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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/15/2026 has been entered.
Response to Amendment
This office action is in response to the remarks filed on 01/15/2025.
The amendment filed 07/16/2025 has been entered. Claims 1-3 and 6-15 remain pending in the application, and claims 4-5 have been canceled.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1 and 15, those of ordinary skill in the art would not understand the processing steps of the algorithm necessary to implement the claimed invention using the claimed “correction algorithm”. In particular, no equations, flowcharts, or phraseology is present in the disclosure that describes the algorithm necessary for the “determining and executing at least one measure based on the outputted output data,
wherein the at least one measure comprises applying a correction algorithm to the image data”.
As explained in MPEP 2161.01(I), simply specifying a desired outcome without sufficiently describing how the functions necessary to achieve the outcome are performed or how the result is achieved, is insufficient to fulfill the written description requirement; the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. MPEP 2161.01(I) further states that:
It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
Applicant’s specification has not disclosed the necessary algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the claimed invention, including how to program a computer to “determine and execute at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data”.
There is insufficient written description of the particular algorithm implemented by the invention of claims 1 and 15.
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 1-3 and 6-15 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 1 recites the limitation “determining and executing at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data” on lines 13-14. This claim is indefinite as it is unclear how the output data, which “describes potential causes of the false value”, is used to determine the “at least one measure” and adjust the “correction algorithm”. Clarification is needed. For examination purposes, this claim limitation will be interpreted as “determining and executing at least one measure, wherein the at least one measure comprises applying a correction algorithm to the image data”.
Claims 2-3 and 6-14 are rejected due to dependency on claim 1.
Claim 15 recites the limitation “determine and execute at least one measure based on the outputted output data. wherein the at least one measure comprises applying a correction algorithm to the image data” on lines 17-19. This claim is indefinite as it is unclear how the output data, which includes “describes potential causes of the false value” is used to determine the “at least one measure” and adjust the “correction algorithm”. Clarification is needed. For examination purposes, this claim limitation will be interpreted as “determine and execute at least one measure, wherein the at least one measure comprises applying a correction algorithm to the image data”.
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 1-15 are rejected under 35 U.S.C. 101.
Regarding claim 1,
Step 1: Statutory category: Yes- A computer-implemented method for operating a magnetic resonance facility to determine at least one potential cause of a false value in image data of an imaging procedure, and is therefore a method.
Step 2: Step 2A, Prong 1, Judicial Exception: Yes- This claim recites the limitation
“applying a trained artificial intelligence classification function to the input dataset to determine an output dataset that describes potential causes of the false value”, “determining and executing at least one measure based on the outputted output data,
wherein the at least one measure comprises applying a correction algorithm to the image data”.
This limitation, as drafted, according to its broadest reasonable interpretation,
recites a mental-process type abstract idea, which can practically be performed in the
mind and/or with the with the aid of pen and paper or with a generic computer, in a
computer environment, or merely using the generic computer as a tool to perform the
steps. One of ordinary skill in the art could group/categorize values from one dataset to determine a secondary dataset that contains false values and potential reasons for the false values, and then correct any false values within the image data. Further, associating values in a dataset with false values and potential causes for false values, according to its broadest reasonable interpretation, recites a mathematical concept (see MPEP 2106.04(a)(2)(I)). That is, nothing in the claim element precludes the step from practically being performed in the mind and/or be reasonably performed with an aid of pen and paper or on a generic computer. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No- the claim recites the following additional elements of “compiling an input dataset that is to be analyzed and comprises radiofrequency signal data acquired during the imaging procedure”, “wherein the radiofrequency signal data comprises sensor data acquired by at least one further radiofrequency sensor of the magnetic resonance facility that is not used for the imaging, the at least one further radiofrequency sensor comprises a pickup coil and/or a breath sensor to acquire sensor data during the imaging procedure without contributing to image formation”, and “outputting at least a portion of the output data of the output dataset”.
Compiling an input dataset that is to be analyzed and comprises radiofrequency signal data acquired during the imaging procedure, and acquiring radiofrequency data is a form of data gathering that is a form of a pre-solution insignificant activity. Outputting at least a portion of the output data of the output dataset is a post-solution insignificant activity.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into
a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to
significantly more than the judicial exception. The claim further recites “wherein the radiofrequency signal data comprises sensor data acquired by at least one further radiofrequency sensor of the magnetic resonance facility that is not used for the imaging” and “wherein the at least one further radiofrequency sensor comprises a pickup coil and/or a breath sensor”, however, radiofrequency sensors of the magnetic resonance facility that is not used for the imaging, like pick up coils and breath sensors, is a well-known generic components. For these reasons, there is no inventive
concept in the claim.
Accordingly, claim 1 is directed to non-eligible patent subject matter and is therefore rejected.
Regarding claim 2,
Step 1: Statutory category: Yes- A computer-implemented method for operating a magnetic resonance facility to determine at least one potential cause of a false value in image data of an imaging procedure, and is therefore a method.
Step 2: Step 2A, Prong 1, Judicial Exception: Yes- This claim contains a judicial exception as noted above for claim 1.
Step 2A, Prong 2, Integrated into Practical Application: No- the claim recites the following additional elements of “wherein the radiofrequency signal data comprises at least a portion of the image data of the imaging procedure in k-space and/or in a hybrid space and/or in image space”.
Obtaining radiofrequency signal data that comprises at least a portion of the image data of the imaging procedure in k-space and/or in a hybrid space and/or in image space is a form of data gathering that is a form of a pre-solution insignificant activity.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into
a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to
significantly more than the judicial exception. For these reasons, there is no inventive
concept in the claim.
Accordingly, claim 2 is directed to non-eligible patent subject matter and is therefore rejected.
Regarding claim 3,
Step 1: Statutory category: Yes- A computer-implemented method for operating a magnetic resonance facility to determine at least one potential cause of a false value in image data of an imaging procedure, and is therefore a method.
Step 2: Step 2A, Prong 1, Judicial Exception: Yes- This claim recites the limitation “wherein during acquisition of multiple k-space sections following a common excitation pulse in one shot, the k-space sections are assigned to the respective shot as an additional dimension of the radiofrequency signal data of the input dataset, and/or that, as a further dimension of the radiofrequency signal data of the input dataset, an assignment to a coil channel in which the signal data was acquired is used”
This limitation, as drafted, according to its broadest reasonable interpretation,
recites a mental-process type abstract idea, which can practically be performed in the
mind and/or with the with the aid of pen and paper or with a generic computer, in a
computer environment, or merely using the generic computer as a tool to perform the
steps. One of ordinary skill in the art could group/categorize values into a particular dimension or assign the signal data to a particular coil channel. Further, associating values in a dataset to a dimension or a coil channel, according to its broadest reasonable interpretation, recites a mathematical concept (see MPEP 2106.04(a)(2)(I)). That is, nothing in the claim element precludes the step from practically being performed in the mind and/or be reasonably performed with an aid of pen and paper or on a generic computer. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No- The claim additionally recites “acquiring multiple k-space sections following a common excitation pulse in one shot”, this is a form of data gathering that is a form of a pre-solution insignificant activity.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into
a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to
significantly more than the judicial exception. The claim additional recites coil channels, however, coil channels are well-known generic components that are used to capture MRI signals. For these reasons, there is no inventive concept in the claim.
Accordingly, claim 3 is directed to non-eligible patent subject matter and is therefore rejected.
Regarding claim 6-8,
Step 1: Statutory category: Yes- A computer-implemented method for operating a magnetic resonance facility to determine at least one potential cause of a false value in image data of an imaging procedure, and is therefore a method.
Step 2: Step 2A, Prong 1, Judicial Exception: Yes- This claim recites the limitation “wherein the input dataset comprises at least one item of supplementary information about the imaging procedure in addition to the radiofrequency signal data”, “wherein the supplementary information is selected from a group consisting of: coil information…, orientation information…, at least one temperature measurement value…, a door sensor signal…”, “wherein the trained classification function, by using the supplementary information, determines, in relation to at least one cause, localization information describing a location of the cause as part of the output dataset”
This limitation, as drafted, according to its broadest reasonable interpretation,
recites a mental-process type abstract idea, which can practically be performed in the
mind and/or with the with the aid of pen and paper or with a generic computer, in a
computer environment, or merely using the generic computer as a tool to perform the
steps. One of ordinary skill in the art could group/categorize/associate values of an input data set with different information that is collected during the imaging procedure including: coil information describing coils used for the imaging, orientation information describing an orientation of measured volumes and/or gradient information describing gradient pulses played out during the imaging procedure, at least one temperature measurement value of a temperature sensor of the magnetic resonance facility, and a door sensor signal indicating a closure state of a door of a shielded cabin of the magnetic resonance facility; which can then be used to determine the cause of the false value. That is, nothing in the claim element precludes the step from practically being performed in the mind and/or be reasonably performed with an aid of pen and paper or on a generic computer. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No- The claim does not
contain additional elements. Therefore, the claim does not integrate the judicial exception into a practical application.
Step 2A, Prong 2, Integrated into Practical Application: No- The claim does not
contain additional elements. Therefore, the claim does not integrate the judicial exception into a practical application.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to
significantly more than the judicial exception. For these reasons, there is no inventive
concept in the claim.
Accordingly, claims 6-8 are directed to non-eligible patent subject matter and is therefore rejected.
Regarding claim 9,
Step 1: Statutory category: Yes- A computer-implemented method for operating a magnetic resonance facility to determine at least one potential cause of a false value in image data of an imaging procedure, and is therefore a method.
Step 2: Step 2A, Prong 1, Judicial Exception: Yes- This claim contains a judicial
exception as noted above for claim 1.
Step 2A, Prong 2, Integrated into Practical Application: No- the claim recites the following additional elements of “wherein the trained classification function comprises a ResNet, in particular a ResNet-18, and/or an AlexNet and/or a SqueezeNet, as a neural network”
The classification function including a ResNet-18, and/or an AlexNet and/or a SqueezeNet, as a neural network is form of data gathering that is a form of a pre-solution insignificant activity.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into
a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to
significantly more than the judicial exception. For these reasons, there is no inventive
concept in the claim.
Accordingly, claim 9 is directed to non-eligible patent subject matter and is therefore rejected.
Regarding claim 10-11,
Step 1: Statutory category: Yes- A computer-implemented method for operating a magnetic resonance facility to determine at least one potential cause of a false value in image data of an imaging procedure, and is therefore a method.
Step 2: Step 2A, Prong 1, Judicial Exception: Yes- This claim recites the limitation “wherein at least one measure is determined and actioned based on the outputted output data”, “wherein the at least one measure is selected from a group consisting of: storing an entry in an error memory; outputting an alert to a user; sending a message to a maintenance service; and applying a correction algorithm to the image data”
This limitation, as drafted, according to its broadest reasonable interpretation,
recites a mental-process type abstract idea, which can practically be performed in the
mind and/or with the with the aid of pen and paper or with a generic computer, in a
computer environment, or merely using the generic computer as a tool to perform the
steps. One of ordinary skill in the art could group/categorize/associate outputted values with an a list of actions which includes storing an entry in an error memory, outputting an alert to a user, sending a message to a maintenance service, and applying a correction algorithm to the image data.
That is, nothing in the claim element precludes the step from practically being performed in the mind and/or be reasonably performed with an aid of pen and paper or on a generic computer. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No- The claim does not
contain additional elements. Therefore, the claim does not integrate the judicial exception into a practical application.
Step 2A, Prong 2, Integrated into Practical Application: No- The claim does not
contain additional elements. Therefore, the claim does not integrate the judicial exception into a practical application.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to
significantly more than the judicial exception. For these reasons, there is no inventive
concept in the claim.
Accordingly, claims 10-11 are directed to non-eligible patent subject matter and is therefore rejected.
Regarding claim 12-13,
Step 1: Statutory category: Yes- A computer-implemented method for operating a magnetic resonance facility to determine at least one potential cause of a false value in image data of an imaging procedure, and is therefore a method.
Step 2: Step 2A, Prong 1, Judicial Exception: Yes- This claim contains a judicial
exception as noted above for claim 1.
Step 2A, Prong 2, Integrated into Practical Application: No- the claim recites the following additional elements of “wherein in order to provide the trained classification function, a pretrained classification function is provided and trained using transfer learning based on training datasets, wherein each training dataset comprises an input dataset and an associated ground truth”, and “wherein at least some of the input datasets of the training datasets are determined from base datasets free of false values using characteristics information assigned to causes”.
Obtaining a pretrained classification function that is trained using transfer learning based on training datasets, wherein each training dataset comprises an input dataset and an associated ground truth, and at least some of the input datasets of the training datasets are determined from base datasets free of false values using characteristics information assigned to causes, is a form of data gathering that is a form of a pre-solution insignificant activity.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into
a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to
significantly more than the judicial exception. For these reasons, there is no inventive
concept in the claim.
Accordingly, claims 12-13 are directed to non-eligible patent subject matter and is therefore rejected.
Regarding claim 14,
Step 1: Statutory category: Yes- A non-transitory electronically readable data medium having stored thereon a computer program having program means such that when the computer program is executed on a computing facility, the computing facility performs the steps of the method as claimed in claim 1, is disclosed, therefore a device is disclosed.
Step 2: Step 2A, Prong 1, Judicial Exception: Yes- This claim contains a judicial exception as noted above for claim 1.
Step 2A, Prong 2, Integrated into Practical Application: No- This claim does not do not integrate the judicial exception into a practical application as noted above for claim 1.
Step 2B, Inventive Concept: No- There is no inventive concept in the claim as noted above in claim 1
Accordingly, claim 14 is directed to non-eligible patent subject matter and is therefore rejected.
Regarding claim 15,
Step 1: Statutory category: Yes- A computing facility for determining at least one potential cause for a false value in image data of at least one imaging procedure is disclosed, therefore a device is disclosed.
Step 2: Step 2A, Prong 1, Judicial Exception: Yes- This claim recites the limitation
“a classification unit operable to apply a trained artificial intelligence classification function to the input dataset to determine an output dataset that describes potential causes of the false value”, and “a measures unit configured with processing circuitry and memory operable to determine and execute at least one measure based on the outputted output data. wherein the at least one measure comprises applying a correction algorithm to the image data”.
This limitation, as drafted, according to its broadest reasonable interpretation,
recites a mental-process type abstract idea, which can practically be performed in the
mind and/or with the with the aid of pen and paper or with a generic computer, in a
computer environment, or merely using the generic computer as a tool to perform the
steps. One of ordinary skill in the art could group/categorize values from one dataset to determine a secondary dataset that contains false values and potential reasons for the false values, and then correct any false values within the image data. Further, associating values in a dataset with false values/potential causes for false values, according to its broadest reasonable interpretation, recites a mathematical concept (see MPEP 2106.04(a)(2)(I)). That is, nothing in the claim element precludes the step from practically being performed in the mind and/or be reasonably performed with an aid of pen and paper or on a generic computer. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No- the claim recites the following additional elements of “receive procedure data describing the imaging procedure and comprising at least radiofrequency signal data acquired during the imaging procedure”, “a compilation unit operable to compile, from the procedure data, an input dataset that is to be analyzed and contains at least a portion of the radiofrequency signal data”, “output at least a portion of the output data of the output dataset”.
Compiling an input dataset that is to be analyzed and comprises radiofrequency signal data acquired during the imaging procedure is a form of data gathering that is a form of a pre-solution insignificant activity. Outputting at least a portion of the output data of the output dataset is a post-solution insignificant activity.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into
a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to
significantly more than the judicial exception. The claim further recites a measures unit configured with processing circuitry and memory, a first interface and a second interface; however interfaces are well-known generic components used to receive and output data, and processing circuitry and memory are well-known generic components for data processing. For these reasons, there is no inventive concept in the claim.
Accordingly, claim 15 is directed to non-eligible patent subject matter and is therefore rejected.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1-3, 6-8, 10-15 is rejected under 35 U.S.C. 103 as being unpatentable over Görtler et al. (EP 3486675 A1, of record, hereinafter De) in view of Weiss et al (US 20230273281 A1, of record) and Chen et al. (US 20230135995 A1, hereinafter “Chen”).
Regarding claim 1, De teaches a computer-implemented method for operating a magnetic resonance facility (MR device [0036]) to determine at least one potential cause of a false value in image data of an imaging procedure (In case of a failure, the cause and source of the detected failure should be identified automatically [0036]), comprising:
compiling an input dataset that is to be analyzed and comprises radiofrequency signal data acquired during the imaging procedure ([0007], [0040],[0050] disclose acquiring input data sets, [0065] discloses that data is acquired using RF coil elements, which is known in the art to acquire radiofrequency signals; The method may be executed during operation of the MR scanner, and even during the scans [0036]).
applying a trained artificial intelligence classification function to the input dataset to determine an output dataset that describes potential causes of the false value (the neural network system may be deployed as ANN framework with several ANNs, wherein each input (raw/image data, text data, historical data, scanner settings etc.) is processed by an appropriate deep neural network to prepare features from the data [0028]; classify images i with respect to possible failure classes [0048]; error detection [0061]-[0062]); and
outputting at least a portion of the output data of the output dataset (result/output data is disclosed in [0030]).
De, however, does not teach, wherein the radiofrequency signal data comprises sensor data acquired by at least one further radiofrequency sensor of the magnetic resonance facility that is not used for the imaging, the at least one further radiofrequency sensor comprises a pickup coil and/or a breath sensor to acquire sensor data during the imaging procedure without contributing to image formation, and determining and executing at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data.
Weiss is considered analogous to the instant application as an MR system is disclosed (abstract). Weiss teaches:
wherein the radiofrequency signal data comprises sensor data acquired by at least one further radiofrequency sensor of the magnetic resonance facility that is not used for the imaging ([0070]-[0072] disclose uses RF coils for localization rather than imaging), the at least one further radiofrequency sensor comprises a pickup coil ([0071]-[0072] discloses that the coils are a pick up coil/detection coil) and/or a breath sensor to acquire sensor data during the imaging procedure without contributing to image formation (the output signal may be used to actively control the imaging procedure in cooperation with the image controller such as in an autonomous imaging setup [0084]
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of De to include wherein the radiofrequency signal data comprises sensor data acquired by at least one further radiofrequency sensor of the magnetic resonance facility that is not used for the imaging, the at least one further radiofrequency sensor comprises a pickup coil and/or a breath sensor to acquire sensor data during the imaging procedure without contributing to image formation, as taught by Weiss. Doing so would allow for robust detection with low error rates, further improving work flow, image throughput and patient safety, as suggested by Weiss ([0025]).
The combined invention still does not teach determining and executing at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data.
Chen is analogous to the instant application as “Multi-slice mri data processing using deep learning techniques” is disclosed (title). Chen teaches determining and executing at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data (The SMS dataset 102 may also include imagery data (e.g., one or more MRI images) that visually depicts the anatomical structure based on the k-space data collected by the MRI device [0015] the SMS data is the image data as claimed; The system 100 shown in FIG. 1 may include an artificial neural network (ANN) 102 configured (e.g., trained) to remove noises (e.g., artifacts) from the SMS dataset 102 [0015]; the artificial neural network is the correction algorithm as claimed).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of De to include determining and executing at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data, as taught by Chen. Doing so would improve MRI image quality/image resolution.
Regarding claim 2, modified De teaches the method of claim 1, as discussed above. De further teaches wherein the radiofrequency signal data comprises at least a portion of the image data of the imaging procedure in k-space ([0035], [0065], figure 7 disclose the image data procedure within the k-space) and/or in a hybrid space and/or in image space.
Regarding claim 3, modified De teaches the method of claim 2, De, however does not teach: acquiring multiple k-space sections following a common excitation pulse in one shot, wherein during acquisition of the multiple k-space sections following a common excitation pulse in one shot, the k-space sections are assigned to the respective shot as an additional dimension of the radiofrequency signal data of the input dataset, and/or that, as a further dimension of the radiofrequency signal data of the input dataset, an assignment to a coil channel in which the signal data was acquired is used.
Weiss is considered analogous to the instant application as an MR system is disclosed (abstract). Weiss teaches: acquiring multiple k-space sections following a common excitation pulse in one shot ([0051]-[0053] discloses acquiring multiple k-space data samples), , wherein during acquisition of multiple k-space sections following a common excitation pulse in one shot ([0051]-[0053] discloses emitting an MR signal and acquiring multiple k-space data samples), the k-space sections are assigned to the respective shot as an additional dimension of the radiofrequency signal data of the input dataset ([0126], [0131]-[0136] discloses assigning radiofrequency data/k-space sections as in the input data space).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of De to include acquiring multiple k-space sections following a common excitation pulse in one shot, during the acquisition of multiple k-space sections following a common excitation pulse in one shot, the k-space sections are assigned to the respective shot as an additional dimension of the radiofrequency signal data of the input dataset, as taught Weiss. Doing so would allow for robust detection with low error rates, further improving work flow, image throughput and patient safety, as suggested by Weiss ([0025]).
Regarding claim 6, modified De teaches the method of claim 1, as discussed above. De further teaches wherein the input dataset comprises at least one item of supplementary information about the imaging procedure in addition to the radiofrequency signal data (other data, also in different formats are considered for failure analysis… All these factors may be used for calculation of performance indicators and may be fed into the fully connected layers of the artificial neural network. [0023]).
Regarding claim 7, modified De teaches the method of claim 6, as discussed above. De further teaches: wherein the supplementary information is selected from a group consisting of: coil information describing coils used for the imaging (coil information/coil issues disclosed as supplementary information disclosed [0028], [0030], [0048])
Regarding claim 8, modified De teaches the method of claimed in claim 7, modified, as discussed above. De further teaches: wherein the trained classification function, by using the supplementary information, determines, in relation to at least one cause, localization information describing a location of the cause as part of the output dataset (coil information/coil issues disclosed as supplementary information disclosed [0028], [0030], [0048]; the result comprises a failure source or an indication of the same. The result helps identifying the failure source or may be the failure source as such, like e.g. "failure in coil xyz" [0030]).
Regarding claim 10, modified De teaches the method of claimed in claim 1, modified, as discussed above. De further teaches: wherein at least one measure is determined and actioned based on the outputted output data (the system can suggest which parts are needed for the service to allow for better preparedness and logistics for failure processing and troubleshooting [0028]).
Regarding claim 11, modified De teaches the method of claim 10, modified, as discussed above. De further teaches: wherein the at least one measure is selected from a group consisting of: storing an entry in an error memory; outputting an alert to a user; sending a message to a maintenance service (the system can suggest which parts are needed for the service to allow for better preparedness and logistics for failure processing and troubleshooting [0028]); and applying a correction algorithm to the image data ([0030]-[0032], and [0047] discloses correcting image data).
Regarding claim 12, modified De teaches the method of claim 10, modified, as discussed above. De further teaches: wherein in order to provide the trained classification function, a pretrained classification function is provided and trained using transfer learning based on training datasets ([0025]-[0027], [0038 disclose the training phase/training datasets/pretrained classification function which are used for training data datasets) wherein each training dataset comprises an input dataset and an associated ground truth ([0028] discloses the use of an input data set/training data set along with using data from real-life application, i.e. “associated ground truth”).
Regarding claim 13, modified De teaches the method of claim 12, modified, as discussed above. De further teaches wherein at least some of the input datasets of the training datasets are determined from base datasets free of false values using characteristics information assigned to causes ([0028]-[0030], [0038] discloses identification of the failure to the source in the data set)
Regarding claim 14, De teaches a non-transitory electronically readable data medium having stored thereon a computer program having program means such that when the computer program is executed on a computing facility (storage system/memory disclosed in [0019], computer program and computer program product disclosed in [0033]-[0034]), the computing facility performs the steps of the method of claim 1 (claim 1 discussed above).
Regarding claim 15, A computing facility (computer program and computer program product disclosed in [0033]-[0034]) for determining at least one potential cause for a false value in image data of at least one imaging procedure (In case of a failure, the cause and source of the detected failure should be identified automatically [0036]), comprises:
a first interface operable to receive procedure data describing the imaging procedure and comprising at least radiofrequency signal data acquired during the imaging procedure ([0007], [0040],[0050] disclose acquiring input data sets, [0065] discloses that data is acquired using RF coil elements, which is known in the art to acquire radiofrequency signals;
a compilation unit configured with processing circuitry and memory operable to compile, from the procedure data, an input dataset that is to be analyzed and contains at least a portion of the radiofrequency signal data (input data set which includes RF data disclosed in [0007], [0040],[0050])
a classification unit configured with processing circuitry and memory operable to apply a trained artificial intelligence classification function to the input dataset to determine an output dataset that describes potential causes of the false value (the neural network system may be deployed as ANN framework with several ANNs, wherein each input (raw/image data, text data, historical data, scanner settings etc.) is processed by an appropriate deep neural network to prepare features from the data [0028]; classify images i with respect to possible failure classes [0048]; error detection [0061]-[0062]); and
a second interface operable to output at least a portion of the output data of the output dataset (the system can suggest which parts are needed for the service to allow for better preparedness and logistics for failure processing and troubleshooting [0028]).
De, however, does not teach, wherein the radiofrequency signal data comprises sensor data acquired by at least one further radiofrequency sensor of the magnetic resonance facility that is not used for the imaging, the at least one further radiofrequency sensor comprises a pickup coil and/or a breath sensor to acquire sensor data during the imaging procedure without contributing to image formation and a measures unit configured with processing circuitry and memory operable to determine and execute at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data.
Weiss is considered analogous to the instant application as an MR system is disclosed (abstract). Weiss teaches:
wherein the radiofrequency signal data comprises sensor data acquired by at least one further radiofrequency sensor of the magnetic resonance facility that is not used for the imaging ([0070]-[0072] disclose uses RF coils for localization rather than imaging), the at least one further radiofrequency sensor comprises a pickup coil ([0071]-[0072] discloses that the coils are a pick up coil/detection coil) and/or a breath sensor to acquire sensor data during the imaging procedure without contributing to image formation (the output signal may be used to actively control the imaging procedure in cooperation with the image controller such as in an autonomous imaging setup [0084].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of De to include wherein the radiofrequency signal data comprises sensor data acquired by at least one further radiofrequency sensor of the magnetic resonance facility that is not used for the imaging, the at least one further radiofrequency sensor comprises a pickup coil and/or a breath sensor to acquire sensor data during the imaging procedure without contributing to image formation, as taught by Weiss. Doing so would allow for robust detection with low error rates, further improving work flow, image throughput and patient safety, as suggested by Weiss ([0025]).
The combined inventions still does not teach a measures unit configured with processing circuitry and memory operable to determine and execute at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data.
Chen is analogous to the instant application as “Multi-slice mri data processing using deep learning techniques” is disclosed (title). Chen teaches a measures unit (apparatus [0033]) configured with processing circuitry and memory (one or more processors, one or more storage devices [0033]) operable to determine and execute at least one measure (artificial neural network (ANN) 102 [0015]) based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data (The SMS dataset 102 may also include imagery data (e.g., one or more MRI images) that visually depicts the anatomical structure based on the k-space data collected by the MRI device [0015] the SMS data is the image data as claimed; The system 100 shown in FIG. 1 may include an artificial neural network (ANN) 102 configured (e.g., trained) to remove noises (e.g., artifacts) from the SMS dataset 102 [0015]; the artificial neural network is the correction algorithm as claimed).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of De to include a measures unit configured with processing circuitry and memory operable to determine and execute at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data, as taught by Chen. Doing so would improve MRI image quality/image resolution.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Görtler et al. (EP 3486675 A1, of record, hereinafter De) in view of Weiss et al (US 20230273281 A1, of record), Chen et al. (US20230135995A1, hereinafter “Chen”), and Paul et al. (US 20210341558 A1)
Regarding claim 9, De teaches the method as claimed in claim 1, as discussed above. De, however, does not teach, wherein the trained classification function comprises a ResNet, in particular a ResNet-18, and/or an AlexNet and/or a SqueezeNet, as a neural network.
Paul is considered analogous to the instant application as “Correction of mismatches in magnetic resonance measurements”. Paul teaches: wherein the trained classification function comprises a ResNet, in particular a ResNet-18 (The convolutional neural network shown is a ResNet18 architecture for image classification [0114]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of De to include wherein the trained classification function comprises a ResNet, in particular a ResNet-18, as taught by Paul. Doing so would allow to easily identify artifacts, as suggested by Paul ([0047]).
Response to Arguments
Applicant's arguments filed 01/15/2026 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. § 101 rejection of claims 1 and 15, applicant argues on pages 7-8 that the newly added amendment “determining and executing at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data” overcomes the rejection as “amendment integrates any allegedly abstract idea into a practical application under Step 2A Prong 2 of the Alice/Mayo framework”. The examiner respectfully disagrees as applying a correction algorithm to the image data recites a mental-process type abstract idea, which can practically be performed with the with the aid of pen and paper or with a generic computer, in a computer environment, or merely using the generic computer as a tool to perform the steps, as outlined in the 35 U.S.C. § 101 rejection above. Accordingly, this argument is not persuasive and the rejection is maintained.
Regarding the 35 U.S.C. § 103 rejection of claims 1 and 15, applicant argument’s on pages 8 that Weiss does not acquire sensor data during the procedure. The examiner respectfully disagrees as the newly cited portions of Weiss above disclose that the sensors can collect data during the procedure ([0084]). Accordingly, this argument is not persuasive. Applicant further argues that the prior art does not teach the newly added amendment “determining and actioning at least one measure based on the outputted output data, wherein the at least one measure comprises applying a correction algorithm to the image data”. This argument is moot in view of new grounds of rejection which relies upon Chen et al. (US 20230135995 A1, hereinafter “Chen”), to teach this limitation. Accordingly, this argument is moot.
The applicant’s arguments on pages 9 regarding the 35 U.S.C. § 103 of the remaining claims, are premised upon the assertion that the claims are allowable for the same reasons as stated above for claim 1. The examiner respectfully disagrees for the reasons stated above.
Conclusion
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/N.B./ Examiner, Art Unit 3798
/PASCAL M BUI PHO/ Supervisory Patent Examiner, Art Unit 3798