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 § 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.
Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhai (US-20180246179-A1) in view of Liu (US-20200256936-A1).
Regarding claim 1
Zhai discloses
A magnetic resonance examination system ([0001]) comprising:
an RF transmit system with RF antenna elements and an RF driver system to activate the RF antenna elements for applying a (B₁) radio frequency field having a predetermined spatial distribution ([0002], the predetermined spatial distribution is the “volume of interest”);
an RF shim system to control the RF driver system to apply shim radio
frequency fields to correct for deviation of the radio frequency field’s spatial
distribution from the predetermined spatial distribution on the basis of RF-shim settings; and ([0006], the RF system creates the B.sub.1 signal plus a shimming component)
Zhai does not disclose
“a trained machine-learning module trained to return the RF-shim settings
from one or more actual load parameters”.
Liu, however, teaches
a trained machine-learning module trained to return the RF-shim settings from one or more actual load parameters ([0160], the “load parameters” are B.sub.0 field of the specific heart region that is “shimmed” since that is the region being imaged).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “trained module returning RF shim settings from load parameters” as taught by Liu in the system of Zhai.
The justification for this modification would be to update the shim parameters on a case-by-case/patient-by-patient scenario.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Punchard et al. (US-20110260727-A1) in view of Liu (US-20200256936-A1) in view of Taerum et al. (US-20200085382-A1).
Regarding claim 11
Punchard discloses
A method to return B0-shim settings for an active shim system to apply shim magnetic fields to correct for inhomogeneities of a static magnetic field ([0130]) from one or more actual load parameters that are determined from images of an examination zone with the patient to be examined in position ([0054], the patient in
the bore is a part of the load parameters; each patient/load is different, and the inhomogeneities have to be corrected on a patient-to-patient basis),
Punchard does not disclose
“machine trained learning module in which the training is based on log-file information of a magnetic resonance examination system or from an installed base of multiple magnetic resonance examination systems”.
Liu, however, teaches
a machine trained learning module (Claim 1)
Punchard in view of Liu do not disclose
“training is based on log-file information of a magnetic resonance examination system or from an installed base of multiple magnetic resonance examination systems”.
Taerum, however, teaches
training is based on log-file information of a magnetic resonance examination system or from an installed base of multiple magnetic resonance examination systems ([0410]—[0413], the training is based on metadata stored in the MRI machine in logs/files).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “machine trained
learning module” of Liu as well as the “log file information of MRI system” as taught by Taerum in the method of Punchard.
The justification for this modification would be to 1) to add a machine trained learning module to quickly processes new developing parameters, and 2) use the newly logged information of changing parameters to update the learning module to make the machine more flexible and faster with changing imaging conditions.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Punchard et al. (US-20110260727-A1) in view of Liu (US-20200256936-A1) in view of Taerum et al. (US-20200085382-A1) in view of Chalom et al. (US-20170308734-A1).
Regarding claim 12
Punchard in view of Liu in view of Taerum teach the method of claim 11,
Punchard in view of Liu in view of Taerum do not teach
“wherein the training employs at least one from a group consisting at least of a random forest generator or a neural network”.
Chalom, however, teaches
wherein the training employs at least one from a group consisting at least of a random forest generator or a neural network ([0075]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “random forest generator” as taught by Chalom in the method of Punchard in view of Liu in view of Taerum.
The justification for this modification would be to improve the predictive accuracy of the neural network when gathering and processing data.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Punchard et al. (US-20110260727-A1) in view of Liu (US-20200256936-A1) in view of Taerum et al. (US-20200085382-A1) in view of Feiweier (DE-102015225080-A1).
Regarding claim 13
Punchard in view of Liu in view of Taerum teach the method of claim 11,
Punchard in view of Liu in view of Taerum do not disclose
“wherein a training data set generated from the log-file information is updated according to a validation of returned B0-shim settings on the basis of an
analysis of the actually achieved B0-mapping in a preparatory signal acquisition carried out with the returned the B0-shim settings”.
Feiweier, however, teaches
wherein a training data set generated from the log-file information is updated according to a validation of returned B0-shim settings on the basis of an analysis of
the actually achieved B0-mapping in a preparatory signal acquisition carried out with the returned the B0-shim settings (¶ 28 – 30 under Description).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “validating shim settings based on return values” as taught by Feiweier in the method of Punchard in view of Liu in view of Taerum.
The justification for this modification would be to optimize shimming based on dynamic machine adjustment.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Punchard et al. (US-20110260727-A1) in view of Liu (US-20200256936-A1) in view of Taerum et al. (US-20200085382-A1) in view of Kongquiao et al. (US-8194921-B2).
Regarding claim 14
Punchard in view of Liu in view of Taerum teach the method of claim 11,
Punchard in view of Liu in view of Taerum do not teach
“wherein,
a reverse operation machine learning module returns region-of-interest data from the returned RF settings; and
a consistency analysis is made of the training dataset on the basis of the region-of-interest data compared with the actual load parameters”.
Kongquiao, however, teaches
wherein,
a reverse operation machine learning module returns region-of-interest data from the returned RF settings; and
a consistency analysis is made of the training dataset on the basis of the region-of-interest data compared with the actual load parameters ( ¶ 40 under (13) DETAILED DESCRIPTION OF SOME EMBODIMENTS OF THE INVENTION).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “consistency analysis” as taught by Kongquiao in the method of Punchard in view of Liu in view of Taerum.
The justification for this modification would be to improve imaging quality.
Regarding claim 15
Punchard in view of Liu in view of Taerum teach the method of claim 11,
Punchard in view of Liu in view of Taerum do not teach
“wherein, the consistency analysis log file data employed for the training are tagged”.
Allowable Subject Matter
Claims 2 – 10 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 2
Nothing in the prior art of record teaches or discloses
“wherein the radio frequency shim system is configured to perform a validation of the returned RF-shim settings on the basis of an analysis of the actually achieved B₁⁺-RF-field in a preparatory signal acquisition carried out
with the returned RF settings”.
In conjunction with the rest of the claim language.
Regarding claims 3 – 5
The claims are allowable due to their dependencies on objected-to claim 2.
Regarding claim 6
Nothing in the prior art of record teaches or discloses
“wherein the trained machine-learning module returns the B0-shim settings from one or more actual load parameters, wherein
the actual load parameters are determined from images of the examination zone with the patient to be examined in position.
In conjunction with the rest of the claim language.
Regarding claims 7 – 10
The claims are allowable due to their dependencies on objected-to claim 6.
Regarding claim 16
Nothing in the prior art of record teaches or discloses
“a trained machine-learning module trained to return the center frequency setting from imaging circumstances aspects”.
In conjunction with the rest of the claim language.
Regarding claims 17, 18
The claims are allowable due to their dependencies on objected-to claim 16.
Regarding claim 19
Nothing in the prior art of record teaches or discloses
“driving a radio frequency (RF) transmit and receive system (T/R) to transmit and RF field and to acquire magnetic resonance signals causing an adjustable centre frequency of the RF (T/R) system’s RF resonance frequency bandwidth set to the return centre frequency”.
In conjunction with the rest of the claim language.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDERICK WENDEROTH whose telephone number is (571)270-1945. The examiner can normally be reached M-F 7 a.m. - 4 p.m.
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/WALTER L LINDSAY JR/Supervisory Patent Examiner, Art Unit 2852
/Frederick Wenderoth/
Examiner, Art Unit 2852
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