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 .
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.
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 1-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without including additional elements that are sufficient to amount to significantly more than the judicial exception itself.
Step 1
The claims are directed to a method and products which fall under at least one of the four statutory categories (STEP 1: YES).
Step 2A, Prong 2
Independent claim 1 recites:
A method for assessing neurocognitive decline (NCD) in a test subject, the method comprising:
collecting neuroimaging data from the test subject, the neuroimaging data including one or more of:
active-state functional neuroimaging data collected while the test subject performs a naturalistic language-based task;
resting-state functional neuroimaging data collected while the test subject is at rest; or
anatomical neuroimaging data characterizing one or more brain structures of the test subject;
extracting a feature set from the functional neuroimaging data;
defining an input data set that includes at least the feature set; and
determining a predicted NCD status for the test subject by analyzing the input data set using a classifier that has been trained using machine learning to predict the NCD status for an individual, wherein training of the classifier is based on corresponding input data sets obtained for a plurality of training subjects having known NCD status based on a neurocognitive assessment.
Independent claim 13 recites:
A system comprising:
a memory; and
a processor coupled to the memory and configured to:
obtain neuroimaging data from a test subject, the neuroimaging data including active-state functional neuroimaging data collected while the test subject performs a naturalistic language-based task;
extract a feature set from the functional neuroimaging data;
define an input data set that includes at least the feature set; and
determine a predicted NCD status for the test subject by analyzing the input data set using a classifier that has been trained using machine learning to predict the NCD status for an individual, wherein training of the classifier is based on corresponding input data sets obtained for a plurality of training subjects having known NCD status based on a neurocognitive assessment.
Independent claim 24:
A computer-readable storage medium having stored therein program code instructions that, when executed by a processor in a computer system, cause the processor to perform a method comprising:
obtaining neuroimaging data from a test subject, the neuroimaging data including one or more of:
active-state functional neuroimaging data collected while the test subject performs a naturalistic language-based task;
resting-state functional neuroimaging data collected while the test subject is at rest; or
anatomical neuroimaging data characterizing one or more brain structures of the test subject;
extracting a feature set from the functional neuroimaging data;
defining an input data set that includes at least the feature set; and
determining a predicted NCD status for the test subject by analyzing the input data set using a classifier that has been trained using machine learning to predict the NCD status for an individual, wherein training of the classifier is based on corresponding input data sets obtained for a plurality of training subjects having known NCD status based on a neurocognitive assessment.
All of the foregoing underlined elements identified above amount to the abstract idea grouping of a certain method of organizing human activity because they amount to managing personal behavior or interactions between people (including social activities, teaching, and following rules or instructions) by merely collecting information, analyzing the collected information, and outputting the results of the collection and analysis. These elements are also interpreted as a series of steps that could reasonably be performed by mental processes with the aid of pen and paper because the claims, under their broadest reasonable interpretation, cover performance of the limitations in the mind (including observation, evaluation, judgment, opinion) but for the recitation of generic computer components. See MPEP 2106.04(a)(2)(III)(C) - A Claim That Requires a Computer May Still Recite a Mental Process. Even if humans would use a physical aid to help them complete the recited steps, the use of such physical aid does not negate the mental nature of these limitations. It is noted that “a screen” as claimed and disclosed is merely a visual output and not a physical element like a smartphone touchscreen is a physical element. Lastly, the extracting through determining steps amount to the abstract idea grouping of mathematical concepts because they recite mathematical calculations as defined in MPEP 2106.05(a)(2)(I) which recites that a “claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the ‘mathematical concepts’ grouping” because a “mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word ‘calculating’ in order to be considered a mathematical calculation. For example, a step of ‘determining’ a variable or number using mathematical methods or ‘performing’ a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation."
The dependent claims amount to merely further defining the judicial exception.
Therefore, the claims recite a judicial exception. (STEP 2A, PRONG 1: YES).
Step 2A, Prong 2
This judicial exception is not integrated into a practical application because the independent and dependent claims do not include additional elements that are sufficient to integrate the exception into a practical application under the considerations set forth in MPEP 2106.04(d). The elements of the claims above that are not underlined constitute additional elements.
The following additional elements, both individually and as a whole, merely generally link the judicial exception to a particular technological environment or field of use: a system comprising a memory and a processor coupled to the memory (claim 13) and a computer-readable storage medium having stored therein program code instructions that, when executed by a processor in a computer system, cause to the processor perform the method (claim 24). This is evidenced by the manner in which these elements are disclosed in the drawings and the instant specification. For example, the drawings are silent regarding these elements and thus identify the computerized system as ancillary to the claimed invention. Similarly, para. 7, 9, 26, and 74-76 merely provide stock descriptions of generic computer hardware and software components in any generic arrangement and illustrate an embodiment that is merely using a software application to cause a computer to implement the judicial exception. Thus, the computer components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. The claims are silent regarding any specific rules with specific characteristics that improve the functionality of the computer system. See, for example, at least para. 30 which explicitly identifies this by reciting that “many combinations of classifier algorithm, function neuroimaging data (and features extracted therefrom), and neurocognitive tests can be used.” See also, for example, para. 83 which identifies that a “variety of classifiers (machine-learned algorithms that can be trained to predict an outcome for an unseen data sample based on a set of data samples with known outcomes) can be used… The parameters used for training and testing the classifier may be varied, including the size of training data sets and the particular combination of inputs. A particular algorithmic implementation of training is not required.” None of the hardware offer a meaningful limitation beyond generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Again, this is evidenced by the manner in which these elements are disclosed in the drawings and specification as identified above. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of the additional elements does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014). Additionally, the claims do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition nor do they apply or use a judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. For instance, the disclosure identifies that the claimed invention is generally for assessing a cognitive state and in particular for using functional neuroimaging data from naturalistic language processing tasks and machine learning to detect a current state and/or forecast a future state of neurocognitive decline in a subject which is merely collecting information, analyzing the collected information, and outputting the results of the collection and analysis. See, for example, at least para. 2 of the specification. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (STEP 2A, PRONG 2: YES).
Step 2B
The independent and dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under the considerations set forth in MPEP 2106.05. As identified in Step 2A, Prong 2, above, the claimed system and the process it performs does not require the use of a particular machine, nor does it result in the transformation of an article. Although the claims recite elements, identified above, for performing at least some of the recited functions, these elements are recited at a high level of generality in a conventional arrangement for performing their basic computer functions (i.e., collecting, receiving, processing, outputting data). This is evidenced by the manner in which these elements are disclosed in the instant specification. For example, the drawings are silent regarding these elements and thus identify the computerized system as ancillary to the claimed invention. Similarly, para. 7, 9, 26, and 74-76 merely provide stock descriptions of generic computer hardware and software components in any generic arrangement and illustrate an embodiment that is focused on a software application that merely causes a computer to implement the judicial exception. Thus, the computer components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. The claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. Thus, the focus of the claimed invention is on the analysis of the collected data, which is itself at best merely an improvement within the abstract idea. See pg. 2-3 in SAP America Inc. v. lnvestpic, LLC (890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018) which proffered “[w]e may assume that the techniques claimed are groundbreaking, innovative, or even brilliant, but that is not enough for eligibility. Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. The claims here are ineligible because their innovation is an innovation in ineligible subject matter. Their subject is nothing but a series of mathematical calculations based on selected information and the presentation of the results of those calculations. Furthermore, the steps are merely recited to be performed by, or using, the elements while the specification makes clear that the computerized system itself is ancillary to the claimed invention as identified above. This further identifies that none of the hardware offer a meaningful limitation beyond, at best, generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Viewed as a whole, these additional claim elements do not provide meaningful limitation to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea of itself (STEP 2B: NO).
Therefore, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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 1-28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bach et al. (US 2022/0133194, hereinafter referred to as Bach).
Regarding claims 1 and 24, Bach teaches a method for assessing neurocognitive decline (NCD) in a test subject (claim 1) and a computer-readable storage medium having stored therein program code instructions that, when executed by a processor in a computer system (Bach, para. 16, “non-transitory computer-readable medium having instructions stored thereon”), cause the processor to perform a method (claim 24), the method comprising:
collecting neuroimaging data from the test subject (Bach, Fig. 5, Simultaneously take neurometric measurements of the persons as they are performing the tasks 255), the neuroimaging data including one or more of:
active-state functional neuroimaging data collected while the test subject performs a naturalistic language-based task (Bach, para. 216, “also record the
person's brain activity while watching a video”);
resting-state functional neuroimaging data collected while the test subject is at rest (Bach, para. 216, “record the person's brain activity while resting to determine an average amount of energy in a specific frequency using specific scalp locations”); or
anatomical neuroimaging data characterizing one or more brain structures of the test subject (Bach, para. 124, “Diffusion MM measures the rate of water diffusion in the brain and is useful in revealing the structural connectivity of the brain.”);
extracting a feature set from the functional neuroimaging data (Bach, para. 188, “data processed using PCA and/or ICA is used to generate 3D maps or graphs illustrating the state and/or functional connectivity of the subject's brain and/or 3D maps or graphs that use color, brightness, and/or thickness to illustrate a ratio or other comparison between the pathways' task-state power values and the baseline power values.”);
defining an input data set that includes at least the feature set (Bach, para. 217, “The model or signature can be a statistical one based on a PCA and/or ICA of the data.”); and
determining a predicted NCD status for the test subject by analyzing the input data set using a classifier that has been trained using machine learning to predict the NCD status for an individual, wherein training of the classifier is based on corresponding input data sets obtained for a plurality of training subjects having known NCD status based on a neurocognitive assessment (Bach, para. 249, “Signatures are further refined by inputting data relating to several subjects' performances on tasks or in practical, real-world activities into the machine learning apparatus. The machine learning apparatus produces a matrix correlating a plurality of variables, including performance in tasks and performance in practical, real-world activities, with brain activity or quantitative representations of the brain systems' functional integrities.”).
Regarding claim 13, Bosch teaches a system comprising:
a memory (Bach, Fig. 1, database 141); and
a processor coupled to the memory (Bach, Fig. 1, decision engine 143, statistical engine 150, reporting/diagnostic engine 160) and configured to:
obtain neuroimaging data from a test subject, the neuroimaging data including active-state functional neuroimaging data collected while the test subject performs a naturalistic language-based task (Bach, para. 216, “also record the person's brain activity while watching a video”. “Watching a video” is a naturalistic language-based task as identified by the instant specification. See para. 6 of the instant specification which examples “watching a movie clip” as a naturalistic language-processing task.);
extract a feature set from the functional neuroimaging data (Bach, para. 188, “data processed using PCA and/or ICA is used to generate 3D maps or graphs illustrating the state and/or functional connectivity of the subject's brain and/or 3D maps or graphs that use color, brightness, and/or thickness to illustrate a ratio or other comparison between the pathways' task-state power values and the baseline power values.”);
define an input data set that includes at least the feature set (Bach, para. 217, “The model or signature can be a statistical one based on a PCA and/or ICA of the data.”); and
determine a predicted NCD status for the test subject by analyzing the input data set using a classifier that has been trained using machine learning to predict the NCD status for an individual, wherein training of the classifier is based on corresponding input data sets obtained for a plurality of training subjects having known NCD status based on a neurocognitive assessment (It is noted that Bach, para. 23, “the system/method provides quantitative measures of cognitive reserve, brain entropy, and other cognitive traits.” Para. 135, “Cognitive reserve and cognitive resilience also refer to the ability of the brain to optimize or maximize performance through the differential recruitment of brain networks or alternate cognitive strategies. The scientific literature doesn't describe measurements for reserve very well, except with respect to decremented nervous systems, such as those beset by Alzheimer's and dementia.” Para. 190, “The report 161 also describes and graphically illustrates how the subject's measured cognitive efficiency… compares with that of one or more populations of persons.” Para. 249, “Signatures are further refined by inputting data relating to several subjects' performances on tasks or in practical, real-world activities into the machine learning apparatus. The machine learning apparatus produces a matrix correlating a plurality of variables, including performance in tasks and performance in practical, real-world activities, with brain activity or quantitative representations of the brain systems' functional integrities.” Para. 250, “In block 588, using the signatures as a normative baseline, construct a spatial, spatio-temporal, and/or frequency-bandpassed representation of the systems and pathways in a subject's brain.”).
Regarding claims 2, 14, and 25, Bach teaches the method of claim 1, the system of claim 13, and the computer-readable storage medium of claim 24 wherein the predicted NCD status is one of:
a binary status that distinguishes between a normal neurocognitive status and presence of NCD (Bach, para. 190, “The report 161 also describes and graphically illustrates how the subject's measured cognitive efficiency… compares with that of one or more populations of persons.”);
a continuous variable that represents severity of NCD (Bach, para. 190, “The report 161 also describes and graphically illustrates how the subject's measured cognitive efficiency… compares with that of one or more populations of persons.”); or
a continuous variable that represents likelihood of NCD (Bach, para. 9, “identify a
probabilistic relationship between the person's neurophysiological data and the person's performance.”).
Regarding claims 3, 4, 15, 16, and 26, Bach teaches the method of claim 1, the system of claim 13, and the computer-readable storage medium of claim 24 wherein the predicted NCD status corresponds to one or both of a present condition of the test subject (Bach, para. 190, “The report 161 also describes and graphically illustrates how the subject's measured cognitive efficiency… compares with that of one or more populations of persons.”) or a forecast of a future condition of the test subject (Bach, para. 249, “The machine learning apparatus also creates a prediction heuristic based on the correlation matrix which generates a prediction of a person's performance in a selected one of the practical, real-world activities as a function of the person's brain activity and performance of a task.”).
Regarding claims 5 and 17, Bach teaches the method of claim 1 and the system of claim 13 wherein the neurocognitive assessment for the training subjects is determined using one or both of a standard cognitive assessment test or a clinician's diagnosis (Bach, para. 226, “The bundle 375 includes a neuro validation battery 376, a simple reaction time task 378, a procedural reaction time task 380, a go/no-go task 382, a code substitution task 384, a spatial processing task 386, a match to sample task 388, a memory search task 389, and another simple reaction time task 378 to measure reaction time after the rest of the tasks are completed. The neuro validation battery 376 comprises a sustained attention task, an encoding task, and an image recognition memory task.” Para. 301, “The following assessments, both pre- and post-training, were performed with behavioral and electrophysiological data recording: Baseline Task of Eyes Open/Eyes Closed, the Eriksen flanker task, the DANA standard neurocognitive assessment (Table 1), and surveys on sleep, stress and emotional resilience.”).
Regarding claims 7, 19, and 28, Bach teaches the method of claim 6, the system of claim 18, and the computer-readable storage medium of claim 27 wherein the naturalistic language-based task includes one or more of:
a movie-watching task in which the test subject is shown a movie clip that includes at least one segment with dialog or monolog and at least one non-speaking segment ( (Bach, para. 216, “record the person's brain activity while resting to determine an average amount of energy in a specific frequency using specific scalp locations, and also record the person's brain activity while watching a video”. “Watching a video” is a naturalistic language-based task as identified by the instant specification. See para. 6 of the instant specification which examples “watching a movie clip” as a naturalistic language-processing task.”); or
a listening task in which the test subject is presented with a stream of spoken-language stimuli.
Regarding claims 8, 9, and 20, Bach teaches the method of claim 1 and the system of claim 13 wherein the neuroimaging data includes a sequence of images of the test subject's brain, each image comprising a plurality of voxels, and wherein extracting the feature set from the neuroimaging data includes:
generating, based on the sequence of images, a T-map characterizing activation of voxels in response to stimulus (Bach, para. 101, “The authors also noted that the concept of a "node" or "voxel" may be defined by the imaging resolution producing the brain image (which is insufficient to distinguish each neuron). For example, a node may be the anatomically localized region or voxel of an fMRI image or equate to whatever group of neurons an individual EEG electrode or MEG sensor senses.” Para. 210, “FIG. 2 illustrates one embodiment of a brain-mapped spatial representation 170 of brain activity, oriented to provide a side view perspective. The darker areas represent high activity. FIG. 3 illustrates another embodiment of a brain-mapped spatial representation 172 of the brain, oriented to provide a top-view perspective. In FIGS. 2 and 3, especially activated (i.e., differentially and positively activated, as compared to a baseline) pathways are illuminated, illustrating the strength and multiplicity of neural links between regions of the brain. A brain-mapped spatial representation 170 can display only selected regions of the brain. Certain exterior regions can be removed from view, as they are in FIG. 4, to better illustrate selected brain regions and pathways.”);
applying a binary brain mask to select a plurality of regions of interest, wherein the binary brain mask is defined based on a group-level analysis of respective T-maps generated for the training subjects (Bach, para. 210, “FIG. 2 illustrates one embodiment of a brain-mapped spatial representation 170 of brain activity, oriented to provide a side view perspective. The darker areas represent high activity. FIG. 3 illustrates another embodiment of a brain-mapped spatial representation 172 of the brain, oriented to provide a top-view perspective. In FIGS. 2 and 3, especially activated (i.e., differentially and positively activated, as compared to a baseline) pathways are illuminated, illustrating the strength and multiplicity of neural links between regions of the brain. A brain-mapped spatial representation 170 can display only selected regions of the brain. Certain exterior regions can be removed from view, as they are in FIG. 4, to better illustrate selected brain regions and pathways.” Para. 541, “(1) efficiently represent brain activity data using matrices that characteristically indicate correlations between different brain regions and brain wave frequencies; (2) "alphabetize" the characteristic states represented by the matrices; (3) use artificial intelligence (aka machine learning) to recognize probabilistic relationships between sequences of brain states and objective measures of the quality or performance achieved by the decision; (4) apply that learning to predict performance on subsequent decisions or conscious actions;”); and
applying a statistically-based feature selection procedure to the voxels of the T-map that are in the regions of interest, wherein the statistically-based feature selection procedure includes one or more of feature selection based on Pearson correlation, L1 regularization penalty feature selection, or principal component analysis (Bach, para. 211, “Brain-mapped spatial representations 170 and 172 can be generated using principal component analysis (PCA), independent component analysis (ICA), or other data transforms such as sparse and low-rank matrix decomposition, t-Distributed Stochastic Neighbor Embedding (tSNE), etc.”).
Regarding claims 10 and 21, Bach teaches the method of claim 1 and the system of claim 13 wherein the classifier is one of:
a support vector machine classifier;
a Bayesian classifier;
a Gaussian Naive Bayes classifier (Bach, para. 551, “Gaussian mixture models or other linear or non-linear clustering methods”); or
a random forest classifier.
Regarding claims 11 and 22, Bach teaches the method of claim 1 and the system of claim 13 further comprising:
obtaining demographic data for the test subject (Bach, para. 167, “Surveys
can also be used to collect other information such as… demographics”),
wherein the input data set further includes the demographic data (Bach, para. 273, “The baseline can be… relevant demographic baselines.”).
Regarding claims 12 and 23, Bach teaches the method of claim 1 and the system of claim 13 wherein the input data set includes the active-state functional neuroimaging data and the anatomical neuroimaging data (Bach, para. 217, “One model relates different types of brain activity in different regions and pathways of the brain to task performances. Another model is a 3D signature or model of brain activity corresponding to different task performances.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Prichep (US 7,647,098) discloses predicting cognitive decline using neuroimaging during stimuli presentation.
Bosch et al. discloses recording and analyzing neuroimages taken using fMRI during a speech comprehension task (listening task) to assess cognitive reserve as in healthy elders, amnestic mild cognitive impairment, and mild Alzheimer’s disease. Bosch et al. further illustrates that the claimed feature extraction steps are commonly known.
Huijbers et al. (WO 2021/148310 and US 2023/0048408) discloses using naturalistic paradigms, including movies.
Geva et al. (US 2016/0038049) discloses analyzing neurophysiological data including neurophysiological data recorded during lower-level cognitive tasks, including watching a movie.
Purushottam et al. (US 11,263,749) also illustrates that the claimed feature extraction steps are commonly known.
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/DANIEL LANE/ Examiner, Art Unit 3715