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 .
Response to Arguments
Drawings
Applicant's arguments filed 2/6/2026 have been fully considered but they are not persuasive. Applicant states that a replacement sheet for Fig. 3 has been enclosed in the response filed 2/6/2026. However, there does not appear to be a replacement sheet on file. Therefore, as no replacement sheet has been filed, Applicant’s argument is not persuasive.
Claim Rejections 101
Applicant's arguments filed 2/6/2026 have been fully considered but they are not persuasive. Applicant argues that “Applicants amended independent claims 1, 13, and 17 to tie the claim to real world applications and particular hardware.”
First, Applicant has amended independent claim 1 to incorporate the features of now cancelled claim 2. As stated in the Non-Final Rejection mailed 11/6/2025 (“NF”) at 7-8
Claim 2, lines 2-6 recites “a trigger algorithm, which is configured to calculate at least one of the triggering times for each predicted respiratory cycle based on an extracted maximum and/or minimum amplitude values of the predicted respiratory cycles.” Under its BRI these limitations requires a mathematical calculation. Namely, an arithmetic calculation (calculating triggering times and extracting a maximum and/or minimum amplitude value). These limitations hence recites a “mathematical calculation” and so falls in to the “mathematical concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2), subsection I.C. These limitations also fall into the “mental process” grouping because they require an assessment of the extracted maximum/minimum amplitude values to identify the triggering times. Moreover, the recited mathematical calculation is simple enough that it can be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
Therefore, the alleged claim features recited in amended independent claim 1, lines 13-16 recite a judicial exception.
Further, the recitation of “wherein the computer, with the triggering module, is configured to implement a trigger algorithm” in amended independent claim 1, lines 13-14 amounts to no more than mere instructions to apply the exception using a generic computer. As stated in the NF at 5
The additional element of “a computer configured with a computation module” in claim 1, lines 6-7 and “configured with a triggering module” recites a computer and associated computation module and triggering module at a high level of generality. The computer is used to perform the abstract idea of lines 6-12, implementing the AI model to generate a prediction of future respiration and identify future triggering timing within the predicted future respiration, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Similarly, the additional element of “the computer, with the triggering module, is configured to implement a trigger algorithm” in claim 1, lines 13-14 recites a computer and associated computation module and triggering module at a high level of generality. The computer is used to perform the abstract idea of lines 13-16, implementing the trigger algorithm to calculate triggering times for each predicted respiratory cycle, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Furthermore, as stated in the NF at 6
The recitation of a computer configured with a computation module and triggering module to perform the limitations of claim 1, lines 6-12 amounts to no more than mere instructions to apply the exception using a generic computer component.
Similarly, the recitation of the computer, with the triggering module, configured to implement a trigger algorithm to perform the limitations of claim 1, lines 13-16 amounts to no more than mere instructions to apply the exception using a generic computer component.
Therefore, the alleged claim features recited in amended independent claim 1, lines 13-14 are additional elements that amount to no more than mere instruction to apply the exception using a generic computer as it recites a computer and associated triggering module at a high level of generality.
Second, Applicant has amended independent claim 13 to incorporate the features of now cancelled claim 14. The features of amended independent claim 13, lines 9-11 are substantially similar to those of amended independent claim 1, lines 13-16 and are addressed as above.
Third, Applicant has amended independent claim 17 to incorporate features substantially similar to those of now cancelled claim 14, which as stated above, are substantially similar to those of amended independent claim 1, lines 13-16 and are addressed as above.
Thus, Applicant’s arguments are not persuasive.
Rejections under 102 or 103
Applicant's arguments filed 2/6/2026 have been fully considered but they are not persuasive.
Applicant argues that “He et al. do not, however, teach or disclose calculating triggering times for each respiratory cycle of a number of predicted respiratory cycles, as required by independent claim 1.” Remarks at 9. Applicant limits the arguments to the disclosure of He paragraphs [0033]-[0035] and fails to consider that not only paragraphs [0035]-[0036], but also paragraphs [0138]-[0140] and [0083]-[0086] and Fig. 3 were cited in the NF to support the finding that He discloses the alleged claim features. As stated in the NF at 13
He discloses the triggering module implements a trigger algorithm, which is configured to calculate at least one of the triggering times for each predicted respiratory cycle based on an extracted maximum and/or minimum amplitude values of the predicted respiratory cycles (triggering detection module, e.g., circuitry, configured to calculate a triggering time for the current respiratory signal period based on an extracted maximum and/or minimum amplitude value of the current respiratory signal period, [0035]-[0036]; predicted respiration signal is employed as the current respiration signal for the determination of the triggering of the MR data acquisition, [0138]-[0140]; triggering is performed for a number of respiratory periods of the patient, [0083]-[0086], Fig. 3).
Applicant fails to address He’s disclosure that the predicted respiration signal is employed as the current respiration signal for the determination of the triggering of the MR data acquisition in paragraphs [0138]-[0140] and that triggering is performed for a number of respiratory periods of the patient in paragraphs [0083]-[0086] and Fig. 3. In context, the disclosure of paragraphs [0138]-[0140] and [0083]-[0086] and Fig. 3 contribute to the disclosure of paragraphs [0035]-[0036] and are not isolated therefrom. A proper reading of He’s disclosure as disclosed across not only paragraphs [0035]-[0036], but also paragraphs [0138]-[0140] and [0083]-[0086] and Fig. 3 discloses that the triggering detection module, e.g., circuitry, is configured to calculate a triggering time for the predicted respiratory signal period based on an extracted maximum and/or minimum amplitude value of the predicted respiratory signal period for a number of predicted respiratory periods of the patient. Thus, contrary to Applicant’s contention, He discloses the alleged claim features.
Therefore, applicant’s arguments are not persuasive.
Drawings
Figure 3 should be designated by a legend such as --Prior Art-- because only that which is old is illustrated. See MPEP § 608.02(g). Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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, 3-13, and 15-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 being illustrative:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. MPEP 2106.03. The claim recites at least one step or act of diagnosing and addressing multiple sclerosis. Thus, the claim is a process, which is a statutory category of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claim 1, lines 6-10 recites “to implement an artificial intelligence entity, which is trained to generate a prediction of a number of respiratory cycles of the patient based on the obtained real-time data.” Under its broadest reasonable interpretation consistent with the specification, the plain and ordinary meaning of this limitation requires a prediction of future respiratory cycles by applying an AI model to real-time data of respiratory cycles of a patient. The “artificial intelligence entity” as recited encompasses a trained clinician that reviews acquired respiratory cycle information and makes a judgement to predict the shape of a number of future respiratory cycles. As this step requires an evaluation of the patient’s current respiratory cycle information and use of the clinician’s knowledge of the shape of the respiratory cycle curve/waveform/signal to make a prediction of the future shape, this limitation falls in to the “mental process” grouping of abstract ideas because the evaluation can be practically performed in the human mind.
Claim 1, lines 10-12 recites “to determine the triggering times corresponding to the predicted respiratory cycles.” Under its broadest reasonable interpretation consistent with the specification, the plain and ordinary meaning of this limitation requires an evaluation of the predicted respiratory cycles to identify triggering times within those cycles. The determination as recited encompasses a trained clinician that reviews acquired respiratory cycle information and makes a judgement to predict the shape of a number of future respiratory cycles and preferred locations at which to acquire MR image data, e.g., regions of limited movement of the patient such as the maximum or minimum portions of the respiratory curve given the cyclic/periodic/repeating nature of respiration. As this step requires an evaluation of the patient’s current respiratory cycle information and use of the clinician’s knowledge of the shape of the respiratory cycle curve/waveform/signal to make a prediction of the future shape and the preferred locations for triggering MR image acquisition, this limitation falls in to the “mental process” grouping of abstract ideas because the evaluation can be practically performed in the human mind.
Claim 1, lines 13-16 recites “to implement a trigger algorithm, which is configured to calculate at least one of the triggering times for each predicted respiratory cycle based on an extracted maximum and/or minimum amplitude values of the predicted respiratory cycles.” Under its BRI these limitations requires a mathematical calculation. Namely, an arithmetic calculation (calculating triggering times and extracting a maximum and/or minimum amplitude value). These limitations hence recites a “mathematical calculation” and so falls in to the “mathematical concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2), subsection I.C. These limitations also fall into the “mental process” grouping because they require an assessment of the extracted maximum/minimum amplitude values to identify the triggering times. Moreover, the recited mathematical calculation is simple enough that it can be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. MPEP 2106.04(d).
The preamble, lines 1-3, to claim 1 recites “a computing device for providing triggering times to a medical scanning apparatus adapted to perform triggered imaging data acquisition.” The computing device is recited at a high level of generality. As discussed below, the computing device is used to perform an abstract idea, implementing the AI model to generate a prediction of future respiration and identify future triggering timing within the predicted future respiration, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Furthermore, the preamble merely provides the identified triggering times to the relevant audience (the medical scanning apparatus and the clinician or other medical professional operating the medical scanning apparatus) and at most adds a suggestion to use the triggering time to trigger imaging data acquisition using the medical scanning apparatus. The preamble may thus be understood as no more than an attempt to generally link the judicial exception to a field of use. See MPEP 2106.05(h). Therefore, the preamble fails to meaningfully limit the claim because it does not require any particular application of the abstract idea and therefore amounts only to a generic instruction to “apply” the exception or to a mere indication of the field of use or technological environment in which the abstract idea is performed.
The additional element of “an input data interface configured to obtain real-time data of respiratory cycles of a patient” in claim 1, lines 4-5 recites the extra-solutionary activity of mere necessary data gathering in that receiving the real-time data of respiratory cycles of a patient is required for all uses of the recited abstract idea such that respiratory cycle information is available for review by the clinician/model for the future respiratory cycle prediction and triggering time identification. As all uses of the recited judicial exceptions require such data gathering, this limitation does not impose any meaningful limits on the claim. This limitation amounts to necessary data gathering. See MPEP 2106.05.
The additional element of “a computer configured with a computation module” in claim 1, lines 6-7 and “configured with a triggering module” recites a computer and associated computation module and triggering module at a high level of generality. The computer is used to perform the abstract idea of lines 6-12, implementing the AI model to generate a prediction of future respiration and identify future triggering timing within the predicted future respiration, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The recitation of “implementing an artificial intelligence entity” in claim 1, line 7 also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “implementing an artificial intelligence entity” limits the identified judicial exceptions of generating a prediction of future respiration and identifying future triggering timing within the predicted future respiration this type of limitation merely confines the use of the abstract idea to a particular technological environment (artificial intelligence models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
The additional element of “an output data interface configured to output the determined triggering times to the medial scanning apparatus” in claim 1, lines 13-14 recites the extra-solutionary activity of outputting results in that output of the triggering times to the medical scanning apparatus is required for all uses of the recited abstract idea such that result of the evaluation, the triggering times, are available for use/display by the medical scanning apparatus. As all uses of the recited judicial exceptions require such result output, this limitation does not impose any meaningful limits on the claim. This limitation amounts to necessary result output. See MPEP 2106.05.
The additional element of “the computer, with the triggering module, is configured to implement a trigger algorithm” in claim 1, lines 13-14 recites a computer and associated computation module and triggering module at a high level of generality. The computer is used to perform the abstract idea of lines 13-16, implementing the trigger algorithm to calculate triggering times for each predicted respiratory cycle, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
While the disclosure states that invention “performs an enhanced determination of the scanning acquisition times through an improved calculation of the triggering time” (applicant’s specification paragraph [011]) there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of predicting future respiratory cycles using current respiratory cycle information and identifying triggering times in the predicted respiratory cycles which may be performed by a clinician rather than to any technology. See MPEP 2106.05(a). Thus, even when considering the elements in combination, the claim as a whole does not integrate the recited exception into a practical application. (Step 2A, Prong Two: NO). Thus, claim 1 is directed to a judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05.
These additional elements should be re-evaluated in Step 2B, in which the extra-solution activity consideration takes into account whether or not an extra-solution activity is well-known.
The preamble fails to meaningfully limit the claim because it does not require any particular application of the abstract idea and therefore amounts only to a generic instruction to “apply” the exception or to a mere indication of the field of use or technological environment in which the abstract idea is performed. Further, the preamble’s recitation of a computing device to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component.
The data gathering activities claim 1, lines 4-5 and the output in claim 1, lines 13-14 are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
The recitation of a computer configured with a computation module and triggering module to perform the limitations of claim 1, lines 6-12 amounts to no more than mere instructions to apply the exception using a generic computer component.
The additional element of “to implement an artificial intelligence entity” in claim 1, line 11 is at best mere instruction to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
The recitation of the computer, with the triggering module, configured to implement a trigger algorithm to perform the limitations of claim 1, lines 13-16 amounts to no more than mere instructions to apply the exception using a generic computer component.
Consequently, for the reasons discussed above, the additional elements individually or in combination with the judicial exception do not provide an inventive concept; so, the claim as a whole does not amount to significantly more than a generic instruction to “apply” the judicial exception. (Step 2B: NO). The claim is not eligible.
Regarding independent claim 13, the features not addressed below are addressed as above for claim 1. Independent claim 13, line 1 further recites “[a] computer-implemented method.” This recites a computer at a high level of generality. The computer is used to perform the abstract idea of lines 7-11, implementing the AI model to generate a prediction of future respiration and identify future triggering timing within the predicted future respiration and the abstract idea of lines 9-11, implementing the trigger algorithm to calculate triggering times for each predicted respiratory cycle, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Therefore, the claim is not eligible.
Regarding independent claim 17, the features not addressed below are addressed as above for claim 1. Independent claim 17, lines 1-3 further recites “[a] non-transitory computer-readable data storage medium comprising executable program code, which is configured, when executed by a processor.” This recites a storage medium, software program code, and processor at a high level of generality. The software program code stored on the storage medium is used to perform the abstract idea of lines 6-10 implementing the AI model to generate a prediction of future respiration and identify future triggering timing within the predicted future respiration and the abstract idea of lines 9-11, implementing the trigger algorithm to calculate triggering times for each predicted respiratory cycle, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Therefore, the claim is not eligible.
Turning to the dependent claims:
Claim 3 merely specifies that the AI entity is a specific type of AI, a deep learning network.
Claim 4 merely specifies that the deep learning network is a specific type of deep learning network, a multilayer perceptron network.
Claim 5 merely specifies that the deep learning network is trained using baseline or patient-specific data. This merely specifies that the clinician/AI model has been trained using prior baseline or patient specific data.
Claim 6 recites “a configuration unit configured to set the data acquisition time.” This recites a configuration unit of the triggering module at a high level of generality. This amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Further, the setting of the data acquisition time is the extra-solutionary activity of mere necessary data gathering in that receiving data acquisition time is required to define the triggering and acquisition of the MR image data such that the information is available for review by the clinician/model for the triggering time identification. As all uses of the recited judicial exceptions require such data gathering, this limitation does not impose any meaningful limits on the claim. This limitation amounts to necessary data gathering. See MPEP 2106.05.
Claim 7 recites “the data-acquisition time is adapted to provide amplitude-symmetric triggered imaging data acquisitions.” Under its BRI these limitations requires a mathematical calculation. Namely, an arithmetic calculation (adapting the data-acquisition time such that the triggered imaging data acquisitions are amplitude-symmetric). These limitations hence recites an adaptation of the data-acquisition time and so falls in to the “mathematical concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2), subsection I.C. These limitations also fall into the “mental process” grouping because they require an assessment of the duration and position of the triggering times and the duration of the data-acquisition time relative to the maximum/minimum of the respiratory curve. Moreover, the recited mathematical calculation is simple enough that it can be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
Claim 8 recites “a threshold unit.” This recites a threshold unit of the triggering module at a high level of generality. This amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Claim 8 further recites “to set an amplitude threshold between 5% and 30% of a difference between an average minimum and an average maximum amplitude values of the obtained respiratory cycles.” These limitations hence recites a “setting” and “a difference” and so falls in to the “mathematical concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2), subsection I.C. These limitations also fall into the “mental process” grouping because they require an assessment in setting a threshold between 5% and 30 relative to the max/min difference of the respiratory curve. Moreover, the recited mathematical calculation is simple enough that it can be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
Claim 9 recites “a rating module.” This recites a rating module of the computer module at a high level of generality. This amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Claim 9, lines 4-8 further recites “to generate a quality rating value for each acquired triggering imaging data acquisition based on time-wise symmetry… and/or based on the difference…” These limitations hence recites “generat[ing] a quality rating value,” evaluating “time-wise symmetry,” and calculating the “difference” and so falls in to the “mathematical concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2), subsection I.C. Moreover, the recited mathematical calculation is simple enough that it can be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
Claim 10 recites “a graphical user interface.” This recites a graphical user interface of the rating module at a high level of generality. This amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Claim 10, lines 3-5 recites “to output a plot comprising an information about the triggering times, the corresponding data-acquisition times, and an amplitude of het respiratory cycle of the patient. These limitations recite the extra-solutionary activity of outputting results by output/display of the calculated and evaluated triggering times, data-acquisition times, and amplitude of the respiratory cycle of the patient that is required for all uses of the recited abstract idea such that result of the calculations and evaluation, are available for use/display by the medical scanning apparatus. As all uses of the recited judicial exceptions require such result output, this limitation does not impose any meaningful limits on the claim. This limitation amounts to necessary result output. See MPEP 2106.05.
Claim 11 recites “a tolerance unit.” This recites a tolerance unit of the rating module at a high level of generality. This amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Claim 11, lines 3-6 recites “to separate, based on a tolerance threshold of the quality rating values, the acquired triggered imaging data acquisitions into two groups, wherein the tolerance threshold is set by a user.” Under its broadest reasonable interpretation consistent with the specification, the plain and ordinary meaning of this limitation requires an evaluation by the user of what level of quality, the user defined tolerance threshold, that is tolerable/acceptable and sorting the data into two groups according to the user’s desired tolerance level. This encompasses a trained clinician that reviews the acquired triggered medical imaging acquisitions and makes a judgement about whether the acquired triggered medical images are of sufficient quality to retain. As this step requires an evaluation of the acquired triggered imaging data to determine if the data is sufficient quality, this limitation falls in to the “mental process” grouping of abstract ideas because the evaluation can be practically performed in the human mind. These limitations also fall into the “mathematical concepts” grouping of abstract ideas as sorting according to a comparison to a threshold involves a mathematical calculation. See MPEP 2106.04(a)(2), subsection I.C. Moreover, the recited mathematical calculation is simple enough that it can be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
Claim 12 recites “an optimization unit” This recites an optimization unit of the triggering module at a high level of generality. This amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Claim 12, lines 3-4 recites “to optimize the trigger algorithm with another artificial intelligence entity.” Under its broadest reasonable interpretation consistent with the specification, the plain and ordinary meaning of this limitation requires implementation of an AI model for the trigger algorithm. The “another artificial intelligence entity” as recited encompasses a trained clinician that mathematical calculations and mental process evaluations discussed above regarding claim 2. Claim 12, lines 4-7 recites “to import an optimized trigger algorithm from another computing device and/or to export the optimized trigger algorithm to the other computing device.” These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
Claim 15 recites subject matter similar to that of claim 7 and is addressed as above.
Claim 16 recites subject matter similar to that of claim 9 and is addressed as above.
Therefore, claims 1, 3-13, and 15-17 are not eligible and are rejected under 35 U.S.C. 101.
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, and 12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by He et al. (U.S. Pub. No. 2021/0378603), hereinafter “He.”
Regarding claim 1, He discloses a computing device for providing triggering times to a medical scanning apparatus adapted to perform triggered imaging data acquisition (an apparatus for triggering magnetic resonance data acquisition comprising a processor, [0050]), said computing device comprising:
an input data interface configured to obtain real-time data of respiratory cycles of a patient (acquire respiration signals for respiration periods of the patient and input the respiration signals in real time, [0134]-[0138]; respiration signal prediction module, e.g., circuitry, is configured to input the respiration signal of the acquisition subject, [0048]);
a computer configured with a computation module configured to implement an artificial intelligence entity, which is trained to generate a prediction of a number of respiratory cycles of the patient based on the obtained real-time data (an apparatus comprising a processor or computer, [0050], [0160]; respiration signal prediction module, e.g., circuitry, configured to predict a respiration signal of the patient based on the input real-time respiration signal, [0043]-[0048], [0138]; prediction is performed for a number of respiratory periods of the patient, [0125]; respiration signal prediction is performed using a trained neural network, [0047]-[0048]), and configured with a triggering module configured to determine the triggering times corresponding to the predicted respiratory cycles (triggering detection module, e.g., circuitry, configured to determine triggering of the MR data acquisition corresponding to the current respiration signal, [0036]; predicted respiration signal is employed as the current respiration signal for the determination of the triggering of the MR data acquisition, [0138]-[0140]; triggering is performed for a number of respiratory periods of the patient, [0083]-[0086], Fig. 3); and
an output data interface configured to output the determined triggering times to the medical scanning apparatus (triggering detection module, e.g., circuitry, is configured to output the determined triggering time to the MR data acquisition apparatus to trigger MR data acquisition, [0036]; triggering is performed for a number of respiratory periods of the patient, [0083]-[0086], Fig. 3),
wherein the computer, with the triggering module, is configured to implement a trigger algorithm, which is configured to calculate at least one of the triggering times for each predicted respiratory cycle based on an extracted maximum and/or minimum amplitude values of the predicted respiratory cycles (an apparatus comprising a processor or computer, [0050], [0160]; triggering detection module, e.g., circuitry, configured to calculate a triggering time for the current respiratory signal period based on an extracted maximum and/or minimum amplitude value of the current respiratory signal period, [0035]-[0036]; predicted respiration signal is employed as the current respiration signal for the determination of the triggering of the MR data acquisition, [0138]-[0140]; triggering is performed for a number of respiratory periods of the patient, [0083]-[0086], Fig. 3).
Regarding claim 3, He discloses the artificial intelligence entity of the computation module is a deep learning network (the neural network is a deep learning network such as a TCN, RNN, or LSTM, Title, [0032]).
Regarding claim 5, He discloses the deep learning network is trained with baseline data and/or trained with patient-specific data of the patient (the respiration signal predictive model is trained with baseline and/or trained with patient-specific data of the patient, [0047]).
Regarding claim 12, He discloses the triggering module further comprises an optimization unit configured to optimize the trigger algorithm with another artificial intelligence entity and/or to import an optimized trigger algorithm from another computing device and/or to export the optimized trigger algorithm to the other computing device (software program code embodies the modules (including the triggering detection module of [0148], respiration direction detection module of [0149]-[0150], expiration peak value predictive module of [0151]-[0152], and respiration signal predictive model establishing module and respiration signal prediction module of [0153]-[0156] comprising the neural network models) and such program code can be downloaded from a server computer or a cloud via communication network and can be implemented across one or more devices, [0158]-[0164]).
Claim 13 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by He.
Regarding claim 13, He discloses a computer-implemented method for providing triggering times to a medical scanning apparatus adapted to perform triggered imaging data acquisition (a non-transitory computer-readable medium having a computer program stored thereon, wherein when executed by a processor realizes a method of providing triggering for magnetic resonance data acquisition, [0049]; a method for triggering magnetic resonance data acquisition, [0012]), the computer-implemented method comprising:
obtaining real-time data of respiratory cycles of a patient (acquire respiration signals for respiration periods of the patient and input the respiration signals in real time, [0134]-[0138]; respiration signal prediction module, e.g., circuitry, is configured to input the respiration signal of the acquisition subject, [0048]);
generating, using an artificial intelligence entity, a prediction of a number of respiratory cycles of the patient based on the obtained real-time data (respiration signal prediction module, e.g., circuitry, configured to predict a respiration signal of the patient based on the input real-time respiration signal, [0043]-[0048], [0138]; prediction is performed for a number of respiratory periods of the patient, [0125]; respiration signal prediction is performed using a trained neural network, [0047]-[0048]),
determining triggering times corresponding to the predicted respiratory cycles (triggering detection module, e.g., circuitry, configured to determine triggering of the MR data acquisition corresponding to the current respiration signal, [0036]; predicted respiration signal is employed as the current respiration signal for the determination of the triggering of the MR data acquisition, [0138]-[0140]; triggering is performed for a number of respiratory periods of the patient, [0083]-[0086], Fig. 3); and
outputting the triggering times to the medical scanning apparatus triggering detection module, e.g., circuitry, is configured to output the determined triggering time to the MR data acquisition apparatus to trigger MR data acquisition, [0036]; triggering is performed for a number of respiratory periods of the patient, [0083]-[0086], Fig. 3),
wherein determining the triggering times comprises calculating, by a computer, at least one of the triggering times for each predicted respiratory cycle based on an extracted maximum and/or minimum amplitude values of the predicted respiratory cycles (an apparatus comprising a processor or computer, [0050], [0160]; triggering detection module, e.g., circuitry, configured to calculate a triggering time for the current respiratory signal period based on an extracted maximum and/or minimum amplitude value of the current respiratory signal period, [0035]-[0036]; predicted respiration signal is employed as the current respiration signal for the determination of the triggering of the MR data acquisition, [0138]-[0140]; triggering is performed for a number of respiratory periods of the patient, [0083]-[0086], Fig. 3).
Claim 17 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by He.
Regarding claim 17, He discloses a non-transitory computer-readable data storage medium comprising executable program code, which is configured, when executed by a processor (a non-transitory computer-readable medium having a computer program stored thereon, wherein when executed by a processor realizes a method of providing triggering for magnetic resonance data acquisition, [0049]), to:
obtain real-time data of respiratory cycles of a patient (acquire respiration signals for respiration periods of the patient and input the respiration signals in real time, [0134]-[0138]; respiration signal prediction module, e.g., circuitry, is configured to input the respiration signal of the acquisition subject, [0048]);
generate, using an artificial intelligence entity, a prediction of respiratory cycles of the patient based on the obtained real-time data (respiration signal prediction module, e.g., circuitry, configured to predict a respiration signal of the patient based on the input real-time respiration signal, [0043]-[0048], [0138]; prediction is performed for a number of respiratory periods of the patient, [0125]; respiration signal prediction is performed using a trained neural network, [0047]-[0048]);
determine triggering times corresponding to the predicted respiratory cycles (triggering detection module, e.g., circuitry, configured to determine triggering of the MR data acquisition corresponding to the current respiration signal, [0036]; predicted respiration signal is employed as the current respiration signal for the determination of the triggering of the MR data acquisition, [0138]-[0140]; triggering is performed for a number of respiratory periods of the patient, [0083]-[0086], Fig. 3); and
outputting the triggering times to a medical scanning apparatus (triggering detection module, e.g., circuitry, is configured to output the determined triggering time to the MR data acquisition apparatus to trigger MR data acquisition, [0036]; triggering is performed for a number of respiratory periods of the patient, [0083]-[0086], Fig. 3),
wherein the determination of the triggering times comprises calculation, by a computer, of at least one of the triggering times for each predicted respiratory cycle based on an extracted maximum and/or minimum amplitude values of the predicted respiratory cycles (an apparatus comprising a processor or computer, [0050], [0160]; triggering detection module, e.g., circuitry, configured to calculate a triggering time for the current respiratory signal period based on an extracted maximum and/or minimum amplitude value of the current respiratory signal period, [0035]-[0036]; predicted respiration signal is employed as the current respiration signal for the determination of the triggering of the MR data acquisition, [0138]-[0140]; triggering is performed for a number of respiratory periods of the patient, [0083]-[0086], Fig. 3).
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 4 is rejected under 35 U.S.C. 103 as being unpatentable over He as in claim 1, above, and further in view of Sun et al. (“Adaptive respiratory signal prediction using dual multi-layer perceptron neural networks” 2020), hereinafter “Sun.”
Regarding claim 4, while He discloses a deep learning network (the neural network is a deep learning network such as a TCN, RNN, or LSTM, Title, [0032]), He does not appear to disclose the deep learning network is a multilayer perceptron network.
However, in the same field of endeavor of respiratory signal prediction, Sun teaches the deep learning network is a multilayer perceptron network (multi-layer perceptron neural network model is used to predict respiratory signals, Abstract, 2.2 Prediction process, 2.4 Evaluation Methods).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Sun’s known technique of using a multi-layer perceptron neural network model to predict respiratory signals to He’s known apparatus using a deep learning neural network model to predict respiratory signals to achieve the predictable result that a multi-layer perceptron neural network model “obtain[s] an effective prediction performance for non-linear respiratory signals.” See, e.g., Sun, P.2, ¶1.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over He as in claim 1, above, and further in view of Yanof et al. (U.S. Pub. No. 2003/0188757), hereinafter “Yanof.”
Regarding claim 6, He does not appear to disclose the triggering module comprises a configuration unit configured to set the data acquisition time.
However, in the same field of endeavor of respiratory triggering, Yanof teaches the triggering module comprises a configuration unit configured to set the data acquisition time (integrated triggering function adapts to the scanner’s image acquisition capabilities with the acquisition time interval being set according to the scan speed, [0056]-[0057]; triggering function is integrated in the computer of the respiratory monitor system, [0034], [0038], [0041], [0050]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Yanof’s known technique of setting the data acquisition time for acquisition following triggering to He’s known apparatus for automatically triggering MR data acquisition to achieve the predictable result that this allows the automated triggering to be adapted for improvements in scan speed as scanner technology improves. See, e.g., Yanof, [0056].
Regarding claim 7, He does not appear to disclose the data-acquisition time is adapted to provide amplitude symmetric triggered imaging data acquisitions.
However, in the same field of endeavor of respiratory triggering, Yanof teaches the data-acquisition time is adapted to provide amplitude symmetric triggered imaging data acquisitions (acquisition time interval is adjusted such that it is centered about the minimum/maximum of the respiratory cycle, [0057]-[0059], Fig. 10.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Yanof’s known technique of centering the acquisition time interval about the minimum/maximum of the respiratory cycle to He’s known apparatus for automatically triggering MR data acquisition to achieve the predictable result that centering on the minimum/maximum of the respiratory cycles minimizes motion effects. See, e.g., Yanof, [0057].
Claim 8 is rejected under 35 U.S.C. 102(a)(1) as anticipated by He or, in the alternative, under 35 U.S.C. 103 as obvious over He in further view of Kartausch et al. (U.S. Pub. No. 2020/0077962), hereinafter “Kartausch.“
Regarding claim 8, He discloses the triggering module comprises a threshold unit configured to set an amplitude threshold between 5% and 30% of a difference between an average minimum and an average maximum amplitude values of the obtained respiratory cycles (trigger point threshold is set at 80% of the expiration amplitude peak based on the average of several previous respiration periods, noting that 80% of the expiration amplitude peak corresponds to 20% of the difference between the minimum and maximum amplitude values of the obtained respiration period, [0071], [0083]-[0085], Fig. 1).
Additionally, or, in the alternative, while He discloses the triggering module comprises a threshold unit configured to set an amplitude threshold between 5% and 30% of a difference between an several minimum and several maximum amplitude values of the obtained respiratory cycles, He may not explictly disclose that the several minimum and several maximum amplitude values are average values.
However, in the same field of endeavor of respiratory gating, Kartausch teaches the several minimum and several maximum amplitude values are average values (complete exhalation trigger threshold value is set below 20% of the difference between an average minimum and an average maximum amplitude values of the obtained respiratory cycles, [0064], [0073]-[0074]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Kartausch’s known technique of setting the trigger threshold using a percentage of the difference between an average minimum and an average maximum amplitude values of the obtain respiratory cycles to He’s known apparatus for setting the trigger threshold using a percentage of the difference between a minimum and maximum amplitude values of the obtained respiratory cycles to achieve the predictable result that identifying the complete exhalation respiration state is well suited for magnetic resonance scans as it is the longest respiration state. See, e.g., Kartausch, [0073].
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over He as in claim 1, above, and further in view of Yanof.
Regarding claim 15, He does not appear to disclose determining the triggering times further comprises setting a data acquisition time adapted to provide amplitude-symmetric triggered imaging data acquisitions.
However, in the same field of endeavor of respiratory triggering, Yanof teaches determining the triggering times further comprises setting a data acquisition time adapted to provide amplitude-symmetric triggered imaging data acquisitions (acquisition time interval is adjusted such that it is centered about the minimum/maximum of the respiratory cycle, [0057]-[0059], Fig. 10.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Yanof’s known technique of centering the acquisition time interval about the minimum/maximum of the respiratory cycle to He’s known process of automatically triggering MR data acquisition to achieve the predictable result that centering on the minimum/maximum of the respiratory cycles minimizes motion effects. See, e.g., Yanof, [0057].
Allowable Subject Matter
Claims 9-11 and 16 are rejected under 35 U.S.C. 101, but would be allowable if amended to overcome the rejection under 35 U.S.C. 101 and rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sun et al. (“Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network” 2017) discloses prediction of respiratory signals using a multi-layer perceptron neural network.
Bukovsky et al. (“A fast neural network approach to predict lung tumor motion during respiration for radiation therapy applications” 2015) discloses prediction of respiratory signals using a multi-layer perceptron neural network.
Kartausch et al. (EP3510918A1) discloses prediction of respiratory signals and triggering using a deep learning neural network.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.M./Examiner, Art Unit 3798
/KEITH M RAYMOND/Supervisory Patent Examiner, Art Unit 3798