DETAILED ACTION The present application is being examined under the pre-AIA first to invent provisions. Claim Status Claims 2-21 are currently pending and under exam herein. Priority The instant Application is a continuation of U.S. App. No. 14/433,279 nationalized April 2, 2015, which is a National Phase application of International App. No. PCT/US 2013/064474 filed October 11, 2013, which claims the benefit of U.S. Prov. App. No. 61/822,382 filed May 12, 2013 and U.S. Prov. App. No. 61/712,430 filed Oct. 11, 2012. Priority is acknowledged to the earliest effective filing date of October 11, 2012 for each of claims 2-21 herein. Information Disclosure Statement The Information Disclosure Statement filed 10 August 2022 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS is included with this Office Action. Drawings The Drawings filed 16 May 2022 are accepted. Specification Note : All references to the Specification herein pertain to the PG publication: US 20220374739 . Claim Rejections - 35 USC § 112 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 appl icant regards as his invention. Claim s 2-21 are rejected under 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 applicant regards as the invention. Claims 2 and 12 recite, “ wherein the CorrCA performs operations including …”, wherein the recitation of the CorrCA as “performing” an operation fails to make sense, as CorrCA is an analysis technique. I t is unclear how said “technique” is above to perform an operation. It is suggested that the claims be amended to include that the CorrCA analysis includes steps of “identifying a first set of time periods…” or the like. Clarification is requested through clearer claim language . Dependent claims 2-11 and 13-21 fail to clarify the above and are also rejected herein. Claims 2 and 12 recite, “ wherein the predictive model has been trained by …” wherein the step is not clear with respect “training” as said operation occurs in the past tense herein ( has been trained ) and therefore it is not clear as to the metes and bounds of the claim coverage sought herein. As claimed, the steps directed to how the mode was trained are not limiting. Clarification through clearer claim language is requested. Dependent claims 2-11 and 13-21 fail to clarify the above and are also rejected herein. Claims 7 and 17 recite, “ wherein the second population has more members than the second population ”, wherein said recitation is not clear with respect to said language. It would appear as if the claim should read, “ wherein the second population has more members that the first ” or “ wherein the first population has more members that the second ” . Clarification is requested through clearer claim language , as currently it is unclear what two “second populations” are intended. Claims 8 and 18 recite, “ wherein the behavioral training data includes communication via social media ” wherein said recitation is not clear with respect to training data includes communication through social media. It would appear as if the training data would include communication data obtained via social media. However this is not clear as claimed. Clarification is req uested. 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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The instant rejection reflects the framework as outlined in the MPEP at 2106.04: Framework with which to Evaluate Subject Matter Eligibility : (1) Are the claims directed to a process, machine, manufacture or composition of matter; (2A) Prong One: Do the claims recite a judicially recognized exception, i.e . a law of nature, a natural phenomenon, or an abstract idea; Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and (2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept. Framework Analysis as Pertains to the Instant Claims: Step 1 Analysis: Are claims directed to process, machine, manufacture/composition of matter With respect to step ( 1 ): yes, the claims are directed to a system and method . Step 2A, Prong 1 Analysis: Do claims recite abstract idea With respect to step ( 2A )( 1 ), the claims recite abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as: mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations); certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information). With respect to the instant claims, under the ( 2A )( 1 ) evaluation, the claims are found herein to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and in conjunction with mathematical concepts (in particular mathematical relationships and formulas). The claim steps to abstract ideas are as follows: Claims 2 and 12 : generate reduced-dimensional neural data that reduces the first dimensionality of the neural data to a mapping dimensionality that is compatible with a second dimensionality of behavioral data used for training a predictive model , wherein the second dimensionality is less than the first dimensionality, wherein the reduced-dimensional neural data is generated using modulated correlated component analysis (CorrCA) , and wherein the CorrCA performs operations including: identifying a first set of time periods and a second set of time periods from the neural data , wherein each time period of the first set includes a window of time where the brain responses have a first level of audience behavioral response, wherein each time period of the second set includes a corresponding window of time where the brain responses have a second level of audience behavior response, and wherein the first level has a degree of response opposite the second level; for each time period of the first set and the second set, determining a correlation among the brain responses that is proportional to the degree of response for the respective levels of audience behavior response ; and generating the reduced-dimensional neural data having the mapping dimensionality as a time series of the correlations from each time period ; generate a behavioral prediction using the predictive model , wherein the predictive model is configured to receive, as input, the reduced- dimensional neural data and to generate, as output, the behavioral prediction indicating a behavioral response to the media stimulus, and wherein the predictive model has been trained by: receiving training data representing responses of a second population to a reference media stimulus, wherein the second population includes individuals absent from the first population, and wherein the training data includes behavioral training data and neural training data corresponding to the behavior training data; reducing the dimensionality of the neural training data to the mapping dimensionality using the operations of the CorrCA ; generating an estimation of the behavioral training data from an input of the neural training data having the mapping dimensionality; and tuning a set of tunable parameters of the predictive model using the estimation of the behavioral training data and the behavior training data ; and predict, using the behavioral prediction, a population behavioral response to a test media stimulus , wherein, save for the recitation of the system processing herein, the operations of generating data using CorrCA are steps that are directed to dimensionality reduction using mathematics, specifically operations of correlated components analysis (see Specification at least at [0046]; [0050]-[0053]. Dependent claims 3-11 and 13-21 further limit the abstract ideas herein. As such, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined herein to each cover performance either in the mind (calculations by hand or pen and paper) and performance by mathematical operation (calculation for assessment of time points as per the recited specific equations in said claims). There are no specifics as to the methodology thus, under the BRI, one could simply, for example, perform said operation with pen and paper, or, alternatively with the aid of a generic computer as a tool to perform said operations/ calculations. These recitations are similar to the concepts of collecting information, analyzing it and providing certain results from the collection and analysis ( Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations ( Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in ( Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind with pen and paper, and can include mathematical concepts. Further, see MPEP § 2106.04(a)(2), subsection III. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid ( e.g., pen and paper or a slide rule) to perform the claim limitation (see, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674: noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016): holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind" (see Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016): holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). Step 2A, Prong 2 Analysis: Integration to a Practical Application Because the claims do recite judicial exceptions, direction under ( 2A )( 2 ) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (MPEP 2106.04(d). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim is said to fail to integrate the abstract idea into a practical application (MPEP 2106.04(d).III). With respect to the instant recitations, the claims recite the following additional elements : Claim s 2 and 12: A system for predicting a population behavioral response, the system comprising: data processing hardware communicatively coupled to a plurality of electroencephalographic (EEG) sensors and operable to: receive neural data from the plurality of EEG sensors capturing brain responses from a first population in response to a media stimulus, wherein the neural data has a first dimensionality and A method comprising: receiving, at data processing hardware, neural data from a plurality of electroencephalographic (EEG) sensors capturing brain responses from a first population in response to a media stimulus, wherein the neural data has a first dimensionality , respectively . Claims 2 and 12 further include “data processing”. Dependent claims 3-11 and 13-21 further limit steps that limit the additional elements in the independent claim s above. With respect to the additional elements in the instant claims, said steps are directed to data gathering and perform functions of collecting the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or on how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g). Further steps herein directed to additional non-abstract elements of “ data processing ” do not describe any specific computational steps by which the “computer parts” perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc… are recited so generically ( i.e ., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer. (see MPEP 2106.05(f)). None of the recited dependent claims recite additional elements which would integrate a judicial exception into a practical application. Step 2B Analysis: Do Claims Provide an Inventive Concept The claims are lastly evaluated using the ( 2B ) analysis, wherein it is determined that because the claims recite abstract ideas, and do not integrate that abstract ideas into a practical application, the claims also lack a specific inventive concept. Applicant is reminded that the judicial exception alone cannot provide the inventive concept or the practical application and that the identification of whether the additional elements amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi). With respect to the instant claims, the additional elements of data gathering described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements ) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s). With respect to the instant claims, the prior art to, for example, Soleymani et al . (IEEE Transactions on Affective Computing (2012) Vol. 3:211-223) disclose that it was well-known and conventional in the art to record EEG signals to classify emotional response to, for example video content (page 211) . Further, the instant S pecification also discloses the conventional and generic computer systems [0140] . As such, a ctivities such as data gathering through EEG signal detection do not improve the functioning of a computer, or comprise an improveme nt to any other technical field; they do not ef fect a transformation of matter; nor do they provide a non-conventional or unconventional step. Rather, the d ata gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)). With respect to the computer-related elements or the general purpose computer said steps do not rise to the level of significantly more than the judicial exception. (see MPEP 2106.05(b)I-III). The dependent claims have been analyzed with respect to step 2B and none of these claims provide a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter. 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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action: (a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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 under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a). 1 . Claims 2-21 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over FILLIN "Insert the prior art relied upon." \d "[ 2 ]" Marci (U.S. 2010 / 0211439) in view of Georgopoulos (U.S. 2008 / 0091118) (IDS references). Claims 2 and 12 are directed to systems and methods, respectively, that includes: data processing hardware communicatively coupled to a plurality of electroencephalographic (EEG) sensors and operable to ( Marci: Paragraph [0028] “ The system may include a plurality of sensors, each adapted for measuring a biologically based response to the content stimulus over a period of two or more time intervals. ” A processing device coupled to a plurality of sensors is taught as system may include a plurality of sensors, each adapted for measuring a biologically based response to the content stimulus ) receive neural data from the plurality of EEG sensors capturing brain responses from a first population in response to a media stimulus, wherein the neural data has a first dimensionality ( Marci at [0008] disclosing “ recording the biologically based audience responses to a presentation (for example, a live or recorded, passive or interactive audio, visual, audio-visual presentation) and for determining a measure of moment-to-moment and overall intensity, synchrony and engagement of the audience with that stimulus presentation. ” Neural data for a group of individuals as they are presented with the past media broadcast is taught as recording the biologically based audience responses to a presentation and for determining a measure of moment-to-moment and overall intensity, synchrony and engagement of the audience with that stimulus presentation ) . Marci does not explicitly disclose claim limitations as directed to : generate reduced-dimensional neural data that reduces the first dimensionality of the neural data to a mapping dimensionality that is compatible with a second dimensionality of behavioral data used for training a predictive model, wherein the second dimensionality is less than the first dimensionality, wherein the reduced-dimensional neural data is generated using modulated correlated component analysis (CorrCA), and wherein the CorrCA performs operations including: identifying a first set of time periods and a second set of time periods from the neural data, wherein each time period of the first set includes a window of time where the brain responses have a first level of audience behavioral response, wherein each time period of the second set includes a corresponding window of time where the brain responses have a second level of audience behavior response, and wherein the first level has a degree of response opposite the second level; for each time period of the first set and the second set, determining a correlation among the brain responses that is proportional to the degree of response for the respective levels of audience behavior response; and generating the reduced-dimensional neural data having the mapping dimensionality as a time series of the correlations from each time period; generate a behavioral prediction using the predictive model, wherein the predictive model is configured to receive, as input, the reduced- dimensional neural data and to generate, as output, the behavioral prediction indicating a behavioral response to the media stimulus, and wherein the predictive model has been trained by: receiving training data representing responses of a second population to a reference media stimulus, wherein the second population includes individuals absent from the first population, and wherein the training data includes behavioral training data and neural training data corresponding to the behavior training data; reducing the dimensionality of the neural training data to the mapping dimensionality using the operations of the CorrCA; generating an estimation of the behavioral training data from an input of the neural training data having the mapping dimensionality; and tuning a set of tunable parameters of the predictive model using the estimation of the behavioral training data and the behavior training data; and predict, using the behavioral prediction, a population behavioral response to a test media stimulus. However the prior art to Georgopoulos teaches use correlated component analysis on the neural data to capture group statistics . Georgopoulo s at [0079] discloses, “Remarkably, neural networks constructed as above were very similar across subjects (FIGS. 8A and 8B, and 9A and 9B). Overall network similarity can be quantified and assessed between all subject pairs by calculating the Pearson correlation coefficient across all z ij o (i.e., all i and j sensors) of the network. The correlation coefficients obtained are high and highly significant (median=0.742; range, 0.663-0.839; P<10-20 for all correlations; >20,000 degrees of freedom). These findings suggest a common network foundation.” Use correlated component analysis on the neural data to capture group statistics is taught as calculating the Pearson correlation coefficient across all z ij o (i.e., all i and j sensors) of the network. Georgopoulos at [0086] discloses reduction in the possibility of false detections due to noise, minimum and maximum heart rates are selected to create a time window, relative to the preceding beat, in which each beat is expected. Peaks in the correlation before the start of the window are ignored, and the beat is taken at the highest peak in correlation during the window, rather than the first one that meets threshold. If no peak within the window meets threshold, then a missed beat is assumed and the window is expanded until it includes a peak that meets the threshold. Both the time window and the correlation threshold are adjusted for each subject to maximize true detections and minimize false detections.” Components having (i) reduced dimensionality relative to the neural data is taught as reducing the possibility of false detections due to noise in order to minimize false detections.) . Georgopoulos at [0086] further includes, “To reduce the possibility of false detections due to noise, minimum and maximum heart rates are selected to create a time window, relative to the preceding beat, in which each beat is expected. Peaks in the correlation before the start of the window are ignored, and the beat is taken at the highest peak in correlation during the window, rather than the first one that meets threshold. If no peak within the window meets threshold, then a missed beat is assumed and the window is expanded until it includes a peak that meets the threshold. Both the time window and the correlation threshold are adjusted for each subject to maximize true detections and minimize false detections.” In addition, “ Components having (i) reduced dimensionality relative to the neural data is taught as reducing the possibility of false detections due to noise in order to minimize false detections ” and “These features were robust across subjects” A measure of across-subject is taught as the data across subjects [0069]. Finally, the prior art to Georgopoulos discloses, “To assess the congruence of the distribution of the Group effect among sensor pairs in the 1st and 2nd sample, the presence or absence of a significant effect for a given sensor pair as 1 and 0, respectively were coded, and the X2 test statistic was computed.” The group statistics are a measure of across-subject agreement of the neural data is taught as to assess the congruence [agreement] of the distribution of the Group effect among sensor pairs [neural data].);… [0116]. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of predicting audience viewing behavior with the method of calculating the Pearson correlation coefficient of Georgopoulos in order to allow adjusting for each subject to maximize true detections and minimize false detections, thereby allowing reducing the possibility of false detections due to noise , as motivated by Georgopoulos at [0086] , including that “threshold are adjusted for each subject to maximize true detections and minimize false detections ” . With respect to claims 3 and 13 , Marci in view of Georgopoulos teaches the system and method of claim 2 and 12 , Marci further teaches wherein the processing device automatically dividing the past media broadcast into a plurality of segments (Marci at [0025] including “determined by using the engagement scores for a content stimulus by: setting at least one threshold value, dividing the engagement curve into ascending segments defined by intervals that the engagement value remains relatively constant or increases, computing the area above the threshold value for each such ascending segment, summing the areas, and dividing the sum by the duration of the content.” The processing device automatically dividing the past media broadcast into a plurality of segments is taught as dividing the engagement curves for the content into ascending segments. Refer to Paragraph [0113] for further details about past show segments used as a content stimulus.). With respect to claims 4 and 14 , Marci in view of Georgopoulos teaches the system and method of claim 3 and 13 , Marci further teaches …the neural data corresponding to each of the segments ( Marci at [0113] “a measure of engagement (engagement score) of a content stimulus (show segment, commercial, presentation, and/or similar media) may be obtained by collecting and analyzing the physiological signals of a test group while watching the content stimulus.” The neural data corresponding to each of the segments is taught as a measure of engagement of a content stimulus may be obtained by collecting and analyzing the physiological signals of a test group.)… Georgopoulos further teaches wherein the correlated component analysis further comprises selecting a respective portion of [data] at [0079] wherein, “Remarkably, neural networks constructed as above were very similar across subjects (FIGS. 8A and 8B, and 9A and 9B). Overall network similarity can be quantified and assessed between all subject pairs by calculating the Pearson correlation coefficient across all zijo (i.e., all i and j sensors) of the network. The correlation coefficients obtained are high and highly significant (median=0.742; range, 0.663-0.839; P<10-20 for all correlations; >20,000 degrees of freedom). These findings suggest a common network foundation.” Use correlated component analysis further comprises selecting a respective portion of [data] is taught as calculating the Pearson correlation coefficient across all zijo (i.e., all i and j sensors) of the network [sensor data or neural data].), determining a respective neural response reliability for each of the selected portions as in Georgopoulos at [0116] stating, “To assess the congruence of the distribution of the Group effect among sensor pairs in the 1st and 2nd sample, the presence or absence of a significant effect for a given sensor pair as 1 and 0, respectively were coded, and the X2 test statistic was computed.” The group statistics are a measure of across-subject agreement of the neural data is taught as to assess the congruence [agreement] of the distribution of the Group effect among sensor pairs [neural data]. According to the applicants specification the across-subject agreement or congruence is the same as reliability refer to Paragraph [0045].), and providing the determined neural response reliabilities as the group-representative data (Georgopoulos at [0117] “The input predictors for that analysis were zij0 values from 271 sensor pairs which showed a highly significant group effect in an ANOVA (P<0.001, F-test). This was done in an effort to reduce the large predictor space consisting of 30,628 values.” The highly significant group being provided as the group with congruence based on the sensor data reliabilities.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of predicting audience viewing behavior with the method of calculating the Pearson correlation coefficient of Georgopoulos in order to allow adjusting for each subject to maximize true detections and minimize false detections, thereby allowing reducing the possibility of false detections due to noise (Georgopoulos at [0086] “threshold are adjusted for each subject to maximize true detections and minimize false detections.”). With respect to claims 5 and 15 , Marci in view of Georgopoulos teaches the system of and method claim 4 and 14 , Marci further teaches… to the corresponding segment (Marci at [0078] wherein “The measure of engagement represents an objective measure of the experience of a defined audience segment based on a plurality of biologically based measures.” The corresponding segments are taught as an audience segment based on a plurality of biological based measures.). Georgopoulos further teaches wherein each respective determined neural response reliability indicates a consistency between the respective neurological responses of at least two subjects in the group (Georgopoulos at [0116] , wherein “To assess the congruence of the distribution of the Group effect among sensor pairs in the 1st and 2nd sample” The congruence is taught as the neural response reliability and is used to identify the agreement of the subjects. In a few examples listed by Georgopoulos, the correlation is shown across 2 samples with multiple subjects in which the subjects responses are consistent. Refer to paragraph [0113-116]) … It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of predicting audience viewing behavior with the method of calculating the Pearson correlation coefficient of Georgopoulos in order to allow adjusting for each subject to maximize true detections and minimize false detections, thereby allowing reducing the possibility of false detections due to noise (Georgopoulos at [0086] disclosing “threshold are adjusted for each subject to maximize true detections and minimize false detections.”). With respect to claims 6 and 16 , Marci in view of Georgopoulos teaches the system and method of claim 2 and 12 , wherein the predicted population behavioral response indicates a predicted action taken by a subject in response to exposure to the future media exposure (Marci at [0122] , disclosing “the negative buildup is used to predict the likelihood that an audience member will fast-forward through the content stimulus.” The predicted population behavioral response indicates a predicted action taken by a subject in response to exposure to the future media exposure is taught as predict the likelihood that an audience member will fast-forward through the content stimulus.). With respect to claims 7 and 17 , Marci in view of Georgopoulos teaches the system and method of claim 2 and 12 , Marci further teaches wherein the population from which the population behavioral data was obtained has more members than the group (Marci at [0013] teaching, “the response data can be collected individually, in a small group, or large group environment” Paragraph [0007] “biologically based responses or patterns of responses in a population sample that can lead to or are associated with behavioral responses or changes in behaviors of the target population.” The sample population is larger than the target population.). With respect to claims 8-10 and 18-20, Marci in view of Georgopoulos teaches the system and method of claim 2 and 12 , Marci further teaches …the neural data corresponding to each of the segments wherein Marci at [0113] discloses “a measure of engagement (engagement score) of a content stimulus (show segment, commercial, presentation, and/or similar media) may be obtained by collecting and analyzing the physiological signals of a test group while watching the content stimulus.” The neural data corresponding to each of the segments is taught as a measure of engagement of a content stimulus may be obtained by collecting and analyzing the physiological signals of a test group.)… It would have been further obvious to include social media data given the teachings disclosed in Marci at Figure 1 disclosing computer interface and use interactions. With respect to claims 11 and 21, Georgopoulos discloses that “c omponents having (i) reduced dimensionality relative to the neural data is taught as reducing the possibility of false detections due to noise in order to minimize false detections ” . Georgopoulos at [0086] further includes, “To reduce the possibility of false detections due to noise, minimum and maximum heart rates are selected to create a time window, relative to the preceding beat, in which each beat is expected. Peaks in the correlation before the start of the window are ignored, and the beat is taken at the highest peak in correlation during the window, rather than the first one that meets threshold. If no peak within the window meets threshold, then a missed beat is assumed and the window is expanded until it includes a peak that meets the threshold. Both the time window and the correlation threshold are adjusted for each subject to maximize true detections and minimize false detections.” Conclusion No claims are allowed. It is noted that US patent 10,835,147 and all co-pending applications to Optios, Inc and the instant inventors have been reviewed for purposes of double patenting assessment and not deemed to necessitate such at this time but may be required at a later date pending claim amendments. E-mail Communications Authorization Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting following form via EFS-Web or Central Fax (571-273-8300): PTO/SB/439 . 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