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 .
Priority
Acknowledgment is made of applicant’s claim for priority to PCT/US20/54259, filed on 10/5/2020. As such the effective filing date of claims 1-15 is 10/5/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/12/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Status
Claims 1-15 are pending.
Claims 1-15 are rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method, system, and CRM for determining a psychological state from observable manifestations of emotion/cognitive states. The judicial exception is not integrated into a practical application because while claims 1-15 attempt to integrated the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of stator subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a method (Claims 1-8), a system (9-12), and a CRM (Claims 13-15)
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, specifically mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claim 1: Processing data using a regression model is a verbal articulation of a mathematical process which is an abstract idea, specifically a mathematical concept. Indexing continuous space based on discrete labels, mapping the coordinate in the continuous space to a set of first and second discrete labels are processes of identifying, comparing/contrasting, and calculating that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 2: The individual being a participant in a video conference is merely further limiting the data itself, which is an abstract idea, specifically a mental process. Determining the first and second contexts are processes of identifying, comparing/contrasting, and calculating that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 3: The output conveying that an individual exhibits one of the specified labels is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 4: The data comprising the specified data conference is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 5: The affect comprising the specified affects conference is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 6: Mapping the coordinate using a Voronoi plot is a verbal articulation of a mathematical process which is an abstract idea, specifically a mathematical concept.
Claim 7: The first set of labels being a different language from the first is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 8: The continuous space comprising two-dimensions with the first and second axis corresponding to valences and arousal respectively is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 9: Processing a plurality of biometrics, determining a coordinate in continuous space, mapping labels onto the continuous space, selecting a subset of labels, and mapping the coordinate to a discrete label are processes of identifying, comparing/contrasting, and calculating that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 10: Generating an embedding, and determining the coordinate based on application of the embedding across a regression model are processes of identifying, comparing/contrasting, and calculating that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 11: Determining the context based on activity of the individual is a process of identifying, comparing/contrasting, and calculating that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 12: The continuous space comprising a two-dimensional space with a hedonic and activation axis is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 13: Processing sensor data using a regression model is a verbal articulation of a mathematical process which is an abstract idea, specifically a mathematical concept. Determining a coordinate in a continuous space, mapping labels to the continuous space, and identifying a first and second set of labels associated with a first and second circumstance, are processes of identifying, comparing/contrasting, and calculating that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 14: The first circumstance comprising the first set of discrete psychological labels is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 15: The first and second set of labels comprising those specified is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of nonabstract elements:
Claim 3: Rendering an output is an insignificant extra solution activity specifically necessary data outputting (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering)) [See MPEP § 2106.05(g)]. A computing device is a generic and nonspecific computer element that does not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP 2106.05(d)].
Claim 9: Rendering an output is an insignificant extra solution activity specifically necessary data outputting (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering)) [See MPEP § 2106.05(g)]. A system, processor, memory, and instructions are generic and nonspecific computer elements that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP 2106.05(d)].
Claim 10: Instructions are a generic and nonspecific computer element that does not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP 2106.05(d)].
Claim 13: A non-transitory computer readable medium, instructions, and a processor are generic and nonspecific computer elements that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP 2106.05(d)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional or nonspecific. These additional elements include:
The additional elements of rendering an output is an insignificant extra solution activity specifically necessary data outputting (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of a system, computing device, processor, memory, non-transitory computer readable medium, and instructions are generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-15, 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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-15 are rejected under 35 U.S.C. 102(a)(I) and (a)(II) as being anticipated by Coleman et al. (US 20180246570 A1).
Claim 1 is directed to a method for determining a psychological state from observable manifestations of emotion/cognitive states using regression.
Claim 9 is directed to a system for determining a psychological state from observable manifestations of emotion/cognitive states using regression.
Claim 13 is directed to a CRM for determining a psychological state from observable manifestations of emotion/cognitive states using regression.
Coleman et al. teaches in paragraph [0055] “The processing and analyzing of collected data may be implemented in computer software or hardware at the at least one client device and at least one computer server. For simplicity, this combination of hardware and software may be referred to as the processing and analyzing system platform (or “system platform”)”, reading on a method implemented using a processor, a system comprising a processor and memory storing instructions that, in response to execution of the instructions by the processor, and non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by a processor. Coleman et al. teaches in paragraph [0007] “The system may identify characteristics of a user's brain state to determine the user's cognitive or emotional state”, in paragraph [0058] “The system platform may then use the preprocessed data to extract features from the signal. For example, this may be done by projecting the EEG signal into a lower dimensioned feature space. This projection may aid in the classification of the collected data, for example, by increasing the separability of classes…The system platform may then classify the extracted features of the data. This classification may provide an indication of the mental state of the user, such as a meditative state…For example, a minimum mean distance classifier or a K nearest neighbours classifier may be employed to analyze the data”, paragraph [0128] “Determination of correct data labelling may be a critical part of estimating the optimal pipeline. The cloud methods are constructed to utilize map-reduce to parallelize estimators and estimate the most likely result based on all methods. Best estimates of class labels are fed back through system to denoise data (such as trim outliers, etc., for methods that are prone). MapReduce operations may be used to distribute signal across feature methods, and features across classifier methods”, and paragraph [0134] “methods used by the system (operating on original signal or features of the signal) may include: short time fourier transform; wavelet; AR model coefficients (auto regressive model of the signal); Non-linear complexity features such as fractal dimensions (Hilbert transform); ensemble empirical mode decomposition; signal derivatives; and regularized covariance matrix estimation (such as ledoit wolfe estimator)”, reading on processing data indicative of a measured affect of an individual using a regression model to determine a coordinate in a continuous space, wherein the continuous space is indexed based on a plurality of discrete psychological labels, process a plurality of biometrics of an individual to determine a coordinate in a continuous space, wherein a superset of discrete psychological labels is mapped onto the continuous space, and process sensor data indicative of an affect of an individual using a regression model to determine a coordinate in a continuous space, wherein a plurality of discrete psychological labels are mapped to the continuous space. Coleman et al. teaches in paragraph [0140] “The pipeline may then classify the extracted features of the data. This classification may provide an indication of the mental state of the user (e.g. a meditative state)”, in paragraph [0141] “Prediction methods may provide for: estimation of hemispheric asymmetries and thus facilitate measurements of emotional valence (positive vs. negative emotions)”, in paragraph [0142] “These prediction models are developed using machine learning methods, of which learning types may include: linear discriminant analysis; support vector machine; decision tree; K nearest neighbour; pattern discovery; Bayesian networks; and artificial neural networks”, in paragraph [0146] “app may include a communication utility 16 which is a switch that routes which sensor data is sent to the analyzer for analysis by the pipeline. It also switches which subset of the processed data is sent to the user interface”, and in paragraph [0059] “This classification may provide an indication of the mental state of the user, such as a meditative state. Various algorithms 116 can be used to classify the features determined in previous stages”, reading on in a first context, mapping the coordinate in the continuous space to one of a first set of the plurality of discrete psychological labels associated with the first context; and in a second context, mapping the coordinate in the continuous space to one of a second set of the plurality of discrete psychological labels associated with the second context, select, from the superset of discrete psychological labels, a subset of discrete psychological labels that is applicable in a given context; map the coordinate in the continuous space to a given discrete psychological label of the subset of discrete psychological labels, and under a first circumstance, identify one of a first set of the plurality of discrete psychological labels associated with the first circumstance based on the coordinate: and under a second circumstance, identify one of a second set of the plurality of discrete psychological labels associated with the second circumstance based on the coordinate. Coleman et al. teaches in paragraph [0371] “an application may be built that allows a user to see another person's brain state rendered in real time emo-graphics”, and in paragraph [0399] “The information can be displayed on a smartphone, tablet, computer or other device. The information can be presented visually in the form of a graph, table, pictorial or rating system. It could also be represented audibly. In one possible implementation the form of a sound (e.g. pitch or volume) may be changed by the system platform as measurements of brain state changes”, reading on cause a computing device to render output that is generated based on the given discrete psychological label.
Claim 2 is directed to the method of claim 1 but further specifies the individual as part of a video conferences and the determination of a first and second context based upon interactions with others in the video.
Coleman et al. teaches in paragraph [0371] “an application may be built that allows a user to see another person's brain state rendered in real time emo-graphics…when the user looks at a person with whom the user is conversing, people who are upset could have storm clouds rendered above their heads, or people who are happy could have sunshine streaming from them. People who are thinking could have the gears turning rendered above them with computer graphics. People who are relating socially to others in their proximity could show lines of interaction between themselves and the people with whom they are relating as lines of coherence between people”, and in paragraph [0189] “It may be desirable to estimate the emotional state of the user but using an input from another system. For instance, User A is talking to user B, User B is using a video camera and is streaming the live feed to the internet. The video feed is available to user A through the system platform, where video analysis can be done by cluster or grid computer. User B is pointing the camera at user A, and thus the processed video contains features of facial expression of user A as well as features related to their body language”, reading on wherein the individual is a first participant of a video conference, and the method comprises: determining the first context based on a first signal associated with a second participant of a video conference; and determining the second context based on a second signal associated with a third participant of the video conference.
Claim 3 is directed to the method of claim 1 but further specifies the rendering of an output that conveys that the individual exhibits one of the specified labels.
Coleman et al. teaches in paragraph [0371] “an application may be built that allows a user to see another person's brain state rendered in real time emo-graphics…when the user looks at a person with whom the user is conversing, people who are upset could have storm clouds rendered above their heads, or people who are happy could have sunshine streaming from them. People who are thinking could have the gears turning rendered above them with computer graphics. People who are relating socially to others in their proximity could show lines of interaction between themselves and the people with whom they are relating as lines of coherence between people”, and in paragraph [0189] “It may be desirable to estimate the emotional state of the user but using an input from another system. For instance, User A is talking to user B, User B is using a video camera and is streaming the live feed to the internet. The video feed is available to user A through the system platform, where video analysis can be done by cluster or grid computer. User B is pointing the camera at user A, and thus the processed video contains features of facial expression of user A as well as features related to their body language”, reading on further comprising: causing a first computing device operated by the second participant to render output conveying that the individual exhibits the one of the first set of the plurality of discrete psychological labels; and causing a second computing device operated by the third participant to render output conveying that the individual exhibits the one of the second set of the plurality of discrete psychological labels.
Claim 4 is directed to the method of claim 1 but further specifies that the data comprises an embedding based on biometrics.
Claim 10 is directed to the system of claim 9 but further specifies that the data comprises an embedding based on biometrics used in a regression model.
Coleman et al. teaches in paragraph [0049] “Sensors for collecting bio-signal data include, for example, electroencephalogram sensors, galvanometer sensors, or electrocardiograph sensors. For example, a wearable sensor 102 for collecting biological data, such as a commercially available consumer grade EEG headset with one or more electrodes for collecting brainwaves from the user…The one or more internal sensors 106, external sensors 104, or wearable sensors 102 may collect bio-signal or non-bio-signal data other than EEG data. For example, bio-signal data may include heart rate or blood pressure, while non-bio-signal data may include time, GPS location, barometric pressure, acceleration forces, ambient light, sound, and other data”, and paragraph [0260] “An eigen decomposition is done to get the eigenvalue (D) and eigenvectors (V)”, reading on wherein the data indicative of the affect comprises an embedding generated based on a plurality of biometrics of the individual, and comprising instructions to preprocess the plurality of biometrics to generate an embedding, wherein the coordinate is determined based on application of the embedding across a regression model.
Claim 5 is directed to the method of claim 1 but further specifies that the affect comprises the specified affects.
Coleman et al. teaches in paragraph [0099] “for basic gestures, such as tapping, the accelerometer has onboard processing that eliminates the need for streaming the inertial data, and the data needed for cognitive training related measures such as posture tracking or breath/body entrainment, fits, along with simultaneous EEG, within the iOS bandwidth limitations”, and paragraph [0189] “It may be desirable to estimate the emotional state of the user but using an input from another system. For instance, User A is talking to user B, User B is using a video camera and is streaming the live feed to the internet. The video feed is available to user A through the system platform, where video analysis can be done by cluster or grid computer. User B is pointing the camera at user A, and thus the processed video contains features of facial expression of user A as well as features related to their body language”, reading on wherein the affect comprises multiple of: a facial expression of the individual; a characteristic of a posture of the individual; or a characteristic of the individual's voice.
Claim 6 is directed to the method of claim 1 but further specifies the use of a Voronoi plot to partition the continuous space.
Coleman et al. teaches in paragraph [0142] “prediction models are developed using machine learning methods, of which learning types may include: linear discriminant analysis; support vector machine; decision tree; K nearest neighbour; pattern discovery; Bayesian networks; and artificial neural networks”, more specifically KNN decision boundaries in 2D are directly represented by Voronoi diagrams, reading on wherein mapping the coordinate in the continuous space to one of the first set of the plurality of discrete psychological labels is performed using a Voronoi plot that partitions the continuous space into regions close to each of the first set of the plurality of discrete psychological labels.
Claim 7 is directed to the method of claim 1 but further specifies that the first set of labels are in one language and the second set is in another.
Coleman et al. teaches in paragraph [0371] “an application may be built that allows a user to see another person's brain state rendered in real time emo-graphics (i.e. perhaps around the other person's head or body) to enhance social interactions over lower than real life bandwidth channels, or to enhance interactions between strangers or between people who speak different languages”, reading on wherein the first set of the plurality of discrete psychological labels are in a first language and the second set of the plurality of discrete psychological labels are in a second language that is different than the first language.
Claim 8 is directed to the method of claim 1 but further specifies the continuous space as a two-dimensional space with axis’ corresponding to valence and arousal.
Coleman et al. teaches in paragraph [0063] “This advancement may enable: estimation of hemispheric asymmetries and thus facilitate measurements of emotional valence (e.g. positive vs. negative emotions); and better signal-t-noise ratio (SNR) for global measurements and thus improved access to high-beta and gamma bands, fast oscillation EEG signals, which may be particularly important for analyzing cognitive tasks such as memory, learning, and perception. It has also been found that gamma bands are an important neural correlate of mediation expertise”, and paragraph [0245] “This message flow describes an application that uses Heart Rate Variability and facial muscle activity to determine the level of a person's emotional arousal continuously and use this information to label sections of EEG data”, reading on wherein the continuous space comprises a two-dimensional space with a first axis corresponding to valence and a second axis corresponding to arousal.
Claim 11 is directed to the system of claim 9 but further specifies the determining is based on the current activity of the individual.
Coleman et al. teaches in paragraph [0151] “A pipeline may be selected that the analyzer will apply to sensor data of a specific user. The conditions of a rule can include the context of the user (such as brain state, activity, prior goals that the user has stated, GPS coordinates, time of day, open apps on the device) to determine how to analyze the sensor data (such as which algorithm pipeline to apply to which sensor), and to determine which results to display and/or store locally on the client and which are stored in the server”, reading on wherein the given context is determined based on a current activity of the individual.
Claim 12 is directed to the system of claim 9 but further specifies that the continuous space comprises a two-dimensional space with a hedonic and activation axis.
Coleman et al. teaches in paragraph [0063] “This advancement may enable: estimation of hemispheric asymmetries and thus facilitate measurements of emotional valence (e.g. positive vs. negative emotions); and better signal-t-noise ratio (SNR) for global measurements and thus improved access to high-beta and gamma bands, fast oscillation EEG signals, which may be particularly important for analyzing cognitive tasks such as memory, learning, and perception. It has also been found that gamma bands are an important neural correlate of mediation expertise”, in paragraph [0244] “Heart Rate Variability is an important physiological measure that is related to emotional arousal, anxiety, time pressure strain and focussed attention. Heart Rate Variability only requires interbeat intervals to be analyzed which requires only bytes of data every second. In addition to EEG signals, muscle tension can be derived from EMG signals measured by forehead EEG electrodes”, in paragraph [0249] “The Machine Learning module of the system platform adjusts the thresholds based on the user's information. The user's heart rate and EMG features are used to determine if the user is under low, medium or high stress”, and paragraph [0404] “graphic tools may be provided to aid human assisted pattern recognition of brain states. This aspect of the invention may involve the presentation of the accumulation of two-dimensional feature space data that is the result of real-time feature (spectral or other) extraction on a user's brainwave data. The user may monitor the user's own brain-state in an open meditation (mindfulness) style, and simultaneously observe the graphical representation to find correlations between the user's brain-state or brain-state dynamics with the two-dimensional feature subspace presented. The graphic tools may include settings for time lag presentation so that observations of the user's internal state and observation of the feature space can be serialized. Tunable temporal band-pass filtering of the feature space may be provided, which a user may configure using settings, in order to aid an experimenter in comparing state dynamics”, reading on wherein the continuous space comprises a two-dimensional space with a hedonic axis and an activation axis.
Claim 14 is directed to the CRM of claim 13 but further specifies that the first circumstance comprises the first set of labels being active.
Coleman et al. teaches in paragraph [0059] “The system platform may then classify the extracted features of the data. This classification may provide an indication of the mental state of the user, such as a meditative state. Various algorithms 116 can be used to classify the features determined in previous stages”, and paragraph [0371] “an application may be built that allows a user to see another person's brain state rendered in real time emo-graphics (i.e. perhaps around the other person's head or body) to enhance social interactions over lower than real life bandwidth channels, or to enhance interactions between strangers or between people who speak different languages”, reading on wherein the first circumstance comprises the first set of the discrete psychological labels being active based on user operation of an input device.
Claim 15 is directed to the CRM of claim 13 but further specifies that the first set of emotions and the second set of emotions are incongruent and the second set are expected under the second circumstance.
Coleman et al. teaches in paragraph [0063] “This advancement may enable: estimation of hemispheric asymmetries and thus facilitate measurements of emotional valence (e.g. positive vs. negative emotions); and better signal-t-noise ratio (SNR) for global measurements and thus improved access to high-beta and gamma bands, fast oscillation EEG signals, which may be particularly important for analyzing cognitive tasks such as memory, learning, and perception. It has also been found that gamma bands are an important neural correlate of mediation expertise”, in paragraph [392]-[0393] “In this possible computer system implementation, truck drivers wear headsets that record their level of fatigue. The data from these headsets is sent through the cloud to the dispatch where dispatchers can monitor each driver and make sure that they are still able to drive safely…In this possible computer system implementation, workers in a factory wear headsets that measure their level of attention while performing sensitive work or potentially dangerous work. If a worker's focus drops while working on a particular piece of equipment for example, that equipment can then be marked to receive extra-attention in the quality-assurance process because of the increased likelihood of error”, and in paragraph [0237] “users of a particular application linked to the system (such as a meditation training application) may be classified based on a range of values associated with their experience of certain meditation induced states. A representative profile may therefore include an identifier that is associated with a state classification profile that best matches the individual user. This system may detect that over time the user's interactions with the application are changing (for example because the user's medication training efforts have been effective). This may be detected by the system, and the system may dynamically adjust the user's profile. Also, the machine learning system may detect and generate insights regarding improving the classification of individuals based on new understanding of interaction of users associated with particular attributes with selected functions of an application, and the creation and use of profiles by the system may be automatically adjusted based on these insights”, reading on wherein the first set of the plurality of discrete psychological labels comprises a first set of emotions that are expected to be observed under the first circumstance, and the second set of the plurality of discrete psychological labels comprises a second set of emotions that is incongruent with the first set of motions, and that are expected to be observed under the second circumstance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEENAN NEIL ANDERSON-FEARS whose telephone number is (571)272-0108. The examiner can normally be reached M-Th, alternate F, 8-5.
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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