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 .
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 1-6 and 8 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 enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding claim 1, the claim recites the limitation “trained beforehand according to a process of learning the disruptive effect of the acquisition of a perfusion sequence on arterial signals and the redundancy of the information in relation to such a second arterial input function shared by a set of at least two tissue signals”, without providing any details as to how to perform such training. The original Specification, as presented, fails to describe the invention in such terms that one skilled in the art can make and use the claimed invention without undue experimentation and therefore fails the enablement requirement set forth under 35 U.S.C. 112(a). See MPEP 2164, citing In re Wands, 858 F.2d 731, 737 (Fed. Cir. 1988).
The following factors are to be considered when determining whether there is sufficient evidence to support a determination that a disclosure does not satisfy the enablement requirement and whether any necessary experimentation is “undue”:
(A) the breadth of the claims;
(B) the nature of the invention;
(C) the state of the prior art;
(D) the level of one of ordinary skill;
(E) the level of predictability in the art;
(F) the amount of direction provided by the inventor;
(G) the existence of working examples; and
(H) the quantity of experimentation needed to make or use the invention based on the content of the disclosure (see MPEP 2164.01(a)).
Here, for example:
(A) Breadth of the claims – recites “trained beforehand according to a process of learning the disruptive effect of the acquisition of a perfusion sequence on arterial signals and the redundancy of the information in relation to such a second arterial input function shared by a set of at least two tissue signals” which involves any sort of training performed on machine learning to perfusion imaging related to disruptive effects and redundancy.
(B) Nature of the invention – the invention is directed to a method and system for training a machine learning system, the training of which is the most complicated, important, and time consuming aspect of machine learning.
(D) Level of ordinary skill – To date, one of ordinary skill in the art would recognize that there are very large number of different ways to train machine learning.
(F) Amount of direction provided by the inventor – The original Specification, as presented, provides no direction as to how one of ordinary skill in the art could train machine learning in the described manner.
(G) Existence of working examples – The Applicant does not provide any working examples in the original Specification.
In light of the above, it is asserted that the original Specification, as presented, fails to describe the invention in such terms that one skilled in the art can make and use the claimed invention without undue experimentation. Accordingly, claim 1 is rejected under 35 U.S.C. 112(a).
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 1-6 and 8 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.
Regarding claim 1, the claim recites the limitation “sampled temporal experimental signal” and “a set of tissue signals”. It is unclear how these signals relate. Clarification is required. For examination purposes, these limitations will be interpreted as referring to any tissue signals, not necessarily the same signals.
Regarding claim 5, the limitation “the Adam optimizer” lacks antecedent basis, no Adam optimizer has previously been set forth. For examination purposes, this limitation will be interpreted as reciting an Adam optimizer.
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 6 and 8 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the limitations “computer-readable storage medium” read on transitory forms of signal transmission. See MPEP 2106.03.I. To overcome this rejection the Examiner recommends amending the claims to recite “non-transitory computer-readable storage medium” .
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6, and 8 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Arnold et al., (US20190150764, hereafter Arnold).
Regarding claim 1, Arnold discloses a method for post-processing a sampled temporal experimental signal resulting from a perfusion acquisition sequence by a medical imaging device (Arnold, Para 3; “The present disclosure relates generally to medical imaging. More particularly, the present disclosure is directed to systems and methods for analyzing perfusion imaging.”) (Arnold, Para 14; "In accordance with one aspect of the disclosure, a method for estimating perfusion parameters using medical imaging is provided. The method includes receiving a perfusion imaging dataset acquired from a subject using an imaging system") and resulting from the passage of a tracer within an elementary volume of an organ (Arnold, Para 9; "MR imaging, and more specifically perfusion-weighted MR imaging, is a common modality used in the diagnosis and treatment of patients with brain pathologies, such as stroke or cancer. Specifically, perfusion-weighted images (“PWI”) are typically obtained by injecting a contrast bolus, such as a gadolinium chelate, into a patient's bloodstream. Images are then acquired as the bolus passes through the patient using dynamic susceptibility contrast (“DSC”) or dynamic contrast enhanced (“DCE”) techniques. The susceptibility effect of the paramagnetic contrast leads to signal loss that can be used to track contrast concentration in specific tissues over time. By applying various models to the resulting concentration-time curves, a number of perfusion parameters can be determined, such as blood volume (“BV”), blood flow (“BF”), mean transit time (“MTT”), time-to-peak (“TIP”), time-to-maximum (“Tmax”), maximum signal reduction (“MSR”), first moment (“FM”), and others. These can be used to determine a chronic or acute condition of the patient. For example, Tmax and MTT have been used to predict a risk of infarction."), said method being implemented by a processing unit of a medical imaging analysis system (Arnold, Para 13; “The present disclosure introduces systems and methods for estimating perfusion parameters using medical imaging.”), said method including:
- a step of selecting a first arterial input function in relation to an arterial region of the organ and a set of tissue signals respectively in relation to separate tissue regions of said organ (Arnold, Para 15; "The instructions include accessing a perfusion imaging dataset acquired from a subject using an imaging system, selecting a voxel in the perfusion imaging dataset, and assembling for the selected voxel a perfusion patch extending in at least two spatial dimensions around the selected voxel and time. The instructions also include pairing the perfusion patch with an arterial input function (AIF) patch corresponding to the selected voxel, and estimating at least one perfusion parameter for the selected voxel by propagating the perfusion patch and AIF patch through a trained convolutional neural network (CNN) that is configured to receive a pair of inputs.") (Arnold, Para 35; “To do so, the processor 104 may select a number of voxels, or regions of interest, in a perfusion image or a perfusion image set and then generate various input patches using the selected voxels.”);
- a step of generating a second arterial input function on the basis of said first arterial input function and of said set of selected tissue signals (Arnold, Para 15; "The instructions also include pairing the perfusion patch with an arterial input function (AIF) patch corresponding to the selected voxel, and estimating at least one perfusion parameter for the selected voxel by propagating the perfusion patch and AIF patch through a trained convolutional neural network (CNN) that is configured to receive a pair of inputs.") (Arnold, Para 59; “The perfusion patch may be paired with an AIF patch corresponding to the selected voxel at process block 206, where the AIF patch is generated using the perfusion imaging dataset. The patches may then be propagated through a trained CNN to estimate at least one perfusion parameter for the selected voxel, as indicated by process blocks 208 Example perfusion parameters include blood volume (“BV”), blood flow (“BF”), mean transit time (MTT), time-to-peak (TTP), time-to-maximum (Tmax), maximum signal reduction (MSR), first moment (FM), Ktrans and others. As indicated in FIG. 3, process blocks 204 through 208 may be repeated a number of times, each time selecting a different voxel. In this manner, a plurality of perfusion parameters can be estimated. These can then be used to generate one or more perfusion parameter maps.”);
- a step of creating a pharmacokinetic parameter on the basis of said second arterial input function and of said experimental signal (Arnold, Para 15; "The instructions further include generating a report indicative of the at least one perfusion parameter estimated. The system further includes an output for providing the report.") (Arnold, Para 59; “The perfusion patch may be paired with an AIF patch corresponding to the selected voxel at process block 206, where the AIF patch is generated using the perfusion imaging dataset. The patches may then be propagated through a trained CNN to estimate at least one perfusion parameter for the selected voxel, as indicated by process blocks 208 Example perfusion parameters include blood volume (“BV”), blood flow (“BF”), mean transit time (MTT), time-to-peak (TTP), time-to-maximum (Tmax), maximum signal reduction (MSR), first moment (FM), Ktrans and others. As indicated in FIG. 3, process blocks 204 through 208 may be repeated a number of times, each time selecting a different voxel. In this manner, a plurality of perfusion parameters can be estimated. These can then be used to generate one or more perfusion parameter maps.”);
wherein the step of generating a second arterial input function consists of the implementation of basic operations by said processing unit, the latter having been trained beforehand according to a process of learning the disruptive effect of the acquisition of a perfusion sequence on arterial signals and the redundancy of the information in relation to such a second arterial input function shared by a set of at least two tissue signals (Arnold, Para 16; “The method also includes training the deep CNN using training data to generate a plurality of feature filters, and for each selected voxel in a perfusion imaging dataset, generating a perfusion patch and an arterial input function (AIF) patch. The method further includes applying the plurality of feature filters to the perfusion patch and AIF patch to estimate at least one perfusion parameter for each selected voxel.”) (Arnold, Para 73-74; “The utility of the bi-CNN was also demonstrated by comparing the salvageable tissue binary masks generated from the bi-CNN and the ground truth perfusion maps. Published CBF and Tmax thresholds were used to define the salvageable tissue binary masks. The similarity between these masks (the ground truth mask, A, and the estimated mask, B) was calculated using the Dice coefficient. A value of 0 indicates no overlap, and a value of 1 indicates perfect similarity (i.e., B=A). A good overlap between masks is generally considered to have occurred when the Dice coefficient is larger than 0.7.”) (Arnold, Para 77; “The average Dice coefficients for the CBF and Tmax masks were 0.830±0.109 and 0.811±0.071 respectively, showing good overlap between the ground truth masks and the estimated masks. These results show that the bi-CNN, in accordance with the present disclosure, can generate useful masks for salvageable tissue approximation.”) (Arnold, Para 59; “The perfusion patch may be paired with an AIF patch corresponding to the selected voxel at process block 206, where the AIF patch is generated using the perfusion imaging dataset. The patches may then be propagated through a trained CNN to estimate at least one perfusion parameter for the selected voxel, as indicated by process blocks 208 Example perfusion parameters include blood volume (“BV”), blood flow (“BF”), mean transit time (MTT), time-to-peak (TTP), time-to-maximum (Tmax), maximum signal reduction (MSR), first moment (FM), Ktrans and others. As indicated in FIG. 3, process blocks 204 through 208 may be repeated a number of times, each time selecting a different voxel. In this manner, a plurality of perfusion parameters can be estimated. These can then be used to generate one or more perfusion parameter maps.”).
Regarding claim 2, Arnold discloses all of the limitations of claim 1 as discussed above.
Arnold further discloses including a step of correction by scaling said second arterial input function generated, before the implementation of the step of generating a pharmacokinetic parameter on the basis of said second arterial input function thus corrected and of said experimental signal (Arnold, Para 42; “Typically, tissue perfusion is modeled by the Indicator-Dilution theory, where the measured tissue concentration time curve (CTC) of a voxel is directly proportional to the convolution of the arterial input function (AIF) and the residue function (R), as scaled by cerebral blood flow (CBF). This model follows the principle of the conservation of mass, meaning that the amount of contrast entering the voxel is equal to the sum of the contrast leaving the voxel and the contrast within the voxel.”).
Regarding claim 3, Arnold discloses all of the limitations of claim 1 as discussed above.
Arnold further discloses an output human-machine interface, said method including a step of creating a graphic representation of said second arterial input function and of outputting said graphic representation by means of said output human-machine interface (Arnold, Para 39; “The processor 104 may also be configured to generate a report, in any form, and provide it via output 108. In some aspects, the report may include various raw or processed maps or images, or color-coded maps or images. For example, the report may include anatomical images, perfusion parameter maps including CBF, CBV, MTT, TPP, Tmax, Ktrans and other perfusion parameter maps. In some aspects, the report may indicate specific regions or tissues of interest, as well as other information. The report may further indicate a condition of the subject or a risk of the subject to developing an acute or chronic condition, such as a risk of infarction.”).
Regarding claim 4, Arnold discloses all of the limitations of claim 1 as discussed above.
Arnold further discloses wherein the learning process consists of deep learning based on minimization of the average value of the quadratic errors between real samples of arterial input functions which have made it possible to generate tissue signals and an estimation of these same samples performed by said learning process (Arnold, Para 54; “The training optimization of the network may then be configured to obtain network weights, Θ, that minimize the mean squared loss between the true value, V, and the estimated value, {circumflex over (V)}(Θ), across the samples with size n:”)
Regarding claim 6, Arnold discloses all of the limitations of claim 1 as discussed above.
Arnold further discloses computer-readable storage medium including one or more program instructions that can be executed by the processing unit of a computer, said program instructions being capable of being loaded into anon-volatile memory of said computer and the execution of which by said processing unit causes the implementation of a method according to claim 1 (Arnold, Para 32; “In addition to being configured to carry out various steps for operating the system 100, the processor 104 may also be programmed to analyze perfusion imaging data, according to methods described herein. Specifically, the processor 104 may be configured to execute instructions, stored in a non-transitory computer readable-media 116, for example. Although the non-transitory computer readable-media 116 is shown in FIG. 2 as included in the memory 106, it may be appreciated that instructions executable by the processor 104 may be additionally or alternatively stored in another data storage location having non-transitory computer readable-media accessible by the processor 104.”).
Regarding claim 8, Arnold discloses all of the limitations of claim 6 as discussed above.
Arnold further discloses a medical imaging analysis system including a processing unit arranged to communicate with the outside world and receive a set of samples of a temporal experimental signal, resulting from a perfusion acquisition sequence by a medical imaging device and resulting from the passage of a tracer within an elementary volume of an organ, said processing unit having been trained beforehand according to a process of learning the disruptive effect of the acquisition of a perfusion sequence on arterial signals and the redundancy of the information in relation to an arterial input function shared by a set of at least two tissue signals and including a computer-readable storage medium according to claim 6 (Arnold, Para 32; “In addition to being configured to carry out various steps for operating the system 100, the processor 104 may also be programmed to analyze perfusion imaging data, according to methods described herein. Specifically, the processor 104 may be configured to execute instructions, stored in a non-transitory computer readable-media 116, for example. Although the non-transitory computer readable-media 116 is shown in FIG. 2 as included in the memory 106, it may be appreciated that instructions executable by the processor 104 may be additionally or alternatively stored in another data storage location having non-transitory computer readable-media accessible by the processor 104.”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Arnold and Bone et al. (US20240062061, hereafter Bone).
Regarding claim 5, Arnold discloses all of the limitations of claim 1 as discussed above.
Arnold does not clearly and explicitly disclose wherein the learning process is carried out via an Adam optimizer.
In an analogous perfusion imaging field using machine learning field of endeavor Bone discloses wherein a learning process is carried out via an Adam optimizer (Bone, Para 190; “In a preferred embodiment, the CNN is trained with the Adam optimizer”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Arnold wherein the learning process is carried out via an Adam optimizer as taught by Bone in order to improve efficiency without as much tuning and memory.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to John Li whose telephone number is (313)446-4916. The examiner can normally be reached Monday to Thursday; 5:30 AM to 3:30 PM Eastern.
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/JOHN D LI/Primary Examiner, Art Unit 3798