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, see applicant arguments/remarks, filed 04/08/2026, with respect to the previous 112 rejections have been fully considered and are persuasive. The previous 112 rejections have been withdrawn, except for claim 4. Please see below for further details.
Applicant’s arguments with respect to the prior art rejections of the independent claims have been considered but are moot because the new ground of rejection does not rely on the same prior art combination used in the prior rejection of record.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 4, the limitation “wherein the computer unit comprises a database with specified parameter values for the parameters of the imaging sequence with fixed step sizes between individual parameter values or has access to this database” is considered indefinite because it is unclear how the limitation “or” were to be used in the limitation. Specifically, the limitations of the claim are considered when the limitation “or has access to this database” is used.
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.
Claim is rejected under 35 U.S.C. 103 as being unpatentable over previously cited Thone (US 2022/0183637), in view of Magland (“Pulse Sequence Programming in a Dynamic Visual Environment: SequenceTree”).
Regarding claim 1, Thone teaches a computer-implemented method for parameterizing an imaging sequence of a magnetic resonance system, the method comprising:
(a) receiving and/or establishing at least one fixed parameter using a computer unit [¶0014, wherein an item information is input. ¶0114, wherein standard pulse sequences can be input. See ¶0043, wherein the feedback statements can be “the image is not sharp enough”, “the image is noisy”, “the image is too dark” or “the image is too light” and in those situations, there is a parameter that is fixed. For example, when providing feedback such as “the image is not sharp enough”, the sharpness would be increased and the FOV would stay fixed in a subsequent optimized pulse sequence. See also rest of reference.]; and
(b) automatically setting at least one further parameter of the imaging sequence using a neural network installed on the computer unit, based on the at least one fixed parameter [¶0018, wherein adapted parameters are determined using a neural network. See also rest of reference.],
wherein the neural network is trained to set the at least one further parameter based on the at least one fixed parameter [ ¶0018, wherein the adapted parameter is determined by inputting the item information into the neural network. See also rest of reference.], and wherein in the neural network the parameterization is transferred into a sentence-like structure [¶0026-¶0027, ¶0043 see speech input is used and a sentence-like structure is used. See also rest of reference.].
However, wherein the sentence-like structure comprises parameter designations linked using connecting symbols to parameter values.
Magland, which is also in the field of MRI, teaches the parameterization is transferred into a sentence-like structure, wherein the sentence-like structure comprises parameter designations linked using connecting symbols to parameter values [See Fig. 3, wherein the source could include a sentence like structure with parameters linked using connecting symbols to parameter values. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Thone and Magland because both references are in the field of parameterizing MRI pulse sequence and because Magland teaches that it is known in the art that computer source code for MRI pulse sequences are formatted with pulse sequence parameters and parameter values linked by connecting symbols [Magland – Fig. 3].
Regarding claim 2, Thone teaches the limitations of claim 1, which this claim depends from.
Thone further teaches wherein the neural network is a pre-trained neural network for language processing which has been adapted using training with parameters of imaging sequences for an automatic parameterization [¶0026-0027, ¶0043. See also rest of reference.].
Regarding claim 3, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches wherein in an intermediate step, the at least one fixed parameter is transferred by the computer unit into an incomplete sentence-like structure and is then passed to the neural network for setting the at least one further parameter [¶0043, wherein the example phrases (“the image is too dark”) are missing punctuations and are therefore, incomplete sentences. See also rest of reference.], wherein the incomplete sentence-like structure is incomplete in that parts in the sentence-like structure which relate to the at least one further parameter are lacking [¶0043, wherein the example phrases (“the image is too dark”) disclose image properties but do not explicitly disclose the further parameter of the pulse sequence. See also rest of reference.], and wherein the neural network enhances the incomplete sentence-like structure and in this manner determines the at least one further parameter [¶0018, wherein the adapted parameter is determined by inputting the item information into the neural network. See also rest of reference.].
Regarding claim 5, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches wherein after receiving and/or establishing the at least one fixed parameter, the computer unit fetches at least one parameter from an imaging device that is to be used as at least one hardware-related parameter [¶0019, see specific absorption rate, wherein SAR is used to determine voltage/power values for RF coils. See also rest of reference.], and wherein using the neural network, the at least one further parameter is set based on the at least one fixed parameter and the at least one hardware-related parameter [¶0019, see specific absorption rate, wherein SAR is used to determine voltage/power values for RF coils. See also rest of reference.].
Regarding claim 6, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches wherein a user fixes the at least one parameter by selecting a parameter from an existing imaging sequence and specifying an amended value for the selected parameter [¶0014, wherein an item information is input. ¶0019, ¶0114-0115, wherein a standard pulse sequence is used. See ¶0043, wherein the feedback statements can be “the image is not sharp enough”, “the image is noisy”, “the image is too dark” or “the image is too light” and in those situations, there is a parameter that is fixed. For example, when providing feedback such as “the image is not sharp enough”, the sharpness would be increased and the FOV would stay fixed in a subsequent optimized pulse sequence. See also rest of reference.], wherein the neural network sets the at least one further parameter in that it adapts the remaining parameters of the existing imaging sequence based on the amended parameter [See wherein the reference discloses a “parameter set”. See also rest of reference.], and wherein the neural network is trained to adapt the remaining parameters [See wherein the reference discloses a “parameter set”. See also rest of reference.].
Regarding claim 7, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches wherein the at least one fixed parameter specifies an amended imaging technique to be used in an existing imaging sequence [¶0014, wherein an item information is input. ¶0019, ¶0114-0115, wherein a standard pulse sequence is used. See ¶0043, wherein the feedback statements can be “the image is not sharp enough”, “the image is noisy”, “the image is too dark” or “the image is too light” and in those situations, there is a parameter that is fixed. For example, when providing feedback such as “the image is not sharp enough”, the sharpness would be increased and the FOV would stay fixed in a subsequent optimized pulse sequence. See also rest of reference.].
Regarding claim 8, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches wherein the fixed parameter is a task of omitting a precondition that is needed for execution of an existing imaging sequence [¶0097, wherein step S1 of capturing an initial MR image is optional. See also rest of reference.], and wherein the neural network adapts the parameters of the existing imaging sequence such that the imaging sequence functions without the precondition that is not present [¶0112, wherein S5 adapting a parameter is still performed without S1. See also rest of reference.].
Regarding claim 9, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches wherein the at least one fixed parameter is fixed in that a user specifies that a parameter of the imaging sequence is to be optimized in a specified manner [¶0021, ¶0065. See also rest of reference.], and wherein the neural network adapts the at least one further parameter in such a way that the fixed parameter is optimized in the specified manner [¶0021, ¶0065. See also rest of reference.].
Regarding claim 10, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches wherein a user fixes all the parameters with exception of a designation of the imaging sequence and/or wherein all the parameters with the exception of the designation of the imaging sequence are received or established as fixed parameters using the computer unit [¶0014 and ¶0043, wherein parameter information and information regarding an image property are input, but the designation is not explicitly stated. See also rest of reference.], wherein the neural network automatically sets a designation as a further parameter based on the parameters and their effect, and wherein the designation includes information regarding properties of the imaging sequence [¶0020 and ¶0115. See also rest of reference.].
Regarding claim 11, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches automatically setting at least all the remaining parameters of the imaging sequence using the neural network installed on the computer unit, based on the at least one fixed parameter [¶0058. See also parameter set. See also rest of reference.].
Regarding claim 12, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches wherein the neural network is trained to adapt the remaining parameters based on specified imaging sequences which vary in the at least one parameter [¶0058. See also parameter set. See also rest of reference.].
Regarding claim 14, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches a non-transitory computer-readable data storage device on which a computer program is stored which, when it is executed on a computer, causes the computer to carry out the method as claimed in claim 1 [claim 15].
Regarding claim 15, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches a magnetic resonance system for imaging, comprising: a computer unit operable to receive at least one fixed parameter of an imaging sequence and/or to establish at least one fixed parameter, wherein the imaging sequence includes a number of parameters [¶0014, wherein an item information is input. ¶0114, wherein standard pulse sequences can be input. See ¶0043, wherein the feedback statements can be “the image is not sharp enough”, “the image is noisy”, “the image is too dark” or “the image is too light” and in those situations, there is a parameter that is fixed. For example, when providing feedback such as “the image is not sharp enough”, the sharpness would be increased and the FOV would stay fixed in a subsequent optimized pulse sequence. See also rest of reference.], wherein the computer unit comprises a neural network and is also operable to set at least one further parameter of the imaging sequence using the neural network based on the at least one fixed parameter [¶0018, wherein adapted parameters are determined using a neural network. See also rest of reference.], wherein the neural network is trained to set the at least one further parameter based on the at least one fixed parameter [¶0018, wherein adapted parameters are determined using a neural network. See also rest of reference.], and wherein the computer unit is configured to carry out the method as claimed in claim 1.
Regarding claim 16, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches wherein the computer unit is operable to set all the remaining parameters of the imaging sequence using the neural network based on the at least one fixed parameter [¶0058. See also parameter set. See also rest of reference.].
Regarding claim 17, Thone teaches a computer-implemented method for training a neural network for parameterizing an imaging sequence of a magnetic resonance system, comprising:
(a) a computer unit providing a trained neural network for language processing [¶0026-¶0027, ¶0043 see speech input is used and a sentence-like structure is used. See also rest of reference.];
(b) the computer unit providing parameterized imaging sequences comprising different parameter values [¶0058, wherein training data includes information input, an imaging parameter and/or an imaging sequence of an imaging examination, a parameter of a workflow of the imaging examination. See also rest of reference.];
(c) the computer unit converting the parameterized imaging sequences into a sentence-like structure, wherein the sentence-like structure describes different the parameter values [¶0014, wherein information input includes an imaging sequence. ¶0034, wherein the model is trained to determine input information. ¶0114, wherein the standard sequence describes a parameter set. See also rest of reference.]; and
(d) the computer unit training the neural network for the language processing with the converted imaging sequences [¶0014, wherein information input includes an imaging sequence. ¶0034, wherein the model is trained to determine input information. ¶0114, wherein the standard sequence describes a parameter set. See also rest of reference.].
However, Thone is silent in teaching wherein the sentence-like structure comprises parameter designations linked using connecting symbols to the different parameter values.
Magland, which is also in the field of MRI, teaches the computer unit converting the parameterized imaging sequences into a sentence-like structure, wherein the sentence-like structure describes the different parameter values, and wherein the sentence-like structure comprises parameter designations linked using connecting symbols to the different parameter values [See Fig. 3, wherein the source could include a sentence like structure with parameters linked using connecting symbols to parameter values. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Thone and Magland because both references are in the field of parameterizing MRI pulse sequence and because Magland teaches that it is known in the art that computer source code for MRI pulse sequences are formatted with pulse sequence parameters and parameter values linked by connecting symbols [Magland – Fig. 3].
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over previously cited Thone, in view of previously cited Magland, and in further view of Yoshida (US 2017/0131371).
Regarding claim 4, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone further teaches wherein the imaging sequence includes parameters [¶0014, wherein an item information is input. ¶0019, ¶0114-0115, wherein a standard pulse sequence is used. See ¶0043, wherein the feedback statements can be “the image is not sharp enough”, “the image is noisy”, “the image is too dark” or “the image is too light” and in those situations, there is a parameter that is fixed. For example, when providing feedback such as “the image is not sharp enough”, the sharpness would be increased and the FOV would stay fixed in a subsequent optimized pulse sequence. See also rest of reference.], wherein the computer unit comprises a database with specified parameter values for the parameters of the imaging sequence with fixed step sizes between individual parameter values and/or has access to this database [¶0094. See also rest of reference.], and wherein the method further comprises: (c) following the setting of the at least one further parameter by the neural network, setting the parameter set by the neural network to the nearest specified parameter value [¶0018, wherein the adapted parameter is determined by inputting the item information into the neural network. See also rest of reference.].
However, Thone and Magland are silent in teaching (c) following the setting of the at least one further parameter, rounding the at least one further parameter to the nearest specified parameter value
Yoshida, which is also in the field of MRI, teaches (c) following the setting of the at least one further parameter, rounding the at least one further parameter to the nearest specified parameter value [¶0012, ¶0064-0066. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Thone and Magland with the teachings of Yoshida because all references are in the field of setting parameters in MRI and Yoshida teaches it is known in the art to use close values when picking from options in a database [Yoshida- ¶0012, ¶0064-0066. See also rest of reference.].
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over previously cited Thone, in view of previously cited Magland, and in further view of Rosen (US 10,598,750).
Regarding claim 13, Thone and Magland teach the limitations of claim 1, which this claim depends from.
Thone and Magland are silent in teaching wherein the at least one fixed parameter is fixed in that a user specifies that a parameter of the imaging sequence having a parameter value is to be optimized by minimizing the parameter value.
Rosen, which is also in the field of MRI, teaches wherein the at least one fixed parameter is fixed in that a user specifies that a parameter of the imaging sequence having a parameter value is to be optimized by minimizing the parameter value [Claim 14].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Thone and Magland with the teaching of Rosen because all references are in the field of setting parameters in MRI and Rosen teaches it is known in the art to minimize a parameter, such as timing, to reduce the examination time [Rosen - Claim 14].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2015/0071517 also shows pulse sequence parameterization where the parameters and parameter values are linked using connecting symbols [See Fig. 3].
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 RISHI R PATEL whose telephone number is (571)272-4385. The examiner can normally be reached Mon-Thurs 7 a.m. - 5 p.m..
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/RISHI R PATEL/Primary Examiner, Art Unit 2858
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