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 § 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.
Claims 3-4, 6-7, 9, 13, and 17are 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 3, the limitation “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, wherein the sentence-like structure is incomplete in that parts in the sentence-like structure which relate to the at least one further parameter are lacking” is considered indefinite because it is unclear if “the sentence-like structure” refers back to “incomplete sentence-like structure” or is different.
Regarding claim 4, the claim recites the limitation "the parameters of the imaging sequence" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Also 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 and/or has access to this database” is considered indefinite because it is unclear how the limitation if “or” were to be used in the limitation.
Regarding claim 4, the claim discloses “rounding the parameter set by the neural network”. However it unclear if “the parameter” refers to the “at least one further parameter” or is different.
Regarding claim 6, the limitation “wherein a user fixes the at least one parameter by selecting and amending a parameter from an existing imaging sequence” is considered indefinite because it is not clear how the at least one parameter is “fixed” and also amended (changed).
Regarding claim 7, the limitation “wherein the fixed parameter is an amended imaging technique in an existing imaging sequence” is considered indefinite because it is not clear how the at least one parameter is “fixed” and also amended (changed).
Regarding claim 9, the limitation “wherein the fixed parameter is fixed in that a user specifies that it is to be optimized in a specified manner” is considered indefinite because it is not clear how the at least one parameter is “fixed” and also optimized (changed).
Regarding claim 13, the limitation “wherein the fixed parameter is fixed in that a user specifies that it is to be optimized by minimizing the parameter value” is considered indefinite because it is not clear how the at least one parameter is “fixed” and also optimized (changed). It is also not clear what is meant by “it” and the term “the parameter value” lacks antecedent basis.
Regarding claim 17, it is not clear if “the imaging sequences” refers back to the “parameterized imaging sequences” and it is also not clear if “the parameter values” refers back to the “different parameter values”.
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)(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 1-3, 5-9, 11-12, and 15-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Thone (US 2022/0183637).
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.].
Regarding claim 2, 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 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 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 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 further teaches wherein a user fixes the at least one parameter by selecting and amending a parameter from 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.], 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 further teaches wherein the fixed parameter is an amended imaging technique 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 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 further teaches wherein the fixed parameter is fixed in that a user specifies that it 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 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 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 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 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 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 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 imaging sequences into a sentence-like structure, wherein the sentence-like structure describes 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.].
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 4 is rejected under 35 U.S.C. 103 as being unpatentable over previously cited Thone, in view of Yoshida (US 2017/0131371).
Regarding claim 4, Thone teaches the limitations of claim 1, which this claim depends from.
Thone further teaches 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 is silent in teaching (c) following the setting of the at least one further parameter, rounding the 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 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 Yoshida because both 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 Rosen (US 10,598,750).
Regarding claim 13, Thone teaches the limitations of claim 1, which this claim depends from.
Thone is silent in teaching wherein the fixed parameter is fixed in that a user specifies that it is to be optimized by minimizing the parameter value.
Rosen, which is also in the field of MRI, teaches wherein the fixed parameter is fixed in that a user specifies that it 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 Rosen because both 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 2022/0292679 also teaches a MRI apparatus including a machine learning algorithm that performs natural language processing [¶0113].
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 2896