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 .
Election/Restrictions
Applicant's election with traverse of Invention I, claims 1 and 3-10 in the reply filed on 5/26/26 is acknowledged. The traversal is on the ground(s) that “there would not be a serious search and/or examination burden on the Office.” This is not found persuasive.
Applicant argues “that the separate classes of search identified by the Office substantially overlap and any cited prior art for Group I claims will likely overlap with cited prior art for Group II claims.”
Applicant supports this claim by asserting that CPC G06V 10/62 and CPC G06F18/23 overlap in subject matter. First, that CPC G06V 10/62 and CPC G06F18/23 have some overlap in subject matter is not sufficient to demonstrate a lack of serious search and/or examination burden. That CPC G06V 10/62 and CPC G06F18/23 are distinct classifications is evidence that a first invention directed to one CPC classification has acquired separate status in the art from a second invention directed to a different CPC classification. Therefore, even if there is some overlap in subject matter between the separate classifications, they are recognized as separate subjects requiring a separate field of search. See MPEP 808.02 “Separate classification thereof: This shows that each invention has attained recognition in the art as a separate subject for inventive effort, and also a separate field of search.”
Second, Applicant has not addressed the differences in the definitions and scope of each of the classifications. Pertinent to the differences in the features between Invention I and Invention II is that the definition of CPC G06V 10/62 encompasses “statistical models such as maximum a-posteriori estimation (MAP)” (e.g., “A Posteriori Maximum Estimator” being recited in claim 1), while the definition of CPC G06F 18/23 does not encompass MAP. Further, the definition of CPC G06F18/23 encompasses “[d]ifferent clustering algorithms include: a) clustering based on statistical measures (which mainly employ numerical data) which adopt a cost function J related to possible groupings which is subject to global or local optimization criterion, and returning a clustering that optimizes J” (e.g., “a cost function” being recited in claim 2), while the definition of CPC G06V 10/62 does not encompass a cost function. Thus, Invention I is appropriately classified in CPC G06V 10/62 and not CPC G06F18/23, and Invention II is appropriately classified in CPC G06F18/23 and not CPC G06V 10/62 because of the differing features of the Inventions and the definitions of the separate CPC classifications.
Applicant further argues that Invention II is more appropriately classified in CPC G06V 10/62. As discussed above, the definition of CPC G06V 10/62 does not encompass the cost function of Invention II, while the definition of CPC G06F18/23 encompasses the cost function. Thus, Invention II is appropriately classified in CPC G06F18/23 and not CPC G06V 10/62 because of the differing features of the Inventions and the definitions of the separate CPC classifications.
Furthermore, Applicant’s arguments fail to address that a complete search for Invention I would require a unique text search separate from that of Invention II. This includes not only within the patent literature, but also, within the non-patent literature for which algorithms and modeling employing the “A Posteriori Maximum Estimator” of Invention I would require a unique text search in a field separate from the algorithms and modeling employing the “cost function” of Invention II. A search including the phrase “maximum a posteriori estimator” in the patent and non-patent literature will not likely result in finding art pertinent to a “cost function” as these involve distinct models and algorithms that are mutually exclusive (for example, Applicant’s specification separates the “first exemplary implementation” employing a cost function from the “second exemplary implementation” employing an a posteriori maximum estimator). It is necessary to search for the features of Invention I separate from the features of Invention II. Ergo, that a text search for the features of Invention I would not likely result in art pertinent for the features of Invention II, establishes that Invention I and Invention II have a different field of search. See MPEP 808.02 “A different field of search: Where it is necessary to search for one of the inventions in a manner that is not likely to result in finding art pertinent to the other invention(s) (e.g., searching different classes/subclasses or electronic resources, or employing different search queries), a different field of search is shown, even though the two are classified together. The indicated different field of search must in fact be pertinent to the type of subject matter covered by the claims. Patents need not be cited to show different fields of search.”
Therefore, Applicant’s arguments that there is not a serious search burden is not persuasive.
Applicant further asserts that there would not be any serious examination burden. The implementation of the “A Posteriori Maximum Estimator” raises different non-prior art issues under 35 U.S.C. 101 from that of the implementation of the “cost function.” Consideration of issues under 35 U.S.C. 101, for example, consideration of the recitation of abstract ideas involving mathematical concepts and mental processes and the feasibility of implantation within the human mind with the aid of pen and paper is necessary for the distinct features of Invention I and distinct features of Invention II. Therefore, there is serious examination burden. See MPEP 808.02 “To demonstrate serious examination burden separate from a serious search burden, the examiner must show by appropriate explanation that the inventions are likely to raise serious examination issues, such as non-prior art issues under 35 U.S.C. 101, pre-AIA 35 U.S.C. 112, first paragraph and/or 35 U.S.C. 112(a). In this situation, a serious examination burden may exist where issues relevant to one invention are not relevant to the other invention.”
The requirement is still deemed proper and is therefore made FINAL.
Claims 1-18 are pending in the present application. Claims 2 and 11-18 are withdrawn from consideration. Claims 1 and 3-10 are under consideration in this Office Action.
Claim Objections
Claims 1 and 7 are objected to because of the following informalities:
Claim 1, line 18 recites “applying the A Posteriori Maximum Estimator.” This should read “applying the A Posteriori Maximum Estimator;.”
Claim 7, line 2 recites “cooperating with memory.” This should read “cooperating with a memory.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-10 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 6, lines 1-5 recites “when the functional imaging analysis system comprises an interface for a user of said system, said user interface cooperating with the processor, further comprising a subsequent step for triggering the output of the calculated physiological signal… and generating an image in the form of a functional activity map.” The phrase "when" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 6, lines 1-2 recites “when the functional imaging analysis system comprises an interface for a user of said system, said user interface cooperating with the processor.” There is insufficient antecedent basis for this limitation in the claim. The recited “said user interface” appears to be intended to refer to “an interface for a user of said system;” however, the inconsistent language renders it unclear if the “user interface” is the same feature as “an interface.”
Claim 7, line 4 recites “the communication interface is arranged to receive.” There is insufficient antecedent basis for this limitation in the claim. The recited “the communication interface” appears to be intended to refer to “an interface for communicating externally of the imaging analysis system” in claim 7, lines 1-2; however, the inconsistent language renders it unclear if “the communication interface” is the same feature as “an interface.”
Claim 8, lines 1-2 recites “the communication interface delivers the calculated physiological signal.” There is insufficient antecedent basis for this limitation in the claim. The recited “the communication interface” appears to be intended to refer to “an interface for communicating externally of the imaging analysis system” in claim 7, lines 1-2; however, the inconsistent language renders it unclear if “the communication interface” is the same feature as “an interface.”
Claims 9-10 are rejected as depending from and incorporating all the limitations thereof of claim 7.
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 and 3-19 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 1-20 recites:
A method for reconstructing a physiological signal of an artery/tissue/vein system of an organ in a surface space, based upon an experimental datum of a region of interest comprising an elementary voxel of said organ and a surface mesh describing said surface space…, and comprises:
jointly assigning:
a direct probability distribution as a likelihood of the experimental datum in said surface space based on parameters involved in reconstruction of the physiological signal of the artery/tissue/vein dynamic system for the voxel in question,
an a priori spatial probability distribution of said physiological signal by introducing a priori information relative to a characteristic of the experimental datum and/or a priori information relative to a property of the artery/tissue/vein dynamic system, and
an a priori temporal probability distribution of said physiological signal by introducing a priori information relative to an impulse response of said artery/tissue/vein dynamic system;
assigning an a posteriori marginal distribution for the physiological signal by multiplying the jointly assigned direct probability distribution, a priori spatial probability distribution and a priori temporal probability distribution;
evaluating said a posteriori marginal distribution by maximizing the a posteriori marginal distribution according to the physiological signal by applying the A Posteriori Maximum Estimator
…
calculating the value of the physiological signal via the configured processor.
Under its broadest reasonable interpretation consistent with the specification, the plain and ordinary meaning of these limitations requires mathematical calculations. The recited method steps explicitly recite “reconstructing a physiological signal… based on experimental datum,” “assigning at least one a posteriori marginal distribution for the physiological signal, based upon the jointly assigned direct probability distribution, a priori spatial probability distribution and a priori temporal probability distribution,” and “calculating the value of the physiological signal.” As recited, the marginal distribution is composed of three other probability distributions: the direct probability distribution, the a priori spatial probability distribution, and the a priori temporal probability distribution. These distributions are explicit recitations of mathematical concepts. The recited method performs this evaluation as a solution to an inverse problem, i.e., the mathematical concept of identifying whether a particular location, i.e., a voxel or pixel, has an active signal or is inactive. In effect, the recited method step seeks to capture the mathematical concept of identifying a whether it is likely/probable that a given location has a signal by evaluating/comparing: 1) the direct probability distribution, i.e., a known routine, ordinary, and conventional probability distribution such as a Gaussian, Poisson, Bernoulli, Binomial, Discrete Uniform, Chi-Squared (e.g. Gamma), F, or Discrete or Continuous Uniform (e.g. Beta) distribution describing random error/noise and signal measurement delays, see e.g., Helwig (“Common Probability Distributions” 2020) and Shaddick & Zidek (“Lecture 1: Introduction to Bayesian Monte Carlo methods in WINBUGS” 2015); 2) the spatial probability distribution, i.e., the probability of a location having a signal relative to its location, and 3) the temporal probability distribution, i.e., the probability of a location having a signal relative to timing of measurement and activity. Further, the claim recites multiplication of the distributions and a maximization estimation by the a posteriori maximum estimator. The applicant has attempted to monopolize the mathematical concept of Bayesian inversion and the maximum a posteriori estimate (MAP) as applied to the “artery/tissue/vein dynamic system” Claim 1, line 11. See e.g., Kolehmainen (“Bayesian Inversion; Examples and Computational Aspects” 2007). See also Penny’s discussion of the fundamental characteristics of solving the inverse problem of mapping experimental data to activation at particular locations and times in the brain, “[g]iven an impulse of neuronal activity, the BOLD signal we measure [being] dispersed both in space and time according to a hemodynamic response function (HRF)” in Penny’s Introduction. These limitations hence recite “mathematical calculation[s]” 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 observation of the input experimental datum and performing an evaluation thereof, i.e., 1) the joint assignment of a direct, an a priori spatial, and an a priori temporal probability distribution; 2) the assignment of a posteriori marginal distribution for the probability distributions; 3) evaluating the a posteriori marginal distribution using the MAP; and 4) calculating/determining the activation/presence of the physiological signal.
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. It is noted that the claim recites evaluating a signal at a single “physiological signal” at a single location “an elementary voxel.” Further, the claim recites no specific physiological signal, therefore the claim encompasses any physiological signal of the artery/tissue/vein system. For example, this includes an activity signal for BOLD/fMRI imaging which is simply a Boolean true or false (1 or 0) indicating whether activity is present in a region of the brain based on blood oxygenation level increased due to increased blood flow in response to a task undertaken by the patient (ex., visual stimulus such as a moving target, auditory stimulus such as music, or tactile stimulus such as stacking boxes). Thus, it would be feasible for a PHOSITA to with the aid of pen and paper to compare a priori knowledge of the probability of activation in regions of the brain such as a person determining that there is high or low probability of activation (posteriori marginal distribution) of a voxel in the visual cortex (spatial probability distribution) following an onset delay after a task (temporal probability distribution) within a certain margin of error due to a random noise/error (direct probability distribution). The probability distributions are informed by the person’s a priori knowledge of brain anatomy and physiology that after a visual task there will likely be activation within the visual cortex after a known onset delay within a margin of error and level of confidence. This a priori knowledge directly informs the PHOSITA’s a posteriori prediction that there will be or not be activation of a voxel within a particular region of the brain at a particular measurement time such as the visual cortex following a visual task. Further, the MAP is an identification of the mode of the distribution, i.e., the most common estimate. A PHOSITA with the aid of pen and paper can again use their a priori knowledge of brain anatomy and physiology to determine the most likely response which corresponds to the mode, i.e., whether there will most likely be activation of a given voxel.
As there are no bright lines between the types of judicial exceptions, and many of the concepts identified by the courts as exceptions can fall under several exceptions, MPEP 2106.04, subsection I instructs examiners to “identify . . . the claimed concept (the specific claim limitation(s) that the examiner believes may recite an exception) [that] aligns with at least one judicial exception.” While the limitations of claim 1, lines 4-15 can be categorized under several exceptions (a mathematical concept-type abstract idea and a mental process-type abstract idea), it is adequate for an examiner to identify the limitation as falling under at least one judicial exception and to base further analysis on that identification. The remainder of this discussion is premised on the recited exception as an abstract idea. See MPEP 2106.04, subsection II.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 to the claim at lines 102 recites “[a] method for reconstructing a physiological signal of an artery/tissue/vein system of an organ in surface space.” This preamble merely provides that the physiological signal is generally linked to a particular technological environment, the artery/tissue/vein system of an organ. 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 description of the experimental data in lines 2-3 recites “based upon an experimental datum of a region of interest comprising an elementary voxel of said organ and a surface mesh describing said surface space.” This merely provides that the experimental data is generally linked to a particular technological environment, experimental data from a ROI of the organ. This 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, this limitation 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. Additionally, the claim does not recite the means or manner by which such experimental datum is acquired. That the method is based upon the experimental datum at most constitutes insignificant extra-solution activity that amounts to mere data gathering incidental to the abstract idea recited in lines 1-20. As all uses of the recited judicial exceptions require such experimental datum this limitation does not impose any meaningful limits on the claim. This limitation amount to necessary data gathering. See MPEP 2106.05.
The generic computer element recited in lines 3-4, “said method being implemented by a processor of a functional imaging analysis system,” and lines 19-20 “configuring the processor in accordance with said a posteriori marginal distribution; and calculating the value of the physiological signal via the configured processor” do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
As disclosed in applicant’s originally filed specification:
To evaluate such an a posteriori marginal distribution, it is necessary to configure, manually or automatically, the processing unit 4 of a functional imaging analysis system, like that previously described in connection with FIGS. 1 and 2. P.12, lines 11-14
and
A method 200 according to the invention may thus comprise configuration steps 240, 250, 260 carried out prior to the assignment 270, manually or automatically, among which the following are necessary and sufficient:
assigning 240 the direct probability distribution
….
assigning 250 an a priori spatial probability distribution
….
assigning 260 an a priori temporal probability distribution… P. 12, line 21 – P.13, line 3.
Applicant’s specification demonstrates not only that configuring the a posteriori marginal distribution onto the processor can be performed manually, but also that the step of configuring of the processor comprises merely applying the joint model, the a posteriori marginal distribution, comprising the three probability distributions, using a processor. The step of evaluating an algorithm/model (here the joint probability distribution/model) on a generic processor is the use of a generic computer component performing a generic computer function of calculating a value from the algorithm/model and/or is insignificant extra-solution activity of merely applying the mathematical concepts and/or mental process to a generic computer component. The processor is recited at a high level of generality. The processor is used to perform the abstract idea 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 MPEP 2106.05 A.:
It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2B. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception is not in itself an inventive concept and does not guarantee eligibility: The fact that a computer "necessarily exist[s] in the physical, rather than purely conceptual, realm," is beside the point. There is no dispute that a computer is a tangible system (in § 101 terms, a "machine"), or that many computer-implemented claims are formally addressed to patent-eligible subject matter. But if that were the end of the § 101 inquiry, an applicant could claim any principle of the physical or social sciences by reciting a computer system configured to implement the relevant concept. Such a result would make the determination of patent eligibility "depend simply on the draftsman’s art," Flook, supra, at 593, 98 S. Ct. 2522, 57 L. Ed. 2d 451, thereby eviscerating the rule that "‘[l]aws of nature, natural phenomena, and abstract ideas are not patentable,’" Myriad, 133 S. Ct. 1289, 186 L. Ed. 2d 124, 133). Alice Corp., 573 U.S. at 224, 110 USPQ2d at 1983-84 (alterations in original). See also Genetic Technologies Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1547 (Fed. Cir. 2016) (steps of DNA amplification and analysis "do not, individually or in combination, provide sufficient inventive concept to render claim 1 patent eligible" merely because they are physical steps).
Furthermore, the recitation of “calculating the value of the physiological signal” does not add a meaningful limitation as it is merely a nominal or token extra-solution component of the claim as a mere output of the method step and further reconstruction of a signal is directed to yet another judicial exception encompassing a mathematical concept and mental process (with the aid of pen and paper).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the preamble and description of the experimental data are nothing more than an attempt to generally link the product of nature to a particular technological environment and the additional limitation of implementing the method step on a processor of is a well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
While the disclosure states that invention achieves “high accuracy… and robustness thereof to noise” (Abstract) 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 applying Bayesian modeling, inversion, and the MAP to predict whether activation of a given voxel in the brain is activated using a generic processor, encompassing mathematical concepts that can be performed as part of a mental process (with the aid of pen and paper) 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.
The experimental datum 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, it at most amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
The recitation of a processor to perform the method steps of claim 1 including performing the final calculation for output 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 the dependent claims:
Claim 3 recites insignificant extra solution activity of acquiring experimental data from functional imaging. This constitutes mere necessary data gathering. Further, it at most amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
Claim 4 recites insignificant extra solution activity of outputting a reconstructed physiological signal on a single voxel of an image for display on a display indicating whether the judicial exception identified activity in the voxel and that the display communicates with the generic computer element, the processor. This constitutes mere data output. Further, it at most amounts to outputting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
Claim 5 recites a preprocessing step to correct and/or recalibrate the experimental data or surface mesh. A preprocessing step of correcting and/or recalibrating data is directed to the judicial exceptions of a mathematical concept and a mental process (with the aid of pen and paper) or, alternatively, merely recites insignificant extra solution activity of acquiring experimental data constituting mere necessary data gathering.
Claim 6 recites insignificant extra solution activity of outputting a reconstructed physiological signal on a single voxel of an image for display indicating whether the judicial exception identified activity in the voxel and that the display communicates with the generic computer element, the processor. Further, it at most amounts to outputting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
Claim 7 recites a generic communication interface for performing the insignificant extra solution activity of acquiring experimental data constituting mere data gathering. Further, it at most amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Claim 7 further recites a generic processor and memory which simply implements the judicial exception on generic computer elements. The processor is used to perform the abstract idea such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Claim 8 recites insignificant extra solution activity of outputting a reconstructed physiological signal for display. Further, it at most amounts to outputting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
Claim 9 recites that the insignificant extra solution activity of outputting a reconstructed physiological signal for display is performed on a generic computer element. The processor is used to perform the abstract idea such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Claim 10 merely recites the method is implemented in a generic computer program implemented on generic computer elements, a processor with a memory. The processor is used to perform the abstract idea such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
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.
Claims 1, 3, and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Penny (“Bayesian fMRI time series analysis with spatial priors” 2005), hereinafter “Penny,” in further view of Kiebel and Friston (“Anatomically Informed Basis Functions in Multisubject Studies” 2002), hereinafter “Kiebel,” in further view of Ciuciu et al. (“Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment” 2003), hereinafter “Ciuciu.”
Regarding claim 1, Penny discloses a method for reconstructing a physiological signal of an artery/tissue/vein system of an organ (functional magnetic resonance imaging (fMRI) using blood oxygen level-dependent (BOLD) contrast to determine regionally specific activation of the human brain from oxygenation changes in venules lying close to the site of neuronal activity, Introduction), based upon an experimental datum of a region of interest comprising an elementary voxel of said organ (voxel-wise estimation, Abstract; voxel temporal and spatial characteristics, Introduction; voxel-wise model for noise (i.e, direct), spatial, and temporal distributions, and posterior distribution, Theory), said method being implemented by a processor of a functional imaging analysis system (joint probability distribution GLM-AR, Variational Bayes (VB) framework/model applied to compute posteriors on modern computers, Approximate posteriors; (2T VISION system (Siemens, Erlangen, Germany), Face-repetition data; Note that modern computers and clinical fMRI imaging systems have a processor coupled to memory; See also Figures 1 and 4 setting out the computation of the posteriori marginal distribution using the GLM-AR-VB model; See also joint posterior distribution is in the voxel space, Theory; See also the words “compute,” “algorithm,” “automatic,” “process,” and variants thereof throughout Penny’s disclosure in reference to implementing the various calculations and equations of Penny’s method), and comprises:
jointly assigning (joint probability of parameters and data defines the posterior distribution comprising the joint assignment of the noise (direct), a priori spatial, and a priori autoregressive (AR/temporal) probability distributions, Theory, Figs. 1 and 4):
a direct probability distribution as a likelihood of the experimental datum based on parameters involved in reconstruction of the physiological signal of the artery/tissue/vein dynamic system for the voxel in question (noise probability distribution due to observation noise of the experimental datum based on parameters informing the reconstruction such as those of Equation (6), Theory: Noise precisions, Figs. 1 and 4),
an a priori spatial probability distribution of said physiological signal by introducing a priori a priori information relative to a characteristic of the experimental datum and/or a priori information relative to a property of the artery/tissue/vein dynamic system (regression coefficients and spatial precisions priors relative to characteristics and/or properties of the blood oxygen dynamics of the brain such as those of Equations (4) and (5), Theory: Regression coefficients, Theory: Spatial precisions, Theory: Spatial kernels, Figs. 1 and 4; See also spatial priors over the regression coefficients estimated from the experimental datum, Introduction; See also impulse of neuronal activity spatial and temporal aspects, Introduction), and
an a priori temporal probability distribution of said physiological signal by introducing a priori information relative to an impulse response of said artery/tissue/vein dynamic system (priors of the autoregressive temporal parameters relative to the characteristics and/or properties of the blood oxygen dynamics of the brain such as those of Equation (7), Theory: AR coefficients, Figs. 1 and 4; See also temporal prior from temporal autocorrelation via an autoregressive process, Introduction; See also impulse of neuronal activity spatial and temporal aspects, Introduction);
assigning an a posteriori marginal distribution of the physiological signal (joint probability distribution GLM-AR, Variational Bayes (VB) framework/model applied to compute posteriors on modern computers, Approximate posteriors; See also Figures 1 and 4 setting out the computation of the posteriori marginal distribution using the GLM-AR-VB model; See also joint posterior distribution is in the voxel space, Theory) by multiplying the jointly assigned direct probability distribution, a priori spatial probability distribution and a priori temporal probability distribution (joint probability of parameters and data defines the posterior distribution comprising the joint assignment of the noise (direct), a priori spatial, and a priori autoregressive (AR/temporal) probability distributions which are multiplied together, Theory, Figs. 1 and 4);
evaluating said a posteriori marginal distribution by maximizing the a posteriori marginal distribution according to the physiological signal (evaluate the a posteriori marginal distribution, the approximate posterior, by maximizing the a posteriori marginal distribution according to the true posterior from the physiological signal, the model evidence, Appendix A-C);
configuring the processor in accordance with said at least one a posteriori marginal distribution (joint probability distribution GLM-AR, Variational Bayes (VB) framework/model applied to compute posteriors on modern computers, Approximate posteriors; (2T VISION system (Siemens, Erlangen, Germany), Face-repetition data; Note that modern computers and clinical fMRI imaging systems have a processor coupled to memory; See also Figures 1 and 4 setting out the computation of the posteriori marginal distribution using the GLM-AR-VB model; See also joint posterior distribution is in the voxel space, Theory; see also the words “compute,” “algorithm,” “automatic,” “process,” and variants thereof throughout Penny’s disclosure in reference to implementing the various calculations and equations of Penny’s method); and
calculating the value of the physiological signal via the configured processor (“We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models (GLMs). Importantly, we use a spatial prior on regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. Further, using a computationally efficient Variational Bayes framework, we are able to let the data determine the optimal amount of smoothing. We assume an arbitrary order Auto-Regressive (AR) model for the errors. Our model generalizes earlier work on voxel-wise estimation of GLM-AR models and inference in GLMs using Posterior Probability Maps (PPMs). Results are shown on simulated data and on data from an event-related fMRI experiment.” Abstract; “Functional magnetic resonance imaging (fMRI) using blood oxygen level-dependent (BOLD) contrast is an established method for making inferences about regionally specific activations in the human brain (Frackowiak et al., 2004). From measurements of changes in blood oxygenation, one uses various statistical models, such as the general linear model (GLM) (Friston et al., 1995), to make inferences about task-specific changes in underlying neuronal activity… In this paper, we characterize the spatial characteristics of the HRF using Bayesian inference and spatial priors over the regression coefficients. The precision with which regression coefficients, and therefore regionally specific effects, are estimated then comprises two contributions (i) the data at a given voxel and (ii) the regression coefficients at neighboring voxels. If data precision is low (e.g., due to high noise variance at that voxel), then neighboring voxels will contribute more to the estimate of the effect. This spatial regularization falls naturally out of the Bayesian framework. Moreover, we are able to use spatial regularization coefficients that can be estimated from the data. The spatial characteristics of the hemodynamic response are therefore handled in a natural and automatic way…. In the GLM framework, temporal autocorrelation can be taken into account by modeling the errors as an Autoregressive (AR) process, as shown, for example, in our previous work (Penny et al., 2003). This is the approach taken in this paper… This motivated the more recent work in which Woolrich et al. (2004a) specified a CAR/MRF model to regularize estimation of AR coefficients using the Variational Bayes (VB) framework. This resulted in an algorithm that could process whole volumes of fMRI data in the order of minutes. This paper also makes use of the VB approach and may be regarded as an extension of Penny et al. (2003) to include spatial priors for the regression coefficients. A key technical contribution of this paper is that we use a prior that captures dependencies across voxels but a (approximate) posterior that factorizes over voxels. This means that we can avoid the inversion of very large covariance matrices. This is made possible using the VB framework and results in an algorithm that both captures spatial dependencies and can be efficiently implemented. In Theory, we review the GLM-AR model defined in Penny et al. (2003). We also describe the priors and show how Variational Bayes is used to define approximate posteriors and how it provides a set of update equations for the sufficient statistics of these distributions. Results present synthetic data and an event-related fMRI data set.” Introduction (emphasis added); See also computed maps of the estimated contrast in Figure 10 and Face-repetition data, computed Posterior Probability Maps (PPMs) in Figure 11 and Face-repetition data, computed maps of the contrast in Figure 12 and Face-repetition data, and computed PPMs in Figure 13; See also “We have proposed a Bayesian estimation and inference procedure for fMRI time series based on the use of GLM-AR(P) models. The novel contribution of this paper has been the incorporation of a spatial prior over regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. Further, we have been able to let the data determine the spatial regularization coefficients. Our model generalizes earlier work on voxel-wise estimation of GLM-AR models that used uninformative priors (Penny et al., 2003) and inference in GLMs using Posterior Probability Maps (PPMs) based on global-shrinkage priors (Friston and Penny, 2003).” Discussion; See also joint probability distribution GLM-AR, Variational Bayes (VB) framework/model applied to compute posteriors on modern computers, Approximate posteriors; See also (2T VISION system (Siemens, Erlangen, Germany), Face-repetition data; Note that modern computers and clinical fMRI imaging systems have a processor coupled to memory; See also Figures 1 and 4 setting out the computation of the posteriori marginal distribution using the GLM-AR-VB model; See also joint posterior distribution is in the voxel space, Theory).
However, Penny does not appear to disclose an artery/tissue/vein system of an organ surface space, and an experimental datum of a region of interest comprising an elementary voxel of said organ and a surface mesh describing said surface space.
However, in the same field of endeavor of functional MRI, Kiebel teaches an artery/tissue/vein system of an organ in a surface space (“AIBF are used to specify an anatomically informed spatial model that embodies anatomical knowledge for the statistical analysis of neuroimaging data. In a previous communication, we showed how AIBF can be used to incorporate prior anatomical constraints in single subject functional magnetic resonance image (fMRI) analyses to augment their anatomical precision… By estimating the hemodynamic signal in this canonical AIBF-space and then projecting it back into the voxel-space, one effectively extracts functional activity that is smooth, within and only within, the cortical sheet while attenuating other components unrelated to the physiological process of interest. The ensuing procedure can be considered as a highly non-stationary, anisotropic anatomically informed [de]convolution or smoothing.” Abstract; “The resulting MIPs centered on the maximum t-values of the AIBG- and CS-analysis are shown in Figures 3 and 4. The t-maps were threshold at P < 0.01 (corrected) and overlaid on a structural MRI of a subject’s brain” fMRI multisubject study, Figs. 3-4), and an experimental datum of a region of interest comprising an elementary voxel of said organ and a surface mesh describing said surface space (“The key concept is that, after spatial normalization, a canonical cortical surface can be used to generate a forward model of signal sources for all subjects. By estimating the hemodynamic signal in this canonical AIBF-space and then projecting it back into the voxel-space, one effectively extracts functional activity that is smooth, within and only within, the cortical sheet while attenuating other components unrelated to the physiological process of interest.” Abstract; “This section describes the generation of an anatomically informed forward model used to implement a spatially variable convolution that conforms to the cortical surface. The description is divided into three parts. First, we will outline the method of anatomically informed basis functions. Second, we specify the model and show how its parameters are estimated. Finally, we describe how these estimated parameters are projected back into image space to give a non-stationary [de]convolution.” Theory and Basis function specification)
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 Kiebel’s known technique of projecting the anatomical, brain, surface spatial model space prior constraints into the combined voxel space for solving the inverse problem to Penny’s known process for spatially informed priors in the combined voxel space for solving the inverse problem to improve the extraction of functional activity by attenuating other components unrelated to the physiological process of interest. See e.g., Kiebel, Abstract. See also MPEP 2141 III.
However, while Penny in further view of Kiebel teaches assigning an a posteriori marginal distribution and evaluating said a posteriori marginal distribution by maximizing the a posteriori marginal distribution according to the physiological signal as detailed above, Penny in further view of Kiebel does not appear to teach evaluating said a posteriori marginal distribution by maximizing the a posteriori marginal distribution according to the physiological signal by applying the A Posteriori Maximum Estimator.
However, in the same field of endeavor of functional MRI, Ciuciu teaches assigning an a posteriori marginal distribution for the physiological signal by multiplying a jointly assigned probability distributions (assigning a marginal posterior distribution/probability density function for the hemodynamic response by multiplying a jointly assigned Gaussian probability density function and a priori temporal probability density function, P.1238, ¶7 – P.1240, ¶1; see also jointly assigning an a priori spatial prior/model, P.1249, ¶9);
evaluating said a posteriori marginal distribution by maximizing the a posteriori marginal distribution according to the physiological signal by applying the A Posteriori Maximum Estimator (evaluating said marginal posterior distribution/probability density function by maximizing the marginal posterior distribution/probability density function according to the hemodynamic response by applying the maximum a posteriori (MAP) estimate, P.1239, ¶5 – P.1240, ¶1);
configuring the processor in accordance with said a posteriori marginal distribution (automated/unsupervised process implemented in computer software on a computer configured to perform the calculation/estimate of the hemodynamic response, P.1235, ¶1-2, P.1236, ¶5-6, P.1241, ¶6, P.1248, ¶3 -P.1249, ¶1, P.1249, ¶4, Figs. 1, 3, 4-7); and
calculating the value of the physiological signal via the configured processor (automated/unsupervised process implemented in computer software on a computer performs the calculation/estimate of the hemodynamic response, P.1235, ¶1-2, P.1236, ¶5-6, P.1241, ¶6, P.1248, ¶3 -P.1249, ¶1, P.1249, ¶4, Figs. 1, 3, 4-7).
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 Cuicui’s known technique of applying a maximum a posteriori estimate to evaluate an assigned a posteriori marginal distribution for the hemodynamic signal assigned by multiplying jointly assigned probability distributions to Penny in further view of Kiebel’s known process of maximization to evaluate an assigned a posteriori marginal distribution for the hemodynamic signal assigned by multiplying jointly assigned direct, a priori spatial, and a priori temporal probability distributions to achieve the predictable result that evaluating the marginal posterior distribution using the maximum a posteriori estimate to calculate the hemodynamic response presents an efficient method to estimate the hemodynamic response from a priori models (priors or probability distributions) that allows for large fMRI time series to be processed quickly. See, e.g., P.1236, ¶5 and P.1249, ¶4. See also, Ciuciu’s contemplation of incorporating a spatial prior into the jointly assigned marginal posterior distribution to improve robustness of the estimation in P.1249, ¶9.
Regarding claim 3, Penny discloses a step for producing said experimental datum from an acquisition of functional imaging signal (functional magnetic resonance imaging (fMRI) using blood oxygen level-dependent (BOLD) contrast to determine regionally specific activation of the human brain from oxygenation changes in venules lying close to the site of neuronal activity, Introduction; “data from an event-related fMRI experiment, Abstract; “Images were acquired from a 2T VISION system (Siemens, Erlangen, Germany) which produced T2*-weighted transverse Echo-Planar Images (EPIs) with BOLD contrast. Whole brain EPIs consisting of 24 transverse slices were acquired every 2 s resulting in a total of T = 351 scans. All functional images…” Face-repetition data).
Regarding claim 5, Penny discloses a prior step for preprocessing of the experimental datum said step being arranged to correct the experimental datum (functional images are preprocessed with a rigid-body transformation, interpolation, normalization, and/or high-pass filtering, Face-repetition data).
Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Penny in further view of Kiebel in further view of Ciucui as in claim 1 above, and further in view of Leuthardt et al. (U.S. Pub. No. 20130123607), hereinafter “Leuthardt.”
Regarding claim 4, while Penny discloses an output of the calculated signal in an appropriate format (see the calculation of the physiological signal using a modern computer as detailed in the rejection of claim 1 above), Penny does not appear to disclose the functional imaging analysis system comprising a user interface for the calculated physiological signal for a user of said system, said user interface cooperating with the processor, said method comprising a subsequent step for triggering a output of the calculated physiological signal in an appropriate format.
However, in the same field of endeavor of fMRI, Leuthardt teaches the functional imaging analysis system comprising a user interface for the calculated physiological signal for a user of said system, said user interface cooperating with the processor, said method comprising a subsequent step for triggering a output of the calculated physiological signal in an appropriate format (“The map, for example, may illustrate the location within the brain of a measured brain activity. Processor 214 may be programmed to produce the map by using the various compared data points in a known algorithm to calculate a plurality of outputs, such as, for example, at least one output vector. One algorithm that may be used is represented in Equation 1 below.” [0039]; “When processor 214 has completed the correlation analysis, processor 214 then produces at least one map (not shown in FIG. 2) of the brain of the subject, such as a functional connectivity map, for each of the measurements.” [0046]; “The brain activity is categorized 314 in a plurality of networks in the brain based on the map. The map and/or an output for the categorization are displayed 316 to a user, via a presentation interface 207 (shown in FIG. 2).” [0048]).
It would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to have applied Leuthardt’s known technique for displaying/outputting activation maps to the user via an interface to Penny in further view of Kiebel in further view of Ciuciu’s known process for generating activation maps to improve the speed of reviewing the activation map image by allowing the user to view a representation of the activation map of the subject’s brain in an intuitive format on a display. See also MPEP 2141 III.
Regarding claim 6, while Penny discloses the output of the calculated physiological signal in a voxel of the region of interest (see the calculation of the physiological signal using a modern computer as detailed in the rejection of claim 1 above; voxel-wise estimation, Abstract; voxel temporal and spatial characteristics, Introduction; voxel-wise model for noise (i.e, direct), spatial, and temporal distributions, and posterior distribution, Theory) and generating an image in the form of a functional activity map (computed maps of the estimated contrast in Figure 10 and Face-repetition data, computed Posterior Probability Maps (PPMs) in Figure 11 and Face-repetition data, computed maps of the contrast in Figure 12 and Face-repetition data, and computed PPMs in Figure 13), Penny does not appear to disclose the functional imaging analysis system comprises an interface for a user of said system, said user interface cooperating with the processor, further comprising a subsequent step for triggering the output of the calculated physiological signal in one or several vertices of the mesh for each voxel of the region of interest and generating an image in the form of a functional activity map.
However, in the same field of endeavor of fMRI, Leuthardt teaches the functional imaging analysis system comprises an interface for a user of said system, said user interface cooperating with the processor, further comprising a subsequent step for triggering the output of the calculated physiological signal in one or several vertices of the mesh for each voxel of the region of interest and generating an image in the form of a functional activity map (“The map, for example, may illustrate the location within the brain of a measured brain activity. Processor 214 may be programmed to produce the map by using the various compared data points in a known algorithm to calculate a plurality of outputs, such as, for example, at least one output vector. One algorithm that may be used is represented in Equation 1 below.” [0039]; “When processor 214 has completed the correlation analysis, processor 214 then produces at least one map (not shown in FIG. 2) of the brain of the subject, such as a functional connectivity map, for each of the measurements.” [0046]; “The brain activity is categorized 314 in a plurality of networks in the brain based on the map. The map and/or an output for the categorization are displayed 316 to a user, via a presentation interface 207 (shown in FIG. 2).” [0048]).
It would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to have applied Leuthardt’s known technique for displaying/outputting activation maps to the user via an interface to Penny in further view of Kiebel in further view of Ciuciu’s known process for generating activation maps to improve the speed of reviewing the activation map image by allowing the user to view a representation of the activation map of the subject’s brain in an intuitive format on a display. See also MPEP 2141 III.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Penny in further view of Kiebel in further view of Ciuciu in further view of Leuthardt.
Regarding claim 7, Penny discloses a processing unit of a functional imaging analysis system, said unit comprising a processor cooperating with memory (2T VISION system (Siemens, Erlangen, Germany), Face-repetition data; algorithm is performed to compute/process joint GLM-AR model, Face-Repetition data; joint probability distribution posterior algorithm inversion implemented on modern computers, Theory: Approximate posteriors; modern computers and clinical fMRI imaging systems have a processor coupled to memory; See also the words “compute,” “algorithm,” “automatic,” “process,” and variants thereof throughout Penny’s disclosure in reference to implementing the various calculations and equations of Penny’s method),
acquiring experimental datum from an elementary volume of an organ (voxel-wise estimation, Abstract; voxel temporal and spatial characteristics, Introduction; voxel-wise model for noise (i.e, direct), spatial, and temporal distributions, and posterior distribution, Theory; functional magnetic resonance imaging (fMRI) using blood oxygen level-dependent (BOLD) contrast to determine regionally specific activation of the human brain from oxygenation changes in venules lying close to the site of neuronal activity, Introduction; “data from an event-related fMRI experiment, Abstract; “Images were acquired from a 2T VISION system (Siemens, Erlangen, Germany) which produced T2*-weighted transverse Echo-Planar Images (EPIs) with BOLD contrast. Whole brain EPIs consisting of 24 transverse slices were acquired every 2 s resulting in a total of T = 351 scans. All functional images…” Face-repetition data);
the memory contains instructions executable or interpretable by the processor (2T VISION system (Siemens, Erlangen, Germany), Face-repetition data; algorithm is performed to compute/process joint GLM-AR model, Face-Repetition data; joint probability distribution posterior algorithm inversion implemented on modern computers, Theory: Approximate posteriors; modern computers and clinical fMRI imaging systems have a processor coupled to memory for storing and executing algorithms such as the joint GLM-AR model of Penny; See also the words “compute,” “algorithm,” “automatic,” “process,” and variants thereof throughout Penny’s disclosure in reference to implementing the various calculations and equations of Penny’s method), whereof the interpretation or execution of said instructions by said processor causes the implementation of a method according to claim 1 (see rejection of claim 1 above).
However, Penny does not appear to explicitly disclose an interface for communicating externally of the imaging analysis system, and the communication interface is arranged to receive, from an external source experimental datum.
However, in the same field of endeavor of fMRI, Leuthardt teaches an interface for communicating externally of the imaging analysis system and a processor cooperating with a memory (“Computing device 104 also includes a processor 214 and a memory device 218. Processor 214 is coupled to user interface 204, presentation interface 207, and to memory device 218 via a system bus 220. In the exemplary embodiment, processor 214 communicates with the user, such as by prompting the user via presentation interface 207 and/or by receiving user inputs via user interface 204.” [0036]-[0038]; See also [0042]-[0043], [0106])
the communication interface is arranged to receive, from an external source, experimental datum from an elementary volume of an organ (“In the exemplary embodiment, processor 214 is programmed to select a plurality of measurements that are received from sensing system 102 of brain activity that is representative of at least one parameter of the brain of the subject during a resting state. The plurality of measurements may include, for example, a plurality of voxels of at least one image of the subject's brain, wherein the image may be generated by processor 214 within computing device 104. The image may also be generated by an imaging device (not shown) that may be coupled to computing device 104 and sensing system 102, wherein the imaging device may generate the image based on the data received from sensing system 102 and then the imaging device may transmit the image to computing device 104 for storage within memory device 218.” [0038]; See also [0042]-[0043]),
the memory contains instructions executable or interpretable by the processor, whereof the interpretation or execution of said instructions by said processor causes the implementation of a method (“Computing device 104 also includes a processor 214 and a memory device 218. Processor 214 is coupled to user interface 204, presentation interface 207, and to memory device 218 via a system bus 220. In the exemplary embodiment, processor 214 communicates with the user, such as by prompting the user via presentation interface 207 and/or by receiving user inputs via user interface 204.” [0036]-[0038]; See also [0042]-[0043], [0106]).
It would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to have applied Leuthardt’s known technique for a processor and memory interface receiving fMRI data to Penny in further view of Kiebel in further view of Ciuciu’s known process for processing fMRI data to improve the speed of access to experimental data by allowing for quick storage and retrieval of acquired fMRI data for processing in the processor. See also MPEP 2141 III. See also MPEP 2144.04 III. “broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art.”
Regarding claim 8, while Penny discloses the calculated physiological signal in an appropriate format (see the calculation of the physiological signal using a modern computer as detailed in the rejection of claim 1 above), Penny does not appear to disclose the communication interface delivers the calculated physiological signal in an appropriate format to an interface suitable for retrieving it for a user.
However, in the same field of endeavor of fMRI, Leuthardt teaches the communication interface delivers the calculated physiological signal in an appropriate format to an interface suitable for retrieving it for a user (“The map, for example, may illustrate the location within the brain of a measured brain activity. Processor 214 may be programmed to produce the map by using the various compared data points in a known algorithm to calculate a plurality of outputs, such as, for example, at least one output vector. One algorithm that may be used is represented in Equation 1 below.” [0039]; “When processor 214 has completed the correlation analysis, processor 214 then produces at least one map (not shown in FIG. 2) of the brain of the subject, such as a functional connectivity map, for each of the measurements.” [0046]; “The brain activity is categorized 314 in a plurality of networks in the brain based on the map. The map and/or an output for the categorization are displayed 316 to a user, via a presentation interface 207 (shown in FIG. 2).” [0048]).
It would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to have applied Leuthardt’s known technique for displaying/outputting activation maps to the user via an interface to Penny in further view of Kiebel in further view of Ciuciu’s known process for generating activation maps to improve the speed of reviewing the activation map image by allowing the user to view a representation of the activation map of the subject’s brain in an intuitive format on a display. See also MPEP 2141 III.
Regarding claim 9, while Penny discloses a functional imaging analysis system comprising a processing unit according to claim 7 (see rejection of claim 7 above), and to output the calculated physiological signal (see the calculation of the physiological signal using a modern computer as detailed in the rejection of claim 1 above) using said method implemented by said processing unit (see rejection of claims 1 and 7 above), Penny does not appear to disclose an interface configured to output, for a user, the calculated physiological signal.
However, in the same field of endeavor of fMRI, Leuthardt teaches an interface configured to output, for a user, the calculated physiological signal using a method implemented by a processing unit (“The map, for example, may illustrate the location within the brain of a measured brain activity. Processor 214 may be programmed to produce the map by using the various compared data points in a known algorithm to calculate a plurality of outputs, such as, for example, at least one output vector. One algorithm that may be used is represented in Equation 1 below.” [0039]; “When processor 214 has completed the correlation analysis, processor 214 then produces at least one map (not shown in FIG. 2) of the brain of the subject, such as a functional connectivity map, for each of the measurements.” [0046]; “The brain activity is categorized 314 in a plurality of networks in the brain based on the map. The map and/or an output for the categorization are displayed 316 to a user, via a presentation interface 207 (shown in FIG. 2).” [0048]).
It would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to have applied Leuthardt’s known technique for displaying/outputting activation maps to the user via an interface to Penny in further view of Kiebel in further view of Ciuciu’s known process for generating activation maps to improve the speed of reviewing the activation map image by allowing the user to view a representation of the activation map of the subject’s brain in an intuitive format on a display. See also MPEP 2141 III.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Penny in further view of Kiebel in further view of Ciuciu in further view of Leuthardt.
Regarding claim 10, Penny discloses one or several instructions interpretable or executable by the processor of the processing unit according to claim 7, wherein the interpretation or execution of said instructions by said processor causes the implementation of said method (see rejection of claims 1 and 7 above).
However, Penny does not appear to explictly disclose a non-transitory computer-readable medium containing a computer program, said processor cooperating with said memory, and said program being loadable in said memory.
However, in the same field of endeavor of fMRI, Leuthardt teaches a non-transitory computer-readable medium containing a computer program comprising one or several instruction interpretable or executable by the processor of the processing unit, said processor cooperating with a memory, said program being loadable in said memory, wherein the interpretation or execution of said instructions by said processor causes the implementation of said method (“memory device 218 includes one or more devices that enable information, such as executable instructions and/or other data, to be stored and retrieved. Moreover, memory device 218 includes one or more computer readable media, such as, without limitation, dynamic random access memory (DRAM), static random access memory (SRAM), a solid state disk, and/or a hard disk. In the exemplary embodiment, memory device 218 stores, without limitation, application source code, application object code, configuration data, additional input events, application states, assertion statements, validation results, and/or any other type of data. Computing device 104, in the exemplary embodiment, may also include a communication interface 230 that is coupled to processor 214 via system bus 220. Moreover, communication interface 230 is communicatively coupled to sensing system 102 and to data management system 108 (shown in FIG. 1).” [0037]; see also [0106]).
It would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to have applied Leuthardt’s known technique for a processor and memory interface receiving fMRI data to Penny in further view of Kiebel in further view of Ciuciu’s known process for processing fMRI data to improve the speed of access to experimental data by allowing for quick storage and retrieval of acquired fMRI data for processing in the processor. See also MPEP 2141 III. See also MPEP 2144.04 III. “broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ciuciu et al. (“Robust estimation of the hemodynamic response function in asynchronous multitasks multisessions event-related fMRI paradigms” 2002) discloses a processor configured for reconstructing a physiological signal of an organ using the evaluation of a jointly assigned marginal posterior distribution of a Gaussian and a temporal prior probability density function using the maximum a posteriori estimate.
Marrelec et al. (“Bayesian estimation of the hemodynamic response function in functional MRI” 2002) discloses a processor configured for reconstructing a physiological signal of an organ using the evaluation of a jointly assigned marginal posterior distribution of a Gaussian and a temporal prior probability density function using the maximum a posteriori estimate.
Marrelec et al. (“Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information” 2003) discloses a processor configured for reconstructing a physiological signal of an organ using the evaluation of a jointly assigned marginal posterior distribution of a Gaussian and a temporal prior probability density function using the maximum a posteriori estimate.
Friston (“Bayesian estimation of dynamical systems: an application to fMRI” 2002) discloses a processor configured for reconstructing a physiological signal of an organ using the evaluation of a jointly assigned marginal posterior distribution of a Gaussian and a temporal prior probability density function using the maximum a posteriori estimate.
Leudthardt et al. (U.S. Patent. No. 9,480,402) discloses a processor configured to determine activation signal spatial and temporal placement from BOLD fMRI data including generating and displaying activation maps.
Gallant et al. (U.S. Patent No. 9,451,883) discloses a processor configured to determine activation signal spatial and temporal placement from BOLD fMRI data including generating and displaying activation maps.
Sitaram et al. (U.S. Pub. No. 2011/0028827) discloses a processor configured to determine activation signal spatial and temporal placement from BOLD fMRI data including generating and displaying activation maps.
Zhang et al. (“A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses” 2014) discloses reconstructing a fMRI/BOLD signal by a Bayesian inference inverse problem solving method including joint assignment of an a posteriori marginal distribution composed of discrete, a priori spatial, and a priori temporal probability distributions.
Penny et al. (“Variational Bayesian inference for fMRI” 2003) discloses reconstructing a fMRI/BOLD signal by a Bayesian inference inverse problem solving method including joint assignment of an a posteriori marginal distribution composed of discrete and a priori temporal probability distributions.
Smith et al. (“Assessing brain activity through spatial Bayesian variable selection” 2003) discloses reconstructing a fMRI/BOLD signal by a Bayesian inference inverse problem solving method including joint assignment of an a posteriori marginal distribution composed of discrete and a priori temporal probability distributions.
Marelec et al. (“Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information” 2003) discloses estimating BOLD fMRI hemodynamic response functions using an a posteriori marginal distribution by jointly assigning a spatial, temporal, and a direct probability distribution representing the likelihood of the experimental datum, and informed by anatomical a priori information (Theoretical Background).
Harrison et al. (“Large-scale probalistic functional modes from resting state fMRI” 2015) discloses a method for reconstructing a physiological signal of an artery/tissue/vein system of an organ in a surface space (Abstract, P. 217; Model, P.217-221), said method being implemented by processing means of a processing unit of a functional imaging analysis system (Computational considerations, P. 229), and comprising a step for reconstructing said physiological signal from an experimental datum of a region of interest comprising an elementary volume-called voxel-of said organ and a surface mesh describing said surface space, wherein said step comprises evaluating, according to a method for solving an inverse problem, an a posteriori marginal distribution for said physiological signal in a vertex of said mesh by (Abstract, P. 217; Definitions, P. 217-218; Model, P.217-221; Network analyses, P. 228):
assigning a direct probability distribution of the experimental datum in said surface space based on the parameters involved in the reconstruction problem of the physiological signal of the artery/tissue/vein dynamic system for the voxel in question (Definitions, P. 217-218; Model, P. 219; Noise model, P. 220; Variational inference, P. 220; Model identifiability, P. 220-221);
jointly assigning an a priori spatial probability distribution of said physiological signal by introducing a priori information relative to a characteristic of the experimental datum and/or a priori information relative to a property of the artery/tissue/vein dynamic system (Model, P.219; Spatial prior, P. 219-220); and
jointly assigning a priori temporal probability distribution of said physiological signal by introducing a priori information relative to the impulse response of said artery/tissue/vein dynamic system (Model, P. 219; Temporal prior, P.220)
Deco et al. (U.S. Pub. No. 2005/0009003) discloses a computer implemented method for reconstructing BOLD signals/images from fMRI data utilizing a probabilitistic approach in the spatial and temporal domains.
Hutel and Ourselin (U.S. Pub. No. 2021/0208224) discloses a computer implemented method for reconstructing BOLD signals/images from fMRI data utilizing a probabilitistic approach in the spatial and temporal domains.
Operto et al. (“Projection of fMRI data onto the cortical surface using anatomically-informed convolution kernels”) discloses using anatomical information to determine a model for mapping fMRI volumes to a cortical surface.
Kiebel et al. (“Anatomically Informed Basis Function”) discloses reconstructing an activity map of the cortical surface from fMRI data by creating a forward model and solving the inverse problem by minimizing a cost function including a distribution and regression operator for the spatial and temporal domains incorporation anatomical information.
Behjat et al. (“Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping”) discloses reconstructing an activity map of the cortical surface from fMRI data by creating a forward model and solving the inverse problem by minimizing a cost function including a distribution and regression operator for the spatial and temporal domains incorporation anatomical information.
Tong et al. (U.S. Pub. No. 2016/0287100) discloses a system and method for generating an anatomical map of a subject’s brain using fMRI and spatial and temporal regressors.
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/Johnathan Maynard/Examiner, Art Unit 3798
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