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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 13 (claims 24 and 32) were found to be §101 eligible at Step 2B, Inventive Concept as limitation of using machine learning for calculating sequence steps, which outputs optimal inversion time using input of data, as these additional limitations that apply judicial exception recited such that the claims as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05 (b) (c) and (e)) and is not well-understood, routine and conventional.
Step 1: Statutory Category: Yes - The claim recites a method for determining an optimal inversion time, thus is a method.
Step 2A, Prong 1, Judicial Exception: Yes - The claim 13 recites the limitations:
A method for determining an optimal inversion time for, the method comprising:
performing a training sequence including: optimizing a machine learning model starting from a plurality of calculated optimal inversion time values correlated to significant data of a related examined subject and specific parameters of a carried-out examination as established in the course of a plurality of sample assessments; and
performing a calculation sequence including:
a first step wherein significant data of a subject to be examined and specific parameters of an examination carried out by the magnetic resonance apparatus are collected and
a second step wherein the computer calculates an optimal inversion time value for the subject to be examined and for the carried-out examination by a machine learning model using the input data;
These limitations, as drafted, is a process step that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind as it recites steps of observing the collection of images and its calculated inversion time values and performing calculation by observing data of a subject for examination with values of collection data, and calculates optimal inversion time value for the subject and for the collection.
These actions fall under the Mental Processes and Mathematical Concepts categories defined in MPEP 2106.04(A)(2) as “concept performed in the human mind (including observation, evaluation, judgement and opinion),” “mathematical concepts (including mathematical calculations) and “Certain Methods of Organizing Human Activity (following rules or instructions).”
A human, such as surgeon and/or physician, could mentally perform tasks of observing the data of related examined subject (such as patient data and files with myocardium conditions) and reviewing all the collected data by determining correlation between inversion time values and the data to calculate inversion time value for the subject. Correlation can be determined using data and putting data into correlation formula and collecting data of a current subject and calculate optimal inversion time value for the subject and following calculation sequence (steps to follow, following directions) can be performed by human activity of simply following first, second and third step listed on a calculation sequence step by step.
These steps can all be performed in the mind with aid of a pen and paper.
That is, nothing in the claim element precludes the step from practically being performed in the mind and/or being performed with the aid of a pen and paper. Accordingly, the claim recites a mental process-type and mathematical concepts abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements:
an "Inversion Recovery" radio frequency pulse sequence of a magnetic resonance for acquiring late images after administering a paramagnetic contrast medium by a control and image capture device associated with a magnet structure of an apparatus for magnetic resonance and connected to a computer configured to exchange information and instructions with the control and image capture device
provided as input data to the computer and the computer calculates
by a machine learning model using the input data;
a third step wherein the optimal inversion time is provided to the control and image capture device so as to drive an acquisition of resulting late images of an area of interest which are optimized for the subject to be examined and for the carried-out examination,
wherein the second step of the calculation sequence uses at least in part the machine learning model as optimized in the training sequence for calculating the optimal inversion time value.
This claim elements of acquiring image and using computer to transfer data and \perform calculations are a mere data collection step which amounts to a pre-solution insignificant activity and post-solution activity. The claims do not specify any configuration or improvement in the MR imaging and computer itself, just simply gathering data which are necessary precursors for all uses of the recited abstract idea since no optimal inversion time can be determined without first gathering necessary image data.
Us of machine learning in the claim represents the set of steps and logic employing the subtract idea, mathematical calculations and human activity in determining optimal inversion time. Executing theses specific algorithms steps on the generic processor/computer using data from the calculation, does not integrate the abstract idea into a practical application. The models act as the instructions being applied by the generic hardware components and their specificity defines the abstract task rather than providing integration through the non-abstract elements (computer). This falls under MPEP 2106.05(f) as specific instructions (“apply it”) for the generic computer.
Moreover, controlling the device of MR using the inversion time calculated is a post-solution insignificant activity, just simply configuring scan procedure with result from calculation.
In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer according to MPEP 2106.05(f). Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims according to MPEP 2106.05(a). That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent claim 1 (and their respective dependent claims) is not integrated into a practical application under MPEP 2106.04(d)(I).
Step 2B, Inventive Concept: Yes – The claim recites additional claim elements recite the following additional elements:
an "Inversion Recovery" radio frequency pulse sequence of a magnetic resonance for acquiring late images after administering a paramagnetic contrast medium by a control and image capture device associated with a magnet structure of an apparatus for magnetic resonance and connected to
a computer configured to exchange information and instructions with the control and image capture device
provided as input data to the computer and the computer calculates
by a machine learning model using the input data;
a third step wherein the optimal inversion time is provided to the control and image capture device so as to drive an acquisition of resulting late images of an area of interest which are optimized for the subject to be examined and for the carried-out examination,
wherein the second step of the calculation sequence uses at least in part the machine learning model as optimized in the training sequence for calculating the optimal inversion time value.
Inversion recovery of MRI for acquiring late images and control and image capture device so as to drive an acquisition of resulting late images of an area of interest which are optimized for the subject to be examined and for the carried-out examination drive are all carried out by MRI acquisition. These elements of inversion recovery is well-understood, routine and conventional as Spottiswoode (US2014/0314289) discloses that conventional inversion recovery in magnetic resonance imaging is used for late enhancement imaging ([0003]).
However, “wherein the second step of the calculation sequence uses at least in part the machine learning model as optimized in the training sequence for calculating the optimal inversion time value” is a step that is not conventional.
Therefore, claim 13 is eligible for being inventive (not well-understood, conventional and routine) under Step 2B, Inventive Concept.
Claims 24 and 32 are eligible for the same reason set forth above for claim 13.
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 14-16, 19, 23-29 and 31-32 are 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.
Claim 14 recites “a related examined subject” and it is not definite whether it is same or new and distinct related examined subject from recited “a related examiner subject” in claim 13.
Claims 15-16 are rejected as they inherit rejection of claim 14 as set forth above.
Claim 19 recites the limitation "the first data" in line 21 page 7. There is insufficient antecedent basis for this limitation in the claim, as “first data” was not previously recited in claims 13 and 19.
Claim 31 is rejected as it inherits the rejection of claim 19 as set forth above.
Claim 23 recites the limitation "the myocardium" in line 10 page 8 There is insufficient antecedent basis for this limitation in the claim, as “a myocardium” was not previously recited in claims 13 and 23.
Claim 24 recites “a subject to be examined” in line 11 page 9. This is not definite as it is not clear whether it is different and new subject to be examined from previously recited “subject to be examined” multiple times in claim 24.
Claims 25-29 are rejected as they inherit rejection of claim 24 as set forth above.
Claim 25 recites “a plurality of calculated optimal inversion time…of a related examined subject” and it is not definite whether it is same or new and distinct related examined subject from recited “a related examiner subject” in claim 13.
Claim 29 recites the limitation "the myocardium" in line 5 page 10. There is insufficient antecedent basis for this limitation in the claim, as “a myocardium” was not previously recited in claims 24 and 29.
Claim 32 recites “a related examined subject” multiple time and it is not definite whether applicant intends to claim more than one related examined subject or meant to recite single related examined subject.
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.
Claims 13-14, 20-25, 28-29 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over “Bahrami et al.,” “Automated Selection of Myocardial Inversion Time with a Convolutional Neural Network: Spatial Temporal Ensemble Myocardium Inversion NETwork,” Magn Reson Med. 2019 May; 81(5): 3283-3291 (hereinafter Bahrami) and “Kuhara,” US 2012/0161760 (hereinafter Kuhara).
Regarding to claim 13, Bahrami teaches a method for determining an optimal inversion time for an "Inversion Recovery" radio frequency pulse sequence of a magnetic resonance for acquiring late images (Delayed enhanced imaging Abstract page 3283) after administering a paramagnetic contrast medium by a control and image capture device associated with a magnet structure of an apparatus for magnetic resonance (8 minutes after the administration of gadolinium0based intravenous contrast, Methods, page 3285) the method comprising:
performing a training sequence including: optimizing a machine learning model starting from a plurality of calculated optimal inversion time values correlated to significant data of a related examined subject and specific parameters of a carried-out examination as established in the course of a plurality of sample assessments (select the image with optimal inversion time, optimal inversion time was marked on all available inversion recovery scout sequences, inversion time null point ground-truth annotation page 3285); and
performing a calculation sequence including:
a first step wherein significant data of a subject to be examined and specific parameters of an examination carried out by the magnetic resonance apparatus are collected and provided as input data to the computer (input window with four consecutive frames as inputs to the neural network, Problem definition and model design page 3285, spatial and temporal characteristics of slices pages 4-5);
a second step wherein the computer calculates an optimal inversion time value for the subject to be examined and for the carried-out examination by a machine learning model using the input data (inversion recovery null point prediction, neural network in identifying myocardial null point, page 5); and
wherein the second step of the calculation sequence uses at least in part the machine learning model as optimized in the training sequence for calculating the optimal inversion time value (CNN for automated prediction of myocardial inversion time Discussion, pages 5-6).
Bahrami does not further teach following limitations:
MRI connected to a computer configured to exchange information and instructions with the control and image capture device,
a third step wherein the optimal inversion time is provided to the control and image capture device so as to drive an acquisition of resulting late images of an area of interest which are optimized for the subject to be examined and for the carried-out examination,
However, in the analogous field of MR late imaging, Kuhara teaches MRI imaging device connected to the computer and a data processing unit ([0043]), after calculating inversion time, and the imaging scan is started with imaging condition setting unit providing the sequence controller for calculated inversion time, control information for imaging scan, enables the imaging scan to be performed based on inversion recovery method with the optimally set inversion time ([0131]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify prediction of inversion time using machine learning model as taught by Bahrami to incorporate teaching of Kuhara, since controlling imaging conditions for performing imaging scan with optimal inversion time was well known in the art as taught by Kuhara. One of ordinary skill in the art could have combined the elements as claimed by Bahrami with no change in their respective functions, simply using calculated inversion time to set imaging condition for imaging scan, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to control and perform imaging scan with optimally set inversion time ([0131]), and there was reasonable expectation of success.
Regarding to claim 14, Bahrami andKuhara together teach all limitations of claim 13 as set forth above.
Bahrmai further teaches wherein the training sequence further includes:
a first step of acquisition of a plurality of calculated optimal inversion time values correlated to significant data of a related examined subject and specific parameters of the carried-out examination established in the course of a plurality of sample assessments (spatial and temporal characteristics that a CNN uses to make classification into “early” and “Acceptable” categories, by decoding important features from the input image, Saliency map pages 4-5);
a second step of optimization of the machine learning model based on the calculated optimal inversion time values correlated to significant data of a related examined subject and specific parameters of the carried-out examination of the plurality of sample assessments (prediction of inversion time from STEMI-Net closely matched with expert annotation page5); and
a third step of generation of the machine learning model so optimized (hyperparameters VGG19, STEMI-Net, training error was minimized using SGD optimizer minimizing binary cross-entropy, page 4 and Discussion, pages 6-7).
Regarding to claims 20-23, Bahrami and Kuhara together teach all limitations of claim 13 as set forth above.
Bahrami further teaches following limitations:
Of claim 20, wherein the significant data of the subject to be examined is selected from a group consisting of: anthropomorphic data, including height, weight, age of the subject, and clinical data, including a medical history or any medications administered to the subject (patient demographics, Table 1 shows age and clinical data pages 3 and 15).
Of claim 21, wherein the specific parameters of the carried- out examination is selected from a group consisting of: type of paramagnetic contrast medium as used, quantities administered, and timing of administration and specifications of the resonance apparatus (cardiac MRI inversion recovery scout acquisitions, 8 minutes after the administration of gadolinium-based intravenous contrast, Patient demographics and cardiac MRI page 3).
Of claim 22, wherein the resulting late images include portions of tissue of the area of interest with several clearly distinct histocellular compositions (Table 1 shows cases with mass, and myocardial scar, which would have several histocellular compositions as claimed, Table 1 page 15)
Of claim 23, wherein the area of interest is the myocardium of the subject to be examined (myocardial scar and viability evaluation, page 2).
Regarding to claim 24, Bahrami teaches a magnetic resonance apparatus provided with at least one magnet structure controlled by a control and image capture device (MRI cardiac exam with 1.5 T scanners, Abstract)
wherein the computer is configured to implement a method for determining an optimal inversion time for an "Inversion Recovery" radio frequency pulse sequence of a magnetic resonance for acquiring late images (Delayed enhanced imaging Abstract page 3283) after administering a paramagnetic contrast medium by a control and image capture device associated with a magnet structure of an apparatus for magnetic resonance (8 minutes after the administration of gadolinium0based intravenous contrast, Methods, page 3285) comprising:
optimizing a machine learning model starting from a plurality of calculated optimal inversion time values correlated to significant data of a related examined subject and specific parameters of the carried-out examination of the plurality of sample assessments (prediction of inversion time from STEMI-Net closely matched with expert annotation page5);
performing a calculation sequence including:
a first step wherein significant data of a subject to be examined and specific parameters of an examination carried out by the magnetic resonance apparatus are collected and provided as input data to the computer (input window with four consecutive frames as inputs to the neural network, Problem definition and model design page 3285, spatial and temporal characteristics of slices pages 4-5);
a second step wherein the computer calculates an optimal inversion time value for the subject to be examined and for the carried-out examination by a machine learning model using the input data (inversion recovery null point prediction, neural network in identifying myocardial null point, page 5); and
wherein the second step of the calculation sequence uses at least in part the machine learning model as optimized in the training sequence for calculating the optimal inversion time value (CNN for automated prediction of myocardial inversion time Discussion, pages 5-6).
Bahrami does not further teach following limitations:
MRI provided with a computer connected to the control and image capture device associated with calculation means,
a third step wherein the optimal inversion time is provided to the control and image capture device so as to drive an acquisition of resulting late images of an area of interest which are optimized for the subject to be examined and for the carried-out examination,
However, in the analogous field of MR late imaging, Kuhara teaches MRI imaging device connected to the computer and a data processing unit ([0043]), after calculating inversion time, and the imaging scan is started with imaging condition setting unit providing the sequence controller for calculated inversion time, control information for imaging scan, enables the imaging scan to be performed based on inversion recovery method with the optimally set inversion time ([0131]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify prediction of inversion time using machine learning model as taught by Bahrami to incorporate teaching of Kuhara, since controlling imaging conditions for performing imaging scan with optimal inversion time was well known in the art as taught by Kuhara. One of ordinary skill in the art could have combined the elements as claimed by Bahrami with no change in their respective functions, simply using calculated inversion time to set imaging condition for imaging scan, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to control and perform imaging scan with optimally set inversion time ([0131]), and there was reasonable expectation of success.
Regarding to claim 25, Bahrami and Kuhara together teach all limitations of claim 24 as set forth above.
Bahrami further teaches following limitations of wherein the training sequence further includes:
a first step of acquisition of a plurality of calculated optimal inversion time values correlated to significant data of a related examined subject and specific parameters of the carried-out examination established in the course of a plurality of sample assessments (spatial and temporal characteristics that a CNN uses to make classification into “early” and “Acceptable” categories, by decoding important features from the input image, Saliency map pages 4-5);
a second step of optimization of the machine learning model based on the calculated optimal inversion time values correlated to significant data of a related examined subject and specific parameters of the carried-out examination of the plurality of sample assessments (prediction of inversion time from STEMI-Net closely matched with expert annotation page5); and
a third step of generation of the machine learning model so optimized (hyperparameters VGG19, STEMI-Net, training error was minimized using SGD optimizer minimizing binary cross-entropy, page 4 and Discussion, pages 6-7).
Regarding to claims 28-29, Bahrami and Kuhara together teach all limitations of claim 24 as set forth above.
Bahrami further teaches following limitations:
Of claim 28, wherein the resulting late images include portions of tissue of the area of interest with several clearly distinct histocellular compositions (Table 1 shows cases with mass, and myocardial scar, which would have several histocellular compositions as claimed, Table 1 page 15)
Of claim 29, wherein the area of interest is the myocardium of the subject to be examined (myocardial scar and viability evaluation, page 2).
Regarding to claim 32, Bahrami teaches a method for determining an optimal inversion time for an "Inversion Recovery" radio frequency pulse sequence of a magnetic resonance for acquiring late images (Delayed enhanced imaging Abstract page 3283) after administering a paramagnetic contrast medium by a control and image capture device associated with a magnet structure of an apparatus for magnetic resonance (8 minutes after the administration of gadolinium-based intravenous contrast, Methods, 1.5T cardiac MRI) comprising:
performing a training sequence including:
a first step of acquisition of a plurality of calculated optimal inversion time values correlated to significant data of a related examined subject and specific parameters of the carried-out examination established in the course of a plurality of sample assessments (spatial and temporal characteristics that a CNN uses to make classification into “early” and “Acceptable” categories, by decoding important features from the input image, Saliency map pages 4-5);
a second step of optimization of the machine learning model based on the calculated optimal inversion time values correlated to significant data of a related examined subject and specific parameters of the carried-out examination of the plurality of sample assessments (prediction of inversion time from STEMI-Net closely matched with expert annotation page5); and
a third step of generation of the machine learning model so optimized (hyperparameters VGG19, STEMI-Net, training error was minimized using SGD optimizer minimizing binary cross-entropy, page 4 and Discussion, pages 6-7).
performing a calculation sequence including:
a first step wherein significant data of a subject to be examined and specific parameters of an examination carried out by the magnetic resonance apparatus are collected and provided as input data to the computer (input window with four consecutive frames as inputs to the neural network, Problem definition and model design page 3285, spatial and temporal characteristics of slices pages 4-5);
a second step wherein the computer calculates an optimal inversion time value for the subject to be examined and for the carried-out examination by a machine learning model using the input data (inversion recovery null point prediction, neural network in identifying myocardial null point, page 5); and
wherein the second step of the calculation sequence uses at least in part the machine learning model as optimized in the training sequence for calculating the optimal inversion time value (CNN for automated prediction of myocardial inversion time Discussion, pages 5-6).
Bahrami does not further teach following limitations:
MRI connected to a computer configured to exchange information and instructions with the control and image capture device,
a third step wherein the optimal inversion time is provided to the control and image capture device so as to drive an acquisition of resulting late images of an area of interest which are optimized for the subject to be examined and for the carried-out examination,
However, in the analogous field of MR late imaging, Kuhara teaches MRI imaging device connected to the computer and a data processing unit ([0043]), after calculating inversion time, and the imaging scan is started with imaging condition setting unit providing the sequence controller for calculated inversion time, control information for imaging scan, enables the imaging scan to be performed based on inversion recovery method with the optimally set inversion time ([0131]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify prediction of inversion time using machine learning model as taught by Bahrami to incorporate teaching of Kuhara, since controlling imaging conditions for performing imaging scan with optimal inversion time was well known in the art as taught by Kuhara. One of ordinary skill in the art could have combined the elements as claimed by Bahrami with no change in their respective functions, simply using calculated inversion time to set imaging condition for imaging scan, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to control and perform imaging scan with optimally set inversion time ([0131]), and there was reasonable expectation of success.
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Bahrmai and Kuhara as applied to claim 14 above, and further in view of “Timm et al.,” US 2020/0243315 (hereinafter Timm).
Regarding to claims 15-16, Bahrmai and Kuhara together teach all limitations of claim 14 as set forth above.
Bahrami does disclose neural network examples and training the model with initial hyperparameters (VGG19, 2nd paragraph page 4), but does not further disclose specific details of optimization of the training sequences.
However, in the analogous field of endeavor in using machine learning training, Timm teaches machine learning training using classifier such as convolutional neural network or random forest classifier, and the hyperparameters of the classifiers are optimized during the training of the classifier, using evolutionary algorithm ([0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify training neural network as taught by Bahrami to incorporate tehacing of Timm, since using random forest classifier and optimizing hyperparameters of the classifiers using evolutionary algorithm was well known in the art as taught by Timm. One of ordinary skill in the art could have combined the elements as claimed by Bahrami with no change in their respective functions, configuring its optimizer for training with random forest classifier and using evolutionary computing techniques, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to optimize hyperparameters of the classifier ([0032]), and there was reasonable expectation of success.
Claims 18-19, 27, and 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over Bahrmai and Kuhara as applied to claims 13 and 24 above, and further in view of “Truwit et al.,” US 2023/0067146 (hereinafter Truwit).
Regarding to claims 18-19, 27, and 30-31, Bahrami and Kuhara together teach all limitations of claims 13 and 24 as set forth above.
Bahrami and Kuhara do not further teach input data provided to the computer through an automatic transmission selected from a group consisting of: automatic transmission of the wire4d type, an automatic transmission of the wireless type and a manual entry.
However, in the analogous field of endeavor in MRI, Truwit discloses MRI device, communication with the processor using interface, including wireless transmission, USB and digital input interface ([0053] and [0073]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify computing device and MRI as taught by Bahrami to incorporate teaching of Truwit, since using interface for transmission of data wired, wireless and manual entry were well known in the art as taught by Truwit. One of ordinary skill in the art could have combined the elements as claimed by Bahrami with no change in their respective functions, inputting data by automatic transmission (Wired or wireless) data, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide control and exchange of data between a processor with an external device and/or imaging device ([0053]), and there was reasonable expectation of success.
Claims 17 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Bahrami and Kuhara as applied to claims 13 and 24 above, and further in view of “Hardy et al.,” US 2015/0094562 (hereinafter Hardy).
Regarding to claims 17 and 26, Bahrami and Kuhara together teach all limitations of claims 13 and 24 as set forth above.
Bahrami and Kuhara do not further disclose inversion time is constantly updated during an examination carried out by the magnetic resonance apparatus.
However, in the analogous field of endeavor in MRI, Hardy teaches TI can be dynamically and automatically adjusted during the scanning process ([0035]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to inversion time as taught by Bahrami to incorporate teaching of Hardy, since updating TI value during scanning was well known in the art as taught by Hardy. One of ordinary skill in the art could have combined the elements as claimed by Bahrami with no change in their respective functions, using its calculated optimal inversion time to be implemented during scanning, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide dynamic adjustment of parameters of MRI acquisition during scanning process ([0035]), and there was reasonable expectation of success.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Maillot (FR 3134710, application 18/856,637) discloses determining optimal inversion time from MRI images in inversion recovery method.
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/PATRICIA J PARK/Primary Examiner, Art Unit 3798
