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 .
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 20-23, 25, 27, 30-32, 34, and 37-39 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Taki (US 2023/0017704 A1, Jan. 19, 2023) (hereinafter “Taki”).
Regarding claim 20: Taki discloses a non-transitory computer-readable storage medium storing a program causing a computer to execute processes of: acquiring an X-ray image of a target site ([0043]-[0045] - images G0, G1, and G2) and a CT (Computed Tomography) image of the target site ([0046] - CT image V0); aligning a position in a 3D space of the target site based on the CT image and a position on a 2D image of the target site based on the X-ray image ([0083] - the subject is included in a three-dimensional space represented by image V0; [0084]-[0085] - CT value and attenuation coefficient is determined for every voxel in V0 represented by V(x,y,z); [0087]-[0089] - the values of V0 at all voxels V(x,y,z) are mapped to a 2D position, and the mapped 2D position is aligned with X-ray images G1 and G2); acquiring training data including the X-ray image of the target site and information related to an amount of body tissue of the target site obtained from the CT image of the target site ([0045]-[0046] - CT image V0 and x-ray images G1 and G2; [0047], [0057] - “teacher data”, [0063]-[0065], [0089], [0095]); and generating a learning model configured to output information related to an amount of body tissue of a target site in an X-ray image when the X-ray image is input using acquired training data ([0047], [0058], [0060]-[0063]).
Regarding claim 21: Taki discloses the non-transitory computer-readable storage medium according to claim 20, wherein the program causes the computer to execute a process of generating the learning model configured to output an image representing an amount of the body tissue of the target site when the X-ray image is input ([0060], [0099] - display image region 71, fig. 17).
Regarding claim 23: Taki discloses the non-transitory computer-readable storage medium according to claim 20, wherein the program causes the computer to execute processes of: classifying the target site in the CT image into a plurality of regions including a bone region and a muscle region based on the CT image ([0083], [0092]); acquiring the training data including information related to bone density of the classified bone region in the CT image ([0092]-[0093]); and generating the learning model configured to output information related to bone density of the bone region in an X-ray image when the X-ray image is input using the training data ([0047], [0058], [0060]-[0063]).
Regarding claim 25: Taki discloses the non-transitory computer-readable storage medium according to claim 20, wherein the program causes the computer to execute processes of: specifying a bone region in the CT image and a bone region in the X-ray image ([0090], [0075]); generating, based on the CT image, a CT image of the bone region viewed in a direction matching a capturing direction of the bone region in the X-ray image ([0087]-[0089] where image CG is generated based on the CT image V0); and by aligning a position of a bone region in the generated CT image and a position of the bone region in the X-ray image, aligning the position of the target site based on the CT image and the position of the target site based on the X-ray image ([0089]).
Regarding claim 27: Taki discloses the non-transitory computer-readable storage medium according to claim 20, wherein the program causes the computer to execute processes of: specifying a projection condition that maximizes a correlation value between an image obtained by projecting a bone region included in the target site in the CT image and a bone region included in the target site in the X-ray image ([0093]-[0094], [0103]); and acquiring the training data including information related to an amount of body tissue of the target site obtained from a projection image obtained by projecting the target site in the CT image under a specified projection condition ([0093]-[0094], [0103]).
Regarding claim 30: Taki discloses a non-transitory computer-readable storage medium storing a program causing a computer to execute processes of: acquiring an X-ray image of a target site ([0045]-[0046] - CT image V0 and x-ray images G1 and G2); and inputting the acquired X-ray image to a learning model trained using training data including an X-ray image of a target site and information related to an amount of body tissue obtained from a CT image of the target site and configured to output information related to an amount of body tissue of a target site in an X-ray image when the X-ray image is input, thereby outputting information related to an amount of body tissue of the target site ([0047], [0058], [0060]-[0063]), wherein the learning model is trained using training data including an X-ray image of a target site and information related to an amount of body tissue of the target site obtained from a CT image obtained after aligning a position in a 3D space of the target site based on the CT image and a position on a 2D image of the target site based on the X-ray image ([0083] - the subject is included in a three-dimensional space represented by image V0; [0084]-[0085] - CT value and attenuation coefficient is determined for every voxel in V0 represented by V(x,y,z); [0087]-[0089] - the values of V0 at all voxels V(x,y,z) are mapped to a 2D position, and the mapped 2D position is aligned with X-ray images G1 and G2; [0095]).
Regarding claim 31: Taki discloses the non-transitory computer-readable storage medium according to claim 30, wherein the output information related to the amount of body tissue is an image representing an amount of body tissue of the target site ([0060], [0099] - display image region 71, fig. 17).
Regarding claim 32: Taki discloses the non-transitory computer-readable storage medium according to claim 30, wherein the output information related to the amount of body tissue is bone density of the target site or muscle mass of the target site ([0060], [0099] - bone density display region 72, fig. 17).
Regarding claim 34: Taki discloses the non-transitory computer-readable storage medium according to claim 30, wherein the learning model is trained using the training data including information related to an amount of body tissue of the target site obtained from a CT image generated for the bone region viewed in a direction matching a capturing direction of a bone region specified in the X-ray image before alignment in which a position of the target site based on the generated CT image is aligned with a position of the target site based on the X-ray image by aligning a bone region in the generated CT image with the bone region in the X-ray image ([0087]-[0089]).
Regarding claim 37: Taki discloses an information processing apparatus comprising a control unit, wherein the control unit is configured to: acquire an X-ray image of a target site ([0045]-[0046] - CT image V0 and x-ray images G1 and G2); and input the acquired X-ray image to a learning model trained using training data including an X-ray image of a target site and information related to an amount of body tissue obtained from a CT image of the target site and configured to output information related to an amount of body tissue of a target site in an X-ray image when the X-ray image is input, thereby outputting information related to an amount of body tissue of the target site ([0047], [0058], [0060]-[0063]), wherein the learning model is trained using training data including an X-ray image of a target site and information related to an amount of body tissue of the target site obtained from a CT image obtained after aligning a position in a 3D space of the target site based on the CT image and a position on a 2D image of the target site based on the X-ray image ([0083] - the subject is included in a three-dimensional space represented by image V0; [0084]-[0085] - CT value and attenuation coefficient is determined for every voxel in V0 represented by V(x,y,z); [0087]-[0089] - the values of V0 at all voxels V(x,y,z) are mapped to a 2D position, and the mapped 2D position is aligned with X-ray images G1 and G2; [0095]).
Regarding claim 38: Taki discloses an information processing apparatus comprising a control unit, wherein the control unit is configured to: acquire an X-ray image of a target site ([0045]-[0046] - CT image V0 and x-ray images G1 and G2); and input the acquired X-ray image to a learning model trained using training data including an X-ray image of a target site and information related to an amount of body tissue obtained from a CT image of the target site and configured to output information related to an amount of body tissue of a target site in an X-ray image when the X-ray image is input, thereby outputting information related to an amount of body tissue of the target site ([0047], [0058], [0060]-[0063]), wherein the learning model is trained using training data including an X-ray image of a target site and information related to an amount of body tissue of the target site obtained from a CT image obtained after aligning a position in a 3D space of the target site based on the CT image and a position on a 2D image of the target site based on the X-ray image ([0083] - the subject is included in a three-dimensional space represented by image V0; [0084]-[0085] - CT value and attenuation coefficient is determined for every voxel in V0 represented by V(x,y,z); [0087]-[0089] - the values of V0 at all voxels V(x,y,z) are mapped to a 2D position, and the mapped 2D position is aligned with X-ray images G1 and G2; [0095]).
Regarding claim 39: Taki discloses the information processing method according to claim 37, wherein the learning model is trained using the training data including information related to an amount of body tissue of the target site obtained from a CT image generated for the bone region viewed in a direction matching a capturing direction of a bone region specified in the X-ray image before alignment in which a position of the target site based on the generated CT image is aligned with a position of the target site based on the X-ray image by aligning a bone region in the generated CT image with the bone region in the X-ray image ([0087]-[0089]).
Allowable Subject Matter
Claims 24, 26, 28-29, 33, and 35-36 are allowed.
Response to Arguments
Applicant’s arguments with respect to prior art rejection of claims 20-23, 25, 27, 30-32, 34, and 37-39, filed 06/10/2026, have been fully considered but are moot in view of the updated grounds of rejection necessitated by amendment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAROLYN A PEHLKE whose telephone number is (571)270-3484. The examiner can normally be reached 9:00am - 5:00pm (Central Time), Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Koharski can be reached at (571) 272-7230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CAROLYN A PEHLKE/Primary Examiner, Art Unit 3799
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