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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/22/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1-18 have been considered but are not persuasive for the following reasons. In reference to the interview summary of 5/6/2026, Examiner did further investigate the Ernst prior art to see if corrective action (i.e., data replacement) is performed once false motion is detected. Applicant’s position is Ernst does not carried out any corrective action. Upon further review, it is believed that Ernst does carried out data replacement upon false movement detection as one possibility (See par. 0032).
“[0032] In certain embodiments, the systems, methods, and devices described herein detect the presence of or a high likelihood of such false marker movements. Upon detection of false movements, the imaging or therapeutic apparatus can be configured to transiently suppress acquisitions and subsequently repeat acquisitions or reconstruct images with partially missing data.”
Applicant is also correct that in Ernst, once false motion is detected, motion correction process can proceed by avoiding it altogether. This is the preferred approach in Ernst, but it is not the only possibility as noted above.
For the reasons as stated above, the rejections of claims 1-9, 11-18 as established in the Final Office Action of 2/26/2026 is maintained and amended claim language in independent claims 1, 13, 15, and 16 from “corrected to” to “replaced by” is sufficient addressed above in the response to remarks.
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 1-9, 11-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US20170032538A1 (Ernst et al).
Regarding claim 1 (Currently Amended), Ernst et al teaches:
A subject motion measuring apparatus (Fig. 1A) comprising: at least one memory storing a program; and at least one processor which, by executing the program, causes the subject motion measuring apparatus (par. 0093) to: measure motion of a subject (Abstract) and output motion information of the subject with a plurality of degrees of freedom (Fig. 9; par. 0084 i.e., 6-DOF), wherein the motion information of the subject includes an error caused by a disturbance other than the motion of the subject (Abstract; pars. 0005, 0007, 0040, 0084 i.e., false motion such as skin movement of the patient and not rigid body movement); and output a corrected motion information by using a trained model (Figs.4-5, 8; pars. 0059-0062; 0075, 0077, 0082 i.e., machine learning e.g., neural networks, AI are utilized as “movement discriminators” to mitigate “false” motion), wherein the corrected motion information is obtained by reducing the error from the motion information, wherein the trained model has functions of receiving the motion information and outputting the corrected motion information with the plurality of degrees of freedom in which the error is reduced (pars. 0059-0062; 0075, 0077, 0082 i.e., rigid body movements are corrected and false movements are discriminated to reduce unnecessary motion corrections), and wherein the corrected motion information is information in which a data point including the error in the motion information of the subject is replace by to a data point at which the error has been reduced (par. 0032 as noted in the response to remarks above).
Regarding claim 2 (Currently Amended), Ernst et al further teaches:
The subject motion measuring apparatus according to claim 1, wherein(See rejection of claim 1 above i.e., 6-DOF and Fig. 9).
Regarding claim 3 (Currently Amended), Ernst et al further teaches:
The subject motion measuring apparatus according to claim 1, wherein the trained model is trained by using a set of time-series data representing the motion information the time-series data being obtained for a predetermined period of time (Rejection of claim 1 incorporated herein; pars. 0080-0082 i.e., velocity-based (time-series) false motion discriminator; false movement tracked during “a particular period of time” and motion correction happens during said particular time period at scanner control module 822).
Regarding claim 4 (Currently Amended), Ernst et al further teaches:
The subject motion measuring apparatus according to claim 3, wherein the time-series data obtained for the predetermined period of time includes the data point including the error and past data before the data point including the error (Rejection of claim 3 is incorporated herein. Claim 4 is self-explanatory. In Ernst, velocity-based i.e. time-series motion tracking/discrimination for tracking rigid body and false motion is carried out during “a particular time period” during which “data points” discriminated would include false motion data point and data points prior to and after said false motion).
Regarding claim 5 (Currently Amended), Ernst et al further teaches:
The subject motion measuring apparatus according to claim 3, wherein the time-series data obtained for the predetermined period of time includes the data point including the error past data before the data point including the error and future data after the data point including the error (Rejections of claims 3 and 4 are incorporated herein. Again, claim 5 is self-explanatory. In Ernst, velocity-based i.e. time-series motion tracking/discrimination for tracking rigid body and false motion is carried out during “a particular time period” during which “data points” discriminated would include false motion data point and data points prior (past) to and after (future) said false motion).
Regarding claim 6 (Currently Amended), Ernst et al further teaches:
The subject motion measuring apparatus according to claim 4, wherein the at least one processor causes the subject motion measuring apparatus to use the motion information obtained by measuring the motion of the subject as the past data before the data point including the error (Rejection of claims 3 and 4 are incorporated herein. Again, claim 6 is self-explanatory. In Ernst, rigid body motion and false motion are continuously tracked/discriminated during “a particular time period”. Thus, subject motion i.e., rigid body motion will certainly be detected prior to false motion is detected/discriminated).
Regarding claim 7 (Currently Amended), Ernst et al further teaches:
The subject motion measuring apparatus according to claim 4, wherein the at least one processor causes the subject motion measuring apparatus to use the corrected motion information as the past data before the data point including the error (Rejection of claims 3 and 4 are incorporated herein. Again, claim 7 is self-explanatory. In Ernst, rigid body motion is considered “correct motion” and false motion is considered “error” motion, both are continuously tracked/discriminated during “a particular time period”. Thus, subject motion or rigid body motion as “correct motion” will certainly be detected prior to false motion is detected/discriminated).
Regarding claim 8 (Currently Amended), Ernst et al further teaches:
The subject motion measuring apparatus according to claim 1, wherein the trained model is trained by supervised learning using learning data that includes error- containing data, which is motion information containing the error, and training data, which is motion information not containing the error (Rejection of claim 1 is incorporated herein; see Figs.4-5, 8; pars. 0059-0062; 0075, 0077, 0082 i.e., machine learning e.g., neural networks, AI are utilized as “trained” models to track/discriminate subject motion or rigid body motion and “false” motion).
Regarding claim 9 (Currently Amended), Ernst et al further teaches:
The subject motion measuring apparatus according to claim 8, wherein the error-containing data is data generated by adding error information generated by simulating a disturbance to motion information not containing the error (Rejections of claims 1 and 8 are incorporated herein; see Figs.4-5, 8; pars. 0059-0062; 0075, 0077, 0082 i.e., machine learning e.g., neural networks, AI are utilized as “trained” models to track/discriminate subject motion or rigid body motion from “false” motion which should not be accounted for during correction).
Regarding claim 11 (Original), Ernst et al further teaches:
The subject motion measuring apparatus according to claim 1, wherein the at least one processor causes the subject motion measuring apparatus to measure motion of a human head as the motion of the subject (Fig. 2; par. 0039-0040).
Regarding claim 12 (Original), Ernst et al further teaches:
The subject motion measuring apparatus according to claim 11, wherein the error caused by the disturbance includes an error caused by a vibration of the subject motion measuring apparatus or an error caused by a movement of skin of the subject (Pars. 0040, 0054-0055).
Regarding claim 13, the scope is substantially the same as claim 1 except for the “medical image diagnostic apparatus” environment as recited. Thus, the rejection of claim 1 is fully incorporated herein. Moreover, Ernst teaches said “medical image diagnostic apparatus” environment as claimed (Abstract).
Regarding claim 14 (Original), Ernst et al further teaches:
The medical image diagnostic apparatus according to claim 13, wherein the medical image diagnostic apparatus is a magnetic resonance imaging apparatus (Par. 0006).
Regarding claim 15 (Currently Amended), which is essentially claim 1 except for a different statutory category of “a method”. Thus, implementing the apparatus claim 1 would essentially carry out the recited steps in claim 15. Therefore, the rejection of claim 1 is fully incorporated herein.
Regarding claim 16 (Currently Amended), which is essentially claim 1 or 15 except for a different statutory category of “a non-transitory computer readable medium” that stores a program when executed by a computer would essentially carry out the steps of claim 15. Therefore, the rejections of claim 1 and 15 are fully incorporated herein.
Regarding claim 17 (Previously Presented), Ernst et al further teaches:
The medical image diagnostic apparatus according to claim 14, wherein the at least one processor further causes the subject motion measuring apparatus to: generate a gradient magnetic field; and control a gradient magnetic field based on the corrected motion information (Pars. 0003, 0006-0007; Note: gradient magnetic field is inherent in MRI imaging).
Regarding claim 18 (Previously Presented), Ernst et al further teaches:
The medical image diagnostic apparatus according to claim 14, wherein the at least one processor further causes the subject motion measuring apparatus to: receive a magnetic resonance signal; and construct an image based on the magnetic resonance signal and the corrected motion information (Pars. 0003-0007; Note: the motion tracking/discrimination in Ernst for used in MRI scanning for the same purpose as claimed).
Allowable Subject Matter
Claim 10 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 best prior art of record, US20170032538A1 (Ernst et al), teaches everything as set forth in the aforementioned 102 rejection. However, Ernst et al does not teach the following limitations as further recited in claim 10.
“wherein the at least one processor causes the subject motion measuring apparatus to calculate three- dimensional coordinates of feature points of the subject from an image captured by a camera imaging the subject and convert the three-dimensional coordinates of the feature points into the motion information, and the error-containing data is data generated by adding the error information to the three- dimensional coordinates of the feature points in motion not containing an error and subsequently converting the three-dimensional coordinates of the feature points to which the error information is added into motion information with a plurality of degrees of freedom by using an algorithm identical to an algorithm used by calculating the three-dimensional coordinates of the feature points.”
Examiner’s Comment: A follow up interview is extended to the applicant for discussion on ways to incorporate the aspect of claim 10 into independent form for prosecution advancement. Examiner recognizes that the aspect of claim 10 may not need to follow the dependency chain of claims 9 and 8 as currently recited.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VU LE whose telephone number is (571)272-7332. The examiner can normally be reached M-F 8:00 - 17:00.
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/VU LE/Supervisory Patent Examiner, Art Unit 2668
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