DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/09/2024 and 02/06/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 8-10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yaroshenko et al. (EP 3494885 A1) in view of Davies US PG-Pub(US 20200372700 A1).
Regarding Claim 1, Yaroshenko teaches a computer-implemented method(DAX)imaging(¶[0025] discloses a method for image analysis of darkfield x-ray images.), comprising:([0024] “The at least one image acquisition unit is configured to provide the X-ray attenuation image, and to provide the dark field X-ray image” ¶[0024] discloses receiving a darkfield x-ray and x-ray attenuation image of a subject by a image acquisition unit.); segmenting(¶[0031], “The processing unit 30 is configured to define a plurality of sub-regions of the region of interest based on the X-ray attenuation image of the region of interest or based on the dark field X-ray image of the region of interest” [0032] “In an example, the region of interest is a lung.” [0033] ”In an example, the plurality of sub-regions are defined through a segmentation of the region of the object. For example, the segmentation can be oriented on anatomical structures and also on manually defined regions.”, ¶[0031]-¶[0033] disclose a region of interest is defined in the x-ray attenuation and DAX image and the images are segmented to capture the region of interest along with subregions of the structure.); analyzing(¶[0025], “c) defining a plurality of sub-regions of the region of interest based on the X-ray attenuation image of the region of interest or based on the dark field X-ray image of the region of interest;
d) deriving at least one quantitative value for each of the plurality of sub-regions, wherein the at least one quantitative value for a sub-region comprises data derived from the X-ray attenuation image of the sub-region and data derived from the dark field X-ray image of the sub-region;
e) assigning a plurality of figures of merit to the plurality of sub-regions, wherein a figure of merit for a sub-region is based on the at least one quantitative value for the sub-region; and
f) outputting data representative of the region of interest with figures of merit for the respective sub-regions.”, ¶[0025] discloses a method of dividing the region of interest into subregions and calculating a quantitative value for each subregion. ¶[0057] and figure 4 further discloses the process of how the sub-regions are defined and the quantitative values are used to determine the dark-field and transmission signals strength.)
Yaroshenko does not explicitly teach quantifying a corresponding measure of image data heterogeneity (Dmeas); and providing a
Davies teaches quantifying a corresponding measure of image data heterogeneity (Dmeas); and providing(¶[0107], “At stage 54, the sampling circuitry 36 receives or generates further sampling points on the image plane. A distribution of the further sampling points is dependent on the heterogeneity map such that an average concentration of further sampled points varies with position in dependence on the value of the heterogeneity metric in the heterogeneity map. The image plane is divided into a plurality of image regions. The heterogeneity map is used to determine whether each of the image regions is low-heterogeneity or high-heterogeneity. A sampling density for each image region is decided based on the heterogeneity map. A lower sampling density is applied to the low-heterogeneity regions than to the high-heterogeneity regions.”, ¶[0107] discloses measuring heterogeneity in regions by sampling points on the image plane and using a heterogeneity map to determine image regions with low and high heterogeneity.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yaroshenko with Davies in order to measure image data heterogeneity in each region of the image. One skilled in the art would have been motivated to modify Yaroshenko in this manner in order to produce images that are convincing or acceptable to the medical practitioner whilst also accurately representing anatomical or other features of the subject of the image. (Davies, ¶[0004)
Regarding Claim 2, the combination of Yaroshenko and Davies teach the computer-implemented method according to claim 1, where Yaroshenko further teaches wherein quantifying the measure of image data heterogeneity comprises: obtaining a measure of central tendency of the DAX image data per subregion N(¶[0025], “d) deriving at least one quantitative value for each of the plurality of sub-regions, wherein the at least one quantitative value for a sub-region comprises data derived from the X-ray attenuation image of the sub-region and data derived from the dark field X-ray image of the sub-region;
e) assigning a plurality of figures of merit to the plurality of sub-regions, wherein a figure of merit for a sub-region is based on the at least one quantitative value for the sub-region; and
f) outputting data representative of the region of interest with figures of merit for the respective sub-regions.” ¶[0025] discloses measuring a quantitative value in each subregion of the dark field image.) and a measure of statistical dispersion based on the ensemble of measures of central tendency of the DAX image data per subregion N. ([0058] “Based on the defined sub-regions of the lung, local quantitative information on the quality of the lung based on the combination of the dark-field and transmission signals is derived. This is achieved through the normalization of the dark-field signal. Then the normalized dark field is summarized for the corresponding sub-region, using for example a 0-4 scale, as described below in the table”, ¶[0058] discloses using the quantitative value calculated in each region and based on the value of the region a measure of the signal strength for each region is shown in the table on page 6 lines 10-19.)
Regarding Claim 3, the combination of Yaroshenko and Davies teach the computer-implemented method according to claim 1, where Davies further teaches wherein the indicator of image data heterogeneity comprises the measure of image data heterogeneity (D.sub.meas) and/or the corresponding subregion size (SR.sub.meas). (¶[0107], “The image plane is divided into a plurality of image regions. The heterogeneity map is used to determine whether each of the image regions is low-heterogeneity or high-heterogeneity. A sampling density for each image region is decided based on the heterogeneity map. A lower sampling density is applied to the low-heterogeneity regions than to the high-heterogeneity regions. Areas of high heterogeneity are sampled at higher density than areas of lower heterogeneity. In other embodiments, any suitable method of determining sampling density based on the heterogeneity map may be used.”, ¶[0107] discloses dividing the image into a plurality of regions and sampling each region to determine areas of high or low heterogeneity in the image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yaroshenko with Davies in order to measure image data heterogeneity in each region of the image. One skilled in the art would have been motivated to modify Yaroshenko in this manner in order to produce images that are convincing or acceptable to the medical practitioner whilst also accurately representing anatomical or other features of the subject of the image. (Davies, ¶[0004)
Regarding Claim 8, Yaroshenko teaches an apparatus for heterogeneity analysis in 3D X-ray dark-field (DAX) imaging(Fig. 2 shows an example of a system 100 for presentation of dark field information.), comprising: a memory that stores a plurality of instructions and a processor coupled to the memory and configured to execute the plurality of instructions (¶[0080] “A computer program may be loaded into a working memory of a data processor. The data processor may thus be equipped to carry out the method according to one of the preceding embodiments.”, ¶[0080] discloses a processor that is coupled to a memory to execute a program) to: receive DAX image data and X-ray attenuation image data of a subject (¶[0024] “The at least one image acquisition unit is configured to provide the X-ray attenuation image, and to provide the dark field X-ray image” ¶[0024] discloses receiving a darkfield x-ray and x-ray attenuation image of a subject by a image acquisition unit.); segment the X-ray attenuation image data and the DAX image data to identify a region of interest (ROI) in the DAX image data (¶[0031], “The processing unit 30 is configured to define a plurality of sub-regions of the region of interest based on the X-ray attenuation image of the region of interest or based on the dark field X-ray image of the region of interest” [0032] “In an example, the region of interest is a lung.” [0033] ”In an example, the plurality of sub-regions are defined through a segmentation of the region of the object. For example, the segmentation can be oriented on anatomical structures and also on manually defined regions.”, ¶[0031]-¶[0033] disclose a region of interest is defined in the x-ray attenuation and DAX image and the images are segmented to capture the region of interest along with subregions of the structure.); analyze the image data heterogeneity by performing a statistical analysis of the ROI based on dividing the ROI in a number N of subregions with an associated subregion size (SR.sub.meas) (¶[0025], “c) defining a plurality of sub-regions of the region of interest based on the X-ray attenuation image of the region of interest or based on the dark field X-ray image of the region of interest;
d) deriving at least one quantitative value for each of the plurality of sub-regions, wherein the at least one quantitative value for a sub-region comprises data derived from the X-ray attenuation image of the sub-region and data derived from the dark field X-ray image of the sub-region;
e) assigning a plurality of figures of merit to the plurality of sub-regions, wherein a figure of merit for a sub-region is based on the at least one quantitative value for the sub-region; and
f) outputting data representative of the region of interest with figures of merit for the respective sub-regions.”, ¶[0025] discloses a method of dividing the region of interest into subregions and calculating a quantitative value for each subregion. ¶[0057] and figure 4 further discloses the process of how the sub-regions are defined and the quantitative values are used to determine the dark-field and transmission signals strength.)
Yaroshenko does not explicitly teach quantifying a corresponding measure of image data heterogeneity (Dmeas); and providing a
Davies teaches quantifying a corresponding measure of image data heterogeneity (Dmeas); and providing(¶[0107], “At stage 54, the sampling circuitry 36 receives or generates further sampling points on the image plane. A distribution of the further sampling points is dependent on the heterogeneity map such that an average concentration of further sampled points varies with position in dependence on the value of the heterogeneity metric in the heterogeneity map. The image plane is divided into a plurality of image regions. The heterogeneity map is used to determine whether each of the image regions is low-heterogeneity or high-heterogeneity. A sampling density for each image region is decided based on the heterogeneity map. A lower sampling density is applied to the low-heterogeneity regions than to the high-heterogeneity regions.”, ¶[0107] discloses measuring heterogeneity in regions by sampling points on the image plane and using a heterogeneity map to determine image regions with low and high heterogeneity.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yaroshenko with Davies in order to measure image data heterogeneity in each region of the image. One skilled in the art would have been motivated to modify Yaroshenko in this manner in order to produce images that are convincing or acceptable to the medical practitioner whilst also accurately representing anatomical or other features of the subject of the image. (Davies, ¶[0004)
Regarding Claim 9, the combination of Yaroshenko and Davies teach the apparatus according to claim 8, where Yaroshenko further teaches wherein quantifying the measure of image data heterogeneity comprises: obtaining a measure of central tendency of the DAX image data per subregion N(¶[0025], “d) deriving at least one quantitative value for each of the plurality of sub-regions, wherein the at least one quantitative value for a sub-region comprises data derived from the X-ray attenuation image of the sub-region and data derived from the dark field X-ray image of the sub-region;
e) assigning a plurality of figures of merit to the plurality of sub-regions, wherein a figure of merit for a sub-region is based on the at least one quantitative value for the sub-region; and
f) outputting data representative of the region of interest with figures of merit for the respective sub-regions.” ¶[0025] discloses measuring a quantitative value in each subregion of the dark field image.) and a measure of statistical dispersion based on the ensemble of measures of central tendency of the DAX image data per subregion N. ([0058] “Based on the defined sub-regions of the lung, local quantitative information on the quality of the lung based on the combination of the dark-field and transmission signals is derived. This is achieved through the normalization of the dark-field signal. Then the normalized dark field is summarized for the corresponding sub-region, using for example a 0-4 scale, as described below in the table”, ¶[0058] discloses using the quantitative value calculated in each region and based on the value of the region a measure of the signal strength for each region is shown in the table on page 6 lines 10-19.)
Regarding Claim 10, the combination of Yaroshenko and Davies teach the apparatus according to claim 8, where Davies further teaches wherein the indicator of image data heterogeneity comprises the measure of image data heterogeneity (D.sub.meas) and/or the corresponding subregion size (SR.sub.meas). (¶[0107], “The image plane is divided into a plurality of image regions. The heterogeneity map is used to determine whether each of the image regions is low-heterogeneity or high-heterogeneity. A sampling density for each image region is decided based on the heterogeneity map. A lower sampling density is applied to the low-heterogeneity regions than to the high-heterogeneity regions. Areas of high heterogeneity are sampled at higher density than areas of lower heterogeneity. In other embodiments, any suitable method of determining sampling density based on the heterogeneity map may be used.”, ¶[0107] discloses dividing the image into a plurality of regions and sampling each region to determine areas of high or low heterogeneity in the image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yaroshenko with Davies in order to measure image data heterogeneity in each region of the image. One skilled in the art would have been motivated to modify Yaroshenko in this manner in order to produce images that are convincing or acceptable to the medical practitioner whilst also accurately representing anatomical or other features of the subject of the image. (Davies, ¶[0004)
Regarding Claim 16, Yaroshenko teaches a non-transitory computer-readable medium for storing executable instructions([0084] A computer program may be stored and/or distributed on a suitable medium, such as an optical storage medium or a solid state medium supplied together with or as part of other hardware,), which cause a computer-implemented method for heterogeneity analysis in 3D X-ray dark-field (DAX) imaging to be performed(¶[0025] discloses a method for image analysis of darkfield x-ray images.),, the method comprising: receiving([0024] “The at least one image acquisition unit is configured to provide the X-ray attenuation image, and to provide the dark field X-ray image” ¶[0024] discloses receiving a darkfield x-ray and x-ray attenuation image of a subject by a image acquisition unit.); segmenting(ROI) in the DAX image data (¶[0031], “The processing unit 30 is configured to define a plurality of sub-regions of the region of interest based on the X-ray attenuation image of the region of interest or based on the dark field X-ray image of the region of interest” [0032] “In an example, the region of interest is a lung.” [0033] ”In an example, the plurality of sub-regions are defined through a segmentation of the region of the object. For example, the segmentation can be oriented on anatomical structures and also on manually defined regions.”, ¶[0031]-¶[0033] disclose a region of interest is defined in the x-ray attenuation and DAX image and the images are segmented to capture the region of interest along with subregions of the structure.); analyzing(¶[0025], “c) defining a plurality of sub-regions of the region of interest based on the X-ray attenuation image of the region of interest or based on the dark field X-ray image of the region of interest;
d) deriving at least one quantitative value for each of the plurality of sub-regions, wherein the at least one quantitative value for a sub-region comprises data derived from the X-ray attenuation image of the sub-region and data derived from the dark field X-ray image of the sub-region;
e) assigning a plurality of figures of merit to the plurality of sub-regions, wherein a figure of merit for a sub-region is based on the at least one quantitative value for the sub-region; and
f) outputting data representative of the region of interest with figures of merit for the respective sub-regions.”, ¶[0025] discloses a method of dividing the region of interest into subregions and calculating a quantitative value for each subregion. ¶[0057] and figure 4 further discloses the process of how the sub-regions are defined and the quantitative values are used to determine the dark-field and transmission signals strength.)
Yaroshenko does not explicitly teach quantifying a corresponding measure of image data heterogeneity (Dmeas); and providing a
Davies teaches quantifying a corresponding measure of image data heterogeneity (Dmeas); and providing(¶[0107], “At stage 54, the sampling circuitry 36 receives or generates further sampling points on the image plane. A distribution of the further sampling points is dependent on the heterogeneity map such that an average concentration of further sampled points varies with position in dependence on the value of the heterogeneity metric in the heterogeneity map. The image plane is divided into a plurality of image regions. The heterogeneity map is used to determine whether each of the image regions is low-heterogeneity or high-heterogeneity. A sampling density for each image region is decided based on the heterogeneity map. A lower sampling density is applied to the low-heterogeneity regions than to the high-heterogeneity regions.”, ¶[0107] discloses measuring heterogeneity in regions by sampling points on the image plane and using a heterogeneity map to determine image regions with low and high heterogeneity.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Yaroshenko with Davies in order to measure image data heterogeneity in each region of the image. One skilled in the art would have been motivated to modify Yaroshenko in this manner in order to produce images that are convincing or acceptable to the medical practitioner whilst also accurately representing anatomical or other features of the subject of the image. (Davies, ¶[0004)
Allowable Subject Matter
Claims 4-7 and 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding Claim 4, the primary reason for the allowance of the claim is the inclusion of the limitation, “wherein the ROI is an ensemble of ROIs (ROI.sub.i, i=[1,n]), each ROI characterized by a measure of image data heterogeneity for a subregion size, and wherein the indicator of image data heterogeneity is an ensemble of measures of image data heterogeneity (D.sub.meas,i=[1,n]) and/or an ensemble of subregion sizes (SR.sub.meas,i=[1,n], and further comprising mapping the elements of the ensemble of indicators of image data heterogeneity to their corresponding ROI”, in all the claim which is not found in the prior art references. It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitation supra, when read in light/combination of the other claimed limitation within the cited claims. Also, it is noted that the quoted limitations, in combination with the other claim limitations of the cited claims, deem the claim patentable, not just the consideration of the quoted limitations by themselves.
Regarding Claim 5, the primary reason for the allowance of the claim is the inclusion of the limitation, “receiving a reference measure of image data heterogeneity (D.sub.ref); determining from an ensemble of ROI subregion sizes with corresponding measures of image data heterogeneity, the ROI subregion size (SR.sub.max) whose measure of image data heterogeneity is maximum (D.sub.max); and wherein the indicator of image data heterogeneity is based on a comparison of the values of D.sub.max and D.sub.ref.”, in all the claim which is not found in the prior art references. It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitation supra, when read in light/combination of the other claimed limitation within the cited claims. Also, it is noted that the quoted limitations, in combination with the other claim limitations of the cited claims, deem the claim patentable, not just the consideration of the quoted limitations by themselves.
Regarding Claim 6, the primary reason for the allowance of the claim is the inclusion of the limitation, “receiving a reference measure of image data heterogeneity (D.sub.ref); estimating a ROI subregion size of the DAX image data (SR.sub.meas,i) whose measure of image data heterogeneity (D.sub.meas,i) is approximately equal to D.sub.ref; and wherein the indicator of image data heterogeneity is the estimated SR.sub.meas,i.” in all the claim which is not found in the prior art references. It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitation supra, when read in light/combination of the other claimed limitation within the cited claims. Also, it is noted that the quoted limitations, in combination with the other claim limitations of the cited claims, deem the claim patentable, not just the consideration of the quoted limitations by themselves.
Regarding Claim 7, the primary reason for the allowance of the claim is the inclusion of the limitation, “receiving a reference measure of image data heterogeneity (D.sub.ref); determining an ensemble of ROI subregion sizes of the DAX image data (SR.sub.meas,i, i=[1,n]) whose measure of image data heterogeneity (D.sub.meas,i, i=[1,n]) is greater than D.sub.ref, and wherein the indicator of image data heterogeneity is the ensemble of estimated ROI subregion sizes (SR.sub.meas,i, i=[1,n]) and/or corresponding measures of image data heterogeneity (D.sub.meas,i, i=[1,n])”, in all the claim which is not found in the prior art references. It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitation supra, when read in light/combination of the other claimed limitation within the cited claims. Also, it is noted that the quoted limitations, in combination with the other claim limitations of the cited claims, deem the claim patentable, not just the consideration of the quoted limitations by themselves.
Regarding Claim 11, the primary reason for the allowance of the claim is the inclusion of the limitation, “wherein the region of interest (ROI) is an ensemble of ROIs (ROI.sub.i, i=[1,n]), each ROI characterized by a measure of image data heterogeneity for a subregion size, and wherein the indicator of image data heterogeneity is an ensemble of measures of ROI image data heterogeneity (D.sub.meas,i, i=[1,n]) and/or an ensemble of subregion sizes (SR.sub.meas,i, i=[1,n]), and wherein the processor is further configured to map the elements of the ensemble of indicators of image data heterogeneity to their corresponding ROI.”, in all the claim which is not found in the prior art references. It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitation supra, when read in light/combination of the other claimed limitation within the cited claims. Also, it is noted that the quoted limitations, in combination with the other claim limitations of the cited claims, deem the claim patentable, not just the consideration of the quoted limitations by themselves.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Daerr et al US Patent(US 10507004 B2) discloses dark field x-ray image analysis and detecting x-ray attenuation when imaging a patient in the abstract.
Velroyen et al. (Grating-based X-ray Dark-field Computed Tomography of Living Mice) discloses a method of grating-based x-ray dark field image analysis in the abstract.
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/HAN HOANG/Examiner, Art Unit 2661