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.
Response to Amendment
This is a response to Applicant’s amendment filed on 17 July 2026, wherein:
Claims 1, 4, 6, 8, 12, 13, 16, 18, 20, 23, 24, 26, and 27 are amended.
Claims 2, 3, 5, 9-11, 14, 15, 17, 21, 22, and 25 are original.
Claims 7, 19, and 28 are canceled.
Claims 29-31 are new.
Claims 1-6, 8-18, 20-27, and 29-31 are pending.
Information Disclosure Statement
The information disclosure statement filed 05 August 2026 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the citation for non-patent document cite no. B1 is improperly missing relevant page numbers. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Claim Objections
Claims 8, 9, 20 are objected to because of the following informalities:
Claims 8 and 20 each have been amended to recite “wherein wherein” in the preamble. The term “wherein” should only be recited once in this instance.
Dependent claim 9 inherits the deficiencies of its respective parent claims, and is thus objected to under the same rationale.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code 101 not included in this action can be found in a prior Office action.
Claims 1-6, 8-18, 20-27, and 29-31 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 active-state functional neuroimaging data collected while the test subject performs a movie-watching task in which the test subject is shown a movie clip that includes a plurality of segments depicting daily-life activities, the plurality of segments including a first segment containing character dialog or monolog speech in a primary language of the test subject and a second segment containing character action and no speech, wherein the neuroimaging data includes a sequence of images of the test subject's brain, each image in the sequence comprising a plurality of voxels;
extracting a feature set from the neuroimaging data, wherein extracting the feature set includes generating a statistical parametric map that represents each voxel’s association with a presence or absence of speech;
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 movie-watching task in which the test subject is shown a movie clip that includes a plurality of segments depicting daily-life activities, the plurality of segments including a first segment containing character dialog or monolog speech in a primary language of the test subject and a second segment containing character action and no speech, wherein the functional neuroimaging data includes a first sequence of images of the test subject's brain, each image in the first sequence comprising a plurality of voxels;
extract a feature set from the neuroimaging data, wherein extracting the feature set includes generating a statistical parametric map that represents each voxel’s association with a presence or absence of speech;
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 active-state functional neuroimaging data collected while the test subject performs a movie-watching task in which the test subject is shown a movie clip that includes a plurality of segments depicting daily-life activities, the plurality of segments including a first segment containing character dialog or monolog speech in a primary language of the test subject and a second segment containing character action and no speech, wherein the functional neuroimaging data includes a first sequence of images of the test subject's brain, each image in the first sequence comprising a plurality of voxels;
extracting a feature set from the neuroimaging data, wherein extracting the feature set includes generating a statistical parametric map that represents each voxel’s association with a presence or absence of speech;
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. 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. Investpic, 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 § 103
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.
Claims 1-6, 8-18, 20-27, and 29-31 are rejected under 35 U.S.C. 103 as being unpatentable over Bach et al. (US 2022/0133194, hereinafter referred to as Bach) in view of Kibar.
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 active-state functional neuroimaging data collected while the test subject performs a movie-watching task (Bach, para. 216, “also record the person's brain activity while watching a video”), wherein the neuroimaging data includes a sequence of images of the test subject's brain, each image in the sequence comprising a plurality of voxels (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.”);
extracting a feature set from the functional neuroimaging data, wherein extracting the feature set includes generating a statistical parametric map that represents each voxel’s association with a presence or absence of speech (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.”).
Bach does not explicitly teach in which the test subject is shown a movie clip that includes a plurality of segments depicting daily-life activities, the plurality of segments including a first segment containing character dialog or monolog speech in a primary language of the test subject and a second segment containing character action and no speech.
However, in related art, Kibar teaches the test subject is shown a movie clip that includes a plurality of segments depicting daily-life activities, the plurality of segments including a first segment containing character dialog or monolog speech in a primary language of the test subject and a second segment containing character action and no speech (Kibar, pg. 26, lines 16-17, “customizing tests (according to… language,… or other demographic properties)”; pg. 18, lines 19-28, “Participants will be shown six movie clips of five minutes each. In tone and content, the clips range from neutral-to-upbeat. Participants will be asked to watch the clips and simultaneously describe what they see. During each of the six clips, one distractor will be interjected (See last page for a list of Distractors). There are six types of distractors: auditory verbal (i.e., a voice overlay), auditory non-verbal (i.e., a phone ringing), visual verbal (i.e., sentences running along the visual field), visual non-verbal (i.e., a picture flashing on the screen), human presence nonverbal (i.e., the tester sitting silently in a chair next to the participant), and human presence verbal (i.e., the tester sitting silently in a chair next to the participant and speaking-neutral content). Each distractor will last 15 seconds, followed with a 15 second off time.”).
However, it would have been obvious to a person having ordinary skill in the art for Bach to include the movie clips of Kibar so that the video in Bach to be a movie clip that includes a plurality of segments depicting daily-life activities, the plurality of segments including a first segment containing character dialog or monolog speech in a primary language of the test subject and a second segment containing character action and no speech because the movie clips may be “selected because they are known to induce emotional states .and cognitive states such as fear, anger, happiness, confusion, frustration, or disorientation in typical subjects” and “[r]eal-world settings can be imitated during a clinical trial to reduce a gap between efficacy and effectiveness”. See Kibar at line 24 of pg. 12 – line 2 of pg. 13 and lines 11-13 of pg. 34, respectively.
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 movie-watching task (Bach, para. 216, “also record the person's brain activity while watching a video”.) , wherein the neuroimaging data includes a sequence of images of the test subject's brain, each image in the sequence comprising a plurality of voxels (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.”);
extract a feature set from the functional neuroimaging data, wherein extracting the feature set includes generating a statistical parametric map that represents each voxel’s association with a presence or absence of speech (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.”).
Bach does not explicitly teach in which the test subject is shown a movie clip that includes a plurality of segments depicting daily-life activities, the plurality of segments including a first segment containing character dialog or monolog speech in a primary language of the test subject and a second segment containing character action and no speech.
However, in related art, Kibar teaches the test subject is shown a movie clip that includes a plurality of segments depicting daily-life activities, the plurality of segments including a first segment containing character dialog or monolog speech in a primary language of the test subject and a second segment containing character action and no speech (Kibar, pg. 26, lines 16-17, “customizing tests (according to… language,… or other demographic properties)”; pg. 18, lines 19-28, “Participants will be shown six movie clips of five minutes each. In tone and content, the clips range from neutral-to-upbeat. Participants will be asked to watch the clips and simultaneously describe what they see. During each of the six clips, one distractor will be interjected (See last page for a list of Distractors). There are six types of distractors: auditory verbal (i.e., a voice overlay), auditory non-verbal (i.e., a phone ringing), visual verbal (i.e., sentences running along the visual field), visual non-verbal (i.e., a picture flashing on the screen), human presence nonverbal (i.e., the tester sitting silently in a chair next to the participant), and human presence verbal (i.e., the tester sitting silently in a chair next to the participant and speaking-neutral content). Each distractor will last 15 seconds, followed with a 15 second off time.”).
However, it would have been obvious to a person having ordinary skill in the art for Bach to include the movie clips of Kibar so that the video in Bach to be a movie clip that includes a plurality of segments depicting daily-life activities, the plurality of segments including a first segment containing character dialog or monolog speech in a primary language of the test subject and a second segment containing character action and no speech because the movie clips may be “selected because they are known to induce emotional states .and cognitive states such as fear, anger, happiness, confusion, frustration, or disorientation in typical subjects” and “[r]eal-world settings can be imitated during a clinical trial to reduce a gap between efficacy and effectiveness”. See Kibar at line 24 of pg. 12 – line 2 of pg. 13 and lines 11-13 of pg. 34, respectively.
Regarding claims 2, 14, and 25, Bach in view of Kibar 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 in view of Kibar 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.”).
Bach does not explicitly teach the future condition is at least six months after collecting the functional neuroimaging data.
However these differences are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The method would be performed the same regardless of how much time passed after the neuroimaging data is collected. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention for the future condition in Bach to be at least six months after collecting the functional neuroimaging data because time that has passed after collecting the functional neuroimaging data is merely a design choice that does not functionally relate to the steps claimed and has not been disclosed to solve any stated problem or is for any particular purpose which does not patentably distinguish the claimed invention.
Regarding claims 5 and 17, Bach in view of Kibar 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 6, 18, and 27, Bach in view of Kibar teaches the method of claim 1, the system of claim 13, and the computer-readable storage medium of claim 24, wherein the active-state functional neuroimaging data is obtained using one or more of:
functional magnetic resonance imaging (fMRI) data (Bach, para. 122, “a functional magnetic resonance imager (fMRI)”);
functional near-infrared spectroscopy (fNIRS) data (Bach, para. 122, “a functional near-image spectroscopy (fNIR)”); or
magnetoencephalography (MEG) data (Bach, para. 122, “a magnetoencephalography imager (fMRI)”).
Regarding claims 8, 9, and 20, Bach in view of Kibar teaches the method of claim 1 and the system of claim 13 wherein wherein extracting the feature set from the neuroimaging data includes:
generating, based on the sequence of images, a T-map characterizing activation of the voxels, wherein the T-map is based at least in part on a difference between images collected during the first segment of the movie clip and images collected during the second segment of the movie clip 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. 172, “The mapper 151 superimposes a preferably live representation of brain activity derived from the neurophysiological data 102 onto a 3D model of a brain. This illustrates areas and/or pathways of the brain that are activated by a given task, and how those area and pathways change over time while the person performs the tasks.” 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.” 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, claims 9 and 20: 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 in view of Kibar 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 in view of Kibar 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 in view of Kibar teaches the method of claim 1 and the system of claim 13 wherein the input data set includes anatomical neuroimaging data characterizing one or more brain structures of the test subject (claim 23: and wherein the input data set further includes the anatomical neuroimaging data) (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.” 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.”).
Regarding claims 29-31, Bach in view of Kibar teaches the method of claim 1, the system of claim 13, and the computer-readable storage medium of claim 24, wherein collecting the neuroimaging data from the test subject further comprises collecting 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”).
Response to Arguments
Applicant's arguments with respect to the rejection of the claims under 35 USC 101 have been fully considered but they are not persuasive. In pg. 11-12, Applicant asserts that the claims do not recite a method of organizing human activity. Here, Applicant asserts that the independent claims have been amended to recite what amounts to exposing the test subject’s central nervous system to a highly-specific clinically-engineered stimulus while collecting neuroimaging data, and that a process of stimulus eliciting a physiological response is not managing personal behavior or interactions.
Examiner is not persuaded. The stimulus is a movie clip, the elements of which are conventional to movie clips (i.e., a plurality of segments depicting daily-life activities, the plurality of segments including a first segment containing character dialog or monolog speech in a primary language of the test subject and a second segment containing character action and no speech) and not “a highly-specific clinically-engineered stimulus”. In particular, the disclosure is silent regarding such a stimulus being “highly-specific” and “clinically-engineered”. Further, the mere presentation of a stimulus (including a movie clip, which is well-known as illustrated by the prior art record) is wholly encompassed in the abstract idea grouping of a certain method of organizing human activity as it is the conventional first step of neurological testing performed between a tester and subject similar to the testing step in In re Meyer (688 F.2d 789 (1982)). Additionally, the collecting of neuroimaging data is expressly collecting information of “collecting information, analyzing the collected information, and outputting the results of the collection and analysis” which the courts have repeatedly identified as wholly encompassed in both a certain method of organizing human activity and mental processes.
In pg. 12-13, Applicant asserts the present claims do not recite a mental process. Here, Applicant asserts that independent claims recite obtaining neuroimaging data and that a human being cannot directly observe such activity because the use of imaging technology, such as fMRI, is required to obtain neuroimaging data. Applicant also asserts that the independent claims also recite the “extracting a feature set” limitation and that a human mind cannot practically perform the computations to produce a brain mask.
Examiner is not persuaded. “Obtaining neuroimaging data” is merely collecting information. In fact, the claims are constructed such that no additional element is recited to perform the method of independent claim 1 and is thus reasonably construed to be performed by a human. Similarly, independent claims 13 and 24 recite that a processor is used to obtain the neuroimaging data and produce a brain mask. Applicant is reminded that a claim that requires a computer may still recite a mental process. See MPEP 2106.04(a)(2)(III)(C). 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. Even if an fMRI were to be claimed, it would amount to merely adding insignificant extra-solution activity to the judicial exception (e.g., mere extra-solution data gathering in conjunction with a law of nature or abstract idea). In fact, if the entirety of the possible disclosed structural elements were to be claimed, they would be identified under Prong Two of Step 2A and under Step 2B to be an entirely conventional system known throughout cognitive testing as the disclosure’s asserted inventive matter is merely the presentation of movie clips as the stimuli which could be construed as merely printed matter and thus not bearing any patentable weight regardless of the fact that such matter as stimuli was well-known before the effective filing date of the claimed invention. The disclosure does not even provide a new manner of analyzing the data which is itself encompassed in the judicial exception. See SAP America Inc. v. Investpic, LLC.
In pg. 13, Applicant asserts that the claims do not recite a mathematical concept. Here, Applicant asserts the claims are similar to those in Example 38 by assertedly being based on mathematical concepts, but mathematical concepts are recited in the claims.
Examiner is not persuaded. Applicant is reminded that Example 38 was provided by the Office as an example to further understanding and is not precedential. Additionally, contrary to Applicant's assertions, the instant amended claims are wholly unrelated to Example 38. As identified in the rejection above, the extracting through the determining steps in the claims recite mathematical calculations and thus recite mathematical concepts.
Under Prong Two of Step 2A, in pg. 13-14, Applicant asserts the claims amount to an improvement to a technology or technical field by assertedly improving over standard laboratory test for neurocognitive decline (NCD) by applying a specific stimulus, the movie-watching task.
Examiner is not persuaded. A “movie” can be many things and the disclosure is silent regarding any particular movies. Thus, it is not a “specific” stimulus. Further, as identified above, the mere use of a movie as a the stimulus is not an improvement as it could be construed as printed matter and was well-known before the effective filing date of the claimed invention.
Applicant further asserts that the movie-watching task stimulates the brain in a specific manner yields information about brain activity that was not previously accessible.
Examiner is not persuaded. In order for the movie-watching task to stimulate the brain in a specific manner, particular clips would need be evaluated and identified to be trigger specific stimulations, and then later selected for an intended stimulation in practice. The disclosure is silent regarding such features. The mere use of a movie does not provide specific manners of stimulation. Further, as identified above, the well-known nature of using movie clips directly negates any assertion of being not previously accessible.
Under Step 2B, in pg. 15-16, Applicant asserts that the prior art is silent regarding the claimed method steps and points to two non-patent literature documents.
Examiner is not persuaded. Applicant is directed to the rejections of the claims under 35 USC 103 which illustrate that the prior art teach the claimed method steps. Furthermore, the prior art record (including prior art provided by Applicant) illustrates the use of movie clips and other forms of naturalistic paradigms as the stimuli was well-known before the effective filing date of the claimed invention. Again, the mere use of movie clips as the stimuli could be construed as printed matter and is wholly encompassed in the judicial exception, not any additional element.
Applicant's arguments with respect to the rejection of the claims under 35 USC 102 have been fully considered but they are not persuasive. In pg. 16-17, Applicant misrepresents what Bach discloses and asserts that Bach does not teach the amended claims.
Examiner is not persuaded. In particular, Bach is silent regarding the term “cognitive efficacy” but describes assessing neurocognitive decline as illustrated in the rejections above. Applicant is directed to the rejections of the claims under 35 USC 103 above which have been updated to address the amendments to the claims.
In pg. 17, Applicant also asserts that, with respect to claims 8 and 20, Bach does not disclose constructing a T-map.
Examiner is not persuaded. As illustrated in the rejection, while Bach does not explicitly use the term “T-map”, one of ordinary skill in the art would readily understand that Bach describes constructing a T-map.
Applicant then asserts that new claims 29-31 are allowable due to their dependencies from independent claims 1, 13, and 24.
Examiner is not persuaded. Applicant is directed to the rejections above which illustrate that neither the independent claims nor the dependent claims are allowable.
The rejections stand.
Conclusion
In addition to the prior art made of record and not relied upon in the Office Action mailed 05 February 2026, the additional prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kibar et al. (US 2004/0210159) is the US PG-Pub related to the Kibar reference used in the 103 rejections.
Tseng et al. (US 2010/0208205) discloses using movie clips as the stimuli in cognitive tests.
el Kaliouby et al. (US 2011/0263946) discloses creating a database of movie clips for particular stimulations uses in cognitive testing.
Furman et al. (US 2021/0390366) also discloses using movies and movie clips as stimuli in cognitive assessment.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LANE whose telephone number is (303)297-4311. The examiner can normally be reached Monday - Friday 8:00 - 4:30 MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL LANE/ Examiner, Art Unit 3715
/XUAN M THAI/ Supervisory Patent Examiner, Art Unit 3715
