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 .
Response to Arguments
Applicant's arguments filed on January 6, 2026 have been fully considered and they are not persuasive. Applicant argues that limitations that have been added to independent claims 1 and 19 by the instant amendment are not disclosed in Honjo. Specifically, Applicant argues that Honjo does not disclose ''set a plurality of candidate regions on a medical image obtained by using a plurality of X-ray energies, as candidates of a region of interest to be set on the medical image," or ''execute bone mineral density measurement for each of the plurality of candidate regions;'' and ''output a plurality of bone mineral densities and the plurality of candidate regions in association with each other." The examiner agrees, and therefore the rejection of claims 1, 3-4, 6, 7, 9, 11, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Publ. Appl. No. 2018/0025492 A1 by Honjo is withdrawn. However, upon further consideration, a new ground of rejection is made in view of U.S. Publ. Appl. No. 2020/0022665 to Shinden et al., as set forth below.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) 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 BRI of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
BRIs for some of the claim limitations are provided below. These BRIs are used for purposes of searching for prior art, but cannot be incorporated into the claims. Should applicant believe that different BRIs are warranted, Applicant should point to the portions of the specification that clearly show support for a different interpretation.
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.
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, 3-4, 6, 7, 9, 11, 13-14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publ. Appl. No. 2018/0025492 A1 by Honjo et al. (hereinafter referred to as “Honjo”) in view of U.S. Publ. Appl. No. 2020/0022665 A1 to Shinden et al. (hereinafter referred to as “Shinden”).
Regarding claim 1, Honjo et al. discloses a medical image processing apparatus (para. [0020], Fig. 1, the analyzing apparatus is a medical image diagnosis apparatus) comprising:
a processor (Fig. 1, processing circuitry 160, paras. [0043]-[0044]); and
a memory (Fig. 1, storage circuitry 150, paras. [0043]-[0044]), including instructions stored thereon, which when executed by the processor cause the apparatus to:
set a plurality of candidate regions on a medical image obtained by using a plurality of X-ray energies, as candidates of a region of interest to be set on the medical image (Honjo does not disclose the underlined portion of this limitation; Fig. 1, paras. [0054]-[0056], index value calculating function 161 divides a firmness image into a plurality of candidate sub-regions and calculates an index value associated with each candidate sub-region; each index value is related to a variance among tissue characteristic parameter values; para. [0096] and Fig. 8A, the determining function 162 compares the index values calculated for each candidate sub-region to a threshold (TH) value to set a plurality of candidate regions of interest (ROIs), R2-R6 in Fig. 8A);
execute bone mineral density measurement for each of the plurality of candidate regions (Honjo does not disclose the underlined portion of this limitation; para. [0109], the statistic value calculating function 163 calculates a statistical value for each of the candidate ROIs: “[f]or instance, a statistic value may be calculated for each of all the measurement candidate ROIs. With this arrangement, for example, the operator is able to select a measurement ROI from among the measurement candidate ROIs, by referring to the statistic value of each of the measurement candidate ROIs);
output a plurality of bone mineral densities and the plurality of candidate regions in association with each other (Honjo does not disclose the underlined portion of this limitation; paras. [0106]-[0110], Figs. 1 and 9, display controlling function 164 constitutes an output unit that causes the plurality of candidate ROIs along with their associated statistic values to be displayed on display 103 to allow the operator to select one of the candidate ROIs based on the statistic value; Honjo does not disclose the underlined portion of this limitation);
accept, from a user who has referred to the plurality of bone mineral densities outputted, an operation of selecting one of the plurality of candidate regions as the region of interest (Honjo does not disclose the underlined portion of this limitation; paras. [0027]-[0028], [0042] and [106]-[0110], the processing circuitry 160 receives signals from the input device 102 based on the operator referencing the plurality of statistic values displayed with the candidate ROIs and selecting one of the candidate ROIs as the measurement ROI; para. [0106] discusses the display controlling function 164 of the processing circuitry 160 displaying the candidate ROIs for the user/operator to reference and para. [0107] discusses the determining function 162 of the processing circuitry 160 receiving “from the operator, an operation to select the measurement ROI from among the plurality of measurement candidate ROIs displayed over the display ROI. When having received the operation (step 207: Yes), the determining function 162 determines the measurement candidate ROI selected by the operation as a measurement ROI.” See also para. [0109]: “[w]ith this arrangement, for example, the operator is able to select a measurement ROI from among the measurement candidate ROIs, by referring to the statistic value of each of the measurement candidate ROIs”); and
output a report including the bone mineral densities to the selected region of interest (Honjo does not disclose the underlined portion of this limitation; para. [0108] of Honjo discloses that when the user/operator selects the measurement ROI, the statistical calculating function 163 of the processing circuitry 160 calculates an average of the shear value of the selected measurement ROI and causes it to be displayed to the user/operator. The display of the statistic value of the measurement ROI constitutes outputting a report including the analysis results corresponding to the selected region of interest).
As indicated above, Honjo does not disclose that the plurality of candidate regions that are set on the medical image are obtained by using a plurality of X-ray energies or that the measurements that are made are bone density measurements. However, Honjo explicitly discloses that the methodology of Honjo is applicable to various types of X-ray apparatuses and diagnoses (para. [0022]: “[i]n the embodiments described below, an ultrasound diagnosis apparatus will be explained as an example of the analyzing apparatus. However, possible embodiments are not limited to this example. For instance, as the analyzing apparatus, other medical image diagnosis apparatuses besides ultrasound diagnosis apparatuses are also applicable, such as X-ray diagnosis apparatuses, X-ray Computed Tomography (CT) apparatuses, Magnetic Resonance Imaging (MRI) apparatuses, Single Photon Emission Computed Tomography (SPECT) apparatuses, Positron Emission computed Tomography (PET) apparatuses, SPECT-CT apparatuses in which a SPECT apparatus and an X-ray CT apparatus are integrated together, PET-CT apparatuses in which a PET apparatus and an X-ray CT apparatus are integrated together, or a group made up of any of these apparatuses.”).
Shinden, in the same field of endeavor, discloses using Dual-Energy X-ray Absorptiometry (DEXA) to measure bone mineral densities by setting a plurality of regions of interest (ROIs) on a medical image obtained by using a plurality of X-ray energies and executing bone mineral density measurement for each of the plurality of ROIs (para. [0099] discusses using DEXA to obtain bone mineral densities; para. [0090] discusses measuring several bone characteristics such as bone strength, bone mineralization degree, bone density, etc.; para. [0098] discusses setting a plurality of ROIs by dividing a measurement target ROI into smaller ROIs).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the system and process of Honjo based on the teachings of Shinden to use a DEXA system and process to obtain bone mineral density measurements as taught by Shinden. One of ordinary skill in the art would have been motivated to make the modification to allow bone mineral density assessments of patients to be performed by the system of Honjo since Honjo discusses applying the processes disclosed therein to X-ray modalities and diagnoses. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods (modifying the system and processes of Honjo to perform DEXA) to yield predictable results.
Regarding claim 3, Honjo discloses that when setting a plurality of regions of interest on the medical image, the processing circuitry 160 sets the plurality of candidate regions for each of the regions of interest, and the analysis unit executes the quantitative analysis for each combination of the candidate regions (Honjo does not explicitly disclose that the quantitative analysis is a bone mineral density measurement; Fig. 8A, each Display ROI encompasses a plurality of the candidate ROIs R1-R6 that are set for each Display ROI; the quantitative analysis, i.e., the calculation of the statistic values by the statistic value calculating function 163, can be performed for each of the candidate ROIs: para. [0109], “[f]or instance, a statistic value may be calculated for each of all the measurement candidate ROIs”; para. [0073], there can be a plurality of measurement ROIs selected from the candidate ROIs, and when a plurality of measurement ROIs are selected for display, statistic values are calculated for the combination of the measurement ROIs).
As indicated above in the rejection of claim 1, Shinden discloses using Dual-Energy X-ray Absorptiometry (DEXA) to measure bone mineral densities by setting a plurality of regions of interest (ROIs) on a medical image obtained by using a plurality of X-ray energies and executing bone mineral density measurement for each of the plurality of ROIs.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the system and process of Honjo based on the teachings of Shinden to use a DEXA system and process to obtain bone mineral density measurements as taught by Shinden for each combination of the candidate ROIs. One of ordinary skill in the art would have been motivated to make the modification to allow bone mineral density assessments of patients to be performed by the system of Honjo since Honjo discusses applying the processes disclosed therein to X-ray modalities and diagnoses. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods (modifying the system and processes of Honjo to perform DEXA) to yield predictable results.
Regarding claim 4, Honjo discloses that the processing circuitry 160 inputs the medical image to a learned model that is activated to accept input of the medical image and estimate the region of interest, and sets a region output from the learned model as an estimation result of the region of interest to one of the plurality of candidate regions (paras. [0127]-[0128], the index value calculating function 161 and the determining functions 162 can be implemented as a trained machine learning model that receives medical images as input, sets candidate ROIs as an estimation result).
Regarding claim 6, Honjo discloses that the processing circuitry 160 performs threshold-based processing for a pixel value of the medical image, thereby setting at least one of the plurality of candidate regions (paras. [0096]-[0102], the index value calculating function 161 calculates the index values and the determining function 162 compares them to a TH value to determine which sub-region candidates are to be set as candidate ROIs).
Regarding claim 7, Honjo discloses that the processing circuitry 160 performs a plurality of threshold-based processes by changing a threshold, thereby setting at least two of the plurality of candidate regions (paras. [0119]-[0126]).
Regarding claim 9, Honjo discloses that the processing circuitry 160 sets a region created by a user operation to one of the plurality of candidate regions (paras. [0109]-[0110]: “[f]or instance, a statistic value may be calculated for each of all the measurement candidate ROIs. With this arrangement, for example, the operator is able to select a measurement ROI from among the measurement candidate ROIs, by referring to the statistic value of each of the measurement candidate ROIs”).
Regarding claim 11, Honjo discloses that the processing circuitry 160 further displays images representing the candidate regions in association with the plurality of analysis results (paras. [0106]-[0110], Figs. 1 and 9, display controlling function 164 constitutes an output unit that causes the plurality of candidate ROIs along with their associated statistic values to be displayed on display 103 to allow the operator to select one of the candidate ROIs based on the statistic value).
Honjo does not explicitly disclose that the analysis results that are displayed in associated with the candidate ROIs are bone mineral density measurement. As indicated above in the rejection of claim 1, Shinden discloses using Dual-Energy X-ray Absorptiometry (DEXA) to measure bone mineral densities by setting a plurality of regions of interest (ROIs) on a medical image obtained by using a plurality of X-ray energies and executing bone mineral density measurement for each of the plurality of ROIs. Para. [0078] of Shinden discloses that the bone mineral density measurements are displayed on display unit 23 shown in Fig. 1.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the system and process of Honjo based on the teachings of Shinden to use a DEXA system and process to display bone mineral density measurements as taught by Shinden in association with the candidate ROIs. One of ordinary skill in the art would have been motivated to make the modification to allow bone mineral density assessments of patients to be displayed to a healthcare provider to help the healthcare provider better diagnose and treat patient conditions. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods (modifying the system and processes of Honjo to perform DEXA and display the bone mineral density results) to yield predictable results.
Regarding claim 13, Honjo does not explicitly disclose that the instructions, when executed by the processor further cause the apparatus to detect an abnormal value candidate from the plurality of bone mineral densities. Shinden discloses that the image processing device 2 shown in Fig. 1 “calculates bone characteristic indicators and bone strength indicators from the acquired reconstructed images, and displays those indicators on a display unit 23.” As indicated above, these bone characteristics include bone mineral densities. Shinden discloses that these measured indicators are compared to associated standard values and displays the corresponding comparison, which constitutes detecting an abnormality in cases where the indicator value is determined to be less than or greater than the standard value (paras. [0134]-[0136]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the system and process of Honjo based on the teachings of Shinden to use a DEXA system and process to detect bone mineral density abnormalities as taught by Shinden. One of ordinary skill in the art would have been motivated to make the modification to allow bone mineral density abnormalities to be detected to aid a healthcare provider in diagnosing and treating patient conditions. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods (modifying the system and processes of Honjo to perform DEXA and display the bone mineral density results) to yield predictable results.
Regarding claim 14, Honjo does not explicitly disclose that the instructions, when executed by the processor further cause the apparatus to display an image representing the candidate region associated with the abnormal value candidate. In Shinden, once the bone mineral density indicators are generated and the comparisons to standard values have been determined to detect abnormalities, the indicators and the comparison results are displayed (paras. [0136]-[0137]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the system and process of Honjo based on the teachings of Shinden to use a DEXA system and process to detect bone mineral density abnormalities as taught by Shinden and cause the abnormalities to be displayed with the image representing the candidate region. One of ordinary skill in the art would have been motivated to make the modification to allow bone mineral density abnormalities to be detected to aid a healthcare provider in diagnosing and treating patient conditions. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods (modifying the system and processes of Honjo to perform DEXA and display the bone mineral density results and comparisons to standard values and cause the results to be displayed along with the corresponding ROI) to yield predictable results.
Regarding claim 19, the rejection of claim 1 applies mutatis mutandis to claim 19.
Regarding claim 20, Honjo discloses a non-transitory computer-readable storage medium storing a program for causing a computer to execute the method (para. [0043], storage circuitry 150 stores processing functions performed by functions 161, 162, 163 and 164).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Honjo in view of Shinden as applied to claims 1, 3-4, 6, 7, 9, 11, 13, 14, 19 and 20 and further in view of U.S. Pat. No. 12,051,206 B2 to Chen et al. (hereinafter referred to as “Chen”).
Regarding claim 5, Honjo does not explicitly disclose that more than one machine learning model can be used and that the medical images can be input to each of a plurality of machine learning models. Chen, in the same field of endeavor, discloses that an input image can be input to each of a plurality of machine learning models (Chen et al., Fig. 2, models 220, 230, 240 and 250, Col. 10, lines 44-Col. 12, line 41-Col. 13, line 2).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the processing circuitry 160 of Honjo to use a plurality of machine learning models as taught by Chen instead of a single machine learning model to perform the functions 161, 162 of Honjo of setting the candidate ROIs. One of ordinary skill in the art would have been motivated to make the modification to improve the accuracy of the estimations of the candidate ROIs. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods (modifying the machine learning model software of Honjo corresponding to functions 161 and 162 to implement a plurality of parallel models) to yield predictable results.
Claims 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Honjo in view of Shinden as applied to claims 1, 3-4, 6, 7, 9, 11, 19 and 20 and further in view of U.S. Pat. No. 8,280,133 B2 to Wels et al. (hereinafter referred to as “Wels”).
Regarding claim 8, Honjo does not explicitly disclose that the processing circuitry 150 performs graph cut processing for the medical image, thereby setting at least one of the plurality of candidate regions. Wels, in the same field of endeavor, discloses performing graph cut processing for brain tumor segmentation in 3D magnetic resonance images (Wels et al., Col. 2, line 42-Col. 3, line 15).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the processing circuitry 160 of Honjo to use the graph cutting processing of Wels et al. to set the candidate regions in Honjo One of ordinary skill in the art would have been motivated to make the modification to improve the efficiency of obtaining the candidate ROIs. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods (modifying the software of Honjo corresponding to functions 161 and 162 to implement a graph cut processing algorithm) to yield predictable results.
Regarding claim 15, Honjo does not explicitly teach that the processing circuitry 160 displays the image representing the candidate region such that a change over time before and after a point of time of occurrence of the abnormal value candidate can be identified. The process of Wels et al. that is used to identify tumors is not limited with regard to the window of time over which it is performed. Performing the analysis of Wels et al. repeatedly over a window of time, such as weekly over a number of months or years, for example, would result in the ability to display changes in the candidate regions over time before and after a point in time when the tumor candidate was identified. Medical image analysis is often performed periodically over time such that images are captured before and after an abnormality occurred can be viewed to allow a physician to identify an abnormality and prescribe treatment to prevent or slow the progression.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the processing circuitry 160 of Honjo based on the teachings of Wels et al. to perform the analysis of Wels et al. periodically over time and to display the corresponding results such that a change in time of an abnormality over time before and after the abnormality is identified can be displayed. One of ordinary skill in the art would have been motivated to make the modification to allow the operator to identify an abnormality and prescribe treatment to prevent or slow its progression. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods (modifying the software of Honjo to store analysis results for the same patient and the same anatomical feature with time stamps to allow the changes over time to be displayed) to yield predictable results.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Honjo in view of Shinden as applied to claims 1, 3-4, 6-7, 9, 11 and 19-20 and further in view of U.S. Publ. Appl. No. 2017/0309021 A1 by Barnes et al. (hereinafter referred to as “Barnes”).
Honjo does not explicitly teach that the processing circuitry 160 sets at least one of the plurality of candidate regions by morphology processing. Barnes, in the same field of endeavor, discloses setting a plurality of candidate regions by morphology processing to determine liver tumor types (para. [0007], “[i]n some embodiments, the determining of co-localized ROIs comprises identifying one or more at least partially overlapping candidate ROIs corresponding to different markers. In some embodiments, the one or more at least partially overlapping candidate ROIs are determined by morphologically and/or logically processing the overlay masks”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the processing circuitry 160 of Honjo to set the candidate regions in Honjo using morphological processing to identify abnormalities (i.e., tumors) in the candidate regions as taught by Barnes et al. One of ordinary skill in the art would have been motivated to make the modification to benefit from the known enhancements in pattern recognition, image segmentation and feature extraction that can be achieved using morphological processing to identify abnormalities. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods (modifying the software of Honjo corresponding to functions 161 and 162 to implement morphological processing as taught by Barnes et al.) to yield predictable results.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Honjo in view of Shinden as applied to claims 1, 3-4, 6-7, 9, 11 and 19-20 and further in view of U.S. Publ. Appl. No. 2006/0245629 A1 by Huo et al. (hereinafter referred to as “Huo”).
Regarding claim 12, Honjo does not explicitly disclose that if the quantitative analysis is performed a plurality of times for the same object, the output unit displays the plurality of analysis results in association with a time base. Huo et al., in the same field of endeavor, discloses performing a quantitative analysis to detect lesions a plurality of times for the same object and displaying the plurality of analysis results (identifying lesion types based on the time/enhancement intensity curve analysis) in association with a time base (Huo et al., Figs. 2 and 7A-7J, paras. [0033]-[0036]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the processing circuitry 160 of Honjo to set the candidate regions in Honjo as modified based on the teachings of Shinden further based on the teachings of Hua to use morphological processing and to perform the bone miner density measurement a plurality of times for the same object as taught by Huo et al. and to display the analysis results in association with a time base as taught by Huo et al. One of ordinary skill in the art would have been motivated to make the modification to allow the operator to select the measurement ROI with greater accuracy by viewing the candidate ROI images as they change over time rather than viewing them at a snapshot in time. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods (modifying the software of Honjo corresponding to functions 163 and 164 to implement the time base analysis as taught by Huo et al.) to yield predictable results.
Allowable Subject Matter
Claim 16 is 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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 16, none of the prior art of record teaches, in combination with the limitations recited in the claims 1 and 13, setting an error range for each of the plurality of candidate regions based on a smaller candidate region and comparing the analysis result corresponding to each of the plurality of candidate regions with the error range set for the candidate region to thereby detect an abnormal value candidate.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL J SANTOS whose telephone number is (571)272-2867. The examiner can normally be reached M-F 9-5.
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/DANIEL J. SANTOS/ Examiner, Art Unit 2667
/MATTHEW C BELLA/ Supervisory Patent Examiner, Art Unit 2667
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