but does not disclose wherein the at least two imaging parameters comprise at least one reconstruction parameter, the at least one reconstruction parameter being a keV level of reconstruction.
Carmi discloses the at least two imaging parameters comprise at least one reconstruction parameter, the at least one reconstruction parameter being a keV level of reconstruction (para 0092, where reconstruction settings may include select keV energy level(s), iterative reconstruction (e.g., adaptive statistical reconstruction), direct multi-planar reconstruction, algorithmic reconstruction, and/or the like).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide reconstruction parameter, the at least one reconstruction parameter being a keV level, as taught by Lou into Bertram in order to provide accurate image reconstructing images and more effective dose management.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Bertram, in view of Teixeira, as applied above and further in view of Jiang (US Pub.20080170228A1), hereinafter Jiang.
Regarding Claim 18, Bertram discloses the computer-implemented method according to claim 8, but do not disclose wherein the at least two imaging parameters comprise a wedge filter setting.
Jiang discloses the at least two imaging parameters comprise a wedge filter setting (para 0034, where certain parameters should be known from the wedge filter 100 manufacturer, such as the length of the wedge filter 100; the width w of the wedge filter 100 he passing spectral range of the wedge filter 100 (e.g., 400 nm-1100 nm); and the passing wavelength versus location along the width w of the wedge filter 100).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide a wedge filter setting as taught by Jiang in combination of Bertram and Teixeira in order to reduce the dynamic range and improves image quality.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Bertram, in view of Yang, as applied above and further in view of Boone et al., (US Pub.20180317867), hereinafter Boone.
Regarding Claim 19, Bertram and Yang disclose the computer-implemented method according to claim 2, wherein automatically adapting the at least two imaging parameters, as recited in claim 1.
Bertram and Yang do not disclose determining, depending on the scan-related information, a wedge filter to be used out of at least two available wedge filters.
Boone discloses automatically adapting the at least two imaging parameters comprises: determining, depending on the scan-related information, a wedge filter to be used out of at least two available wedge filters (para [0011], where selecting a bowtie-shaped filter among a plurality of bowtie-shaped filters based on the shape or size of the body part, selecting a wedge-shaped filter among a plurality of wedge-shaped filter based on the shape or size of the body part, and combining the selected bowtie-shaped filter and the selected wedge-shaped filter into the combined filter).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to a wedge filter to be used out of at least two available wedge filters, as taught by Boone into Bertram in order to increase the image quality.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Bertram, as applied above and further in view of Jan (US Pub.20150117597), hereinafter Jan.
Regarding Claim 11, Bertram discloses the computer-implemented method according to claim 1, wherein the automatically adapting the at least two imaging parameters comprises: choosing a computed tomography scan mode from a plurality of available computed tomography scan modes.
Jan discloses automatically adapting the at least two imaging parameters comprises: choosing a computed tomography scan mode from a plurality of available computed tomography scan modes (para [003], where computed tomographic imaging that choose the most appropriate scan mode based on the characteristics of the subject to be scanned so as to obtain a corresponding optimal image by analyzing the transmitted projection data within a limited scan range).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to choosing a computed tomography scan mode, as taught by Jan into Bertram in order to enhance the diagnostic capabilities and quality of the images produced.
Conclusion
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/KALERIA KNOX/
Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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