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 .
Response to Arguments
Applicant’s arguments, see “Applicant Arguments/Remarks”, filed 11/12/2025, with respect to rejections under U.S.C. 112(b) have been fully considered and are persuasive. The rejections under U.S.C. 112(b) have been withdrawn.
Applicant's arguments filed 11/12/2025 regarding the rejections under U.S.C. 101 and 102 have been fully considered but they are not persuasive.
Applicant’s first argument, that the amended subject matter amounts to an improvement to the collection of traffic data as in Example 40 of the 2019 PEG, is unpersuasive. Regarding the potential similarities to Example 40, Applicant’s claims are missing the interconnected step found in Example 40. In Example 40, the second system, the Netflow analysis, runs only if the first system, the quality tracking, identifies that the quality of the signal is damaged. Therefore, the Netflow analysis will never run if the quality tracking maintains that the signal is high-quality. This step is missing, as the second algorithm of Applicant’s high-power computing device runs alongside/regardless of the data collected from the wearable/processing device. Further, assigning different types of processing to different computers/processors based on the capabilities of the processor is routine and conventional, as shown in Para. 0107 of Yocca, the cited 102 art.
Applicant’s arguments to the rejection under U.S.C. 102 and amendments to the claims are likewise unpersuasive. Yocca teaches the usage of a wearable device (Para. 0043) monitoring a non-neural physiological condition of the user (Para. 0010, “heart rate variability”, or Para. 0089, “skin conductance”) for real-time monitoring/assessment (Para. 0171).
For these reasons, the rejections are maintained, and updated below to address the newly amended limitations.
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 1-22 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1: Claim 1 is directed to a system, and thus is directed to a statutory category of invention.
Step 2A, Prong 1:
Claim 1 recites the following claim limitation:
output sensed data indicative of the continuously variable non-neural physiological condition
run the first algorithm to determine a real-time estimate of a state of a nervous system based on the sensed data
run the second algorithm to determine updates to improve the real-time estimate of the state of the nervous system
These limitations, under their broadest reasonable interpretation, cover concepts that can be practically performed in the human mind, i.e. using pen and paper, and using mathematical concepts. A person is capable of receiving sensor data and running the data through algorithmic calculations on pen and paper to make a determination about a health condition, and using a second algorithm to derive data to improve the second. Thus, the claims recite limitations that fall within the ‘mental processes’ and ‘mathematical concepts’ grouping of abstract ideas.
Step 2A, Prong 2:
Claim 1 recites the following additional elements:
at least one physiological sensor configured to sense a continuously variable non-neural physiological condition
a processing device having a first computation power and configured to receive the sensed data from the at least one physiological sensor, the processing device including a processor and memory storing a first algorithm, wherein the processing device is configured to output the real-time estimate of the state of the nervous system;
and a computing device having a second computation power greater than the first computation power, the computing device operably coupled to processing device of the wearable device and including a processor and memory storing a second algorithm that, when executed by the processor, causes the processor to run the second algorithm to determine periodic updates, the computing device configured to communicate the periodic updates to the processing device of the wearable device to improve subsequent determinations of the real-time estimate of the state of the nervous system of the user.
Electronically receiving a plurality of measurements over an unspecified time span is merely insignificant pre-solution activity (See MPEP 2106.05(g)).
Claim 1's recitation of one or more processors and computer storage media with computer-usable instructions that, when executed by the one or more processors, implement an algorithmic method are merely reciting both the processors and computer storage media at a high-level of generality, and the computer readable storage media merely instructs the processors to carry out the steps of the method. The usage of the algorithmic steps is recited broadly and generically, with no indication that the algorithmic steps are incapable of being performed abstractly/on pen and paper. In other words, the computer components are being used as a tool to carry out the method (See MPEP 2106.05(f)).
Thus, the abstract idea is not integrated into a practical application. The combination of these additional elements is no more than insignificant extra solution activity, and mere instructions to apply the exception using generic computer components (the processors and computer readable storage media). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than insignificant extra solution activity and mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept.
The differing computational powers of the processing and the computing device is merely routine and conventional network construction, as taught in Yocca in Para. 0171, as it is routine to choose the level of computational power based on the needs of the particular device.
For the "electrically receiving..." step that was considered insignificant extra-solution activity in Step 2A Prong Two, it has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The following evidence supports such a determination:
electronically receiving a plurality of measurements of physiological variables for a patient, the plurality of measurements being acquired over a time span
(See MPEP 2106.05(d) II. 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
For these reasons, there is no inventive concept. The claim is not patent eligible. Even when viewed as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent Claims:
Claims 2-5 and 19-21 recite limitations that further define the type of data collected and number of sensors, and are merely limiting the physiological variables to particular fields of use.
Claims 6-18 further limits the abstract idea and mathematical concepts with limitations practically performable in the human mind (specifying basic algorithmic steps, weighing data, comparing data).
Claim 22 recites a Markush group, wherein the system of Claim 1 has either an output device as described, or a therapy device. For the former, broadly reciting an output device and an output is nothing more than extra-solution activity that does not amount to significantly more. Regarding the therapy device, the therapy is not specified in the Claim, and likewise (in view of the breadth) merely extra-solution activity. The Examiner notes that merely specifying the type of therapy is likewise not inherently capable of overcoming this rejection, as Para. 0080 specifies various types therapies, including some that amount to mere extra-solution outputs (visual/audio therapy).
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 1-3, 6, 8, and 16-22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Publication 20220202373 awarded to Yocca et al, hereinafter Yocca.
Regarding Claim 1, Yocca teaches a system for estimating a state of the nervous system (abstract), comprising: a wearable device configured to be worn by or attached to a user (Para. 0043), the wearable device including: at least one physiological sensor configured to sense a continuously variable non-neural physiological condition of the user (Para. 0089, “The present disclosure thus provides the monitoring of the emergence of agitation by identifying increased sympathetic nervous system activity from physiological signals such as changes in Electrodermal activity (skin conductance response)”) the wearable device configured to output sensed data indicative of the continuously variable non-neural physiological condition (Para. 0101); a processing device having a first computation power and configured to receive the sensed data from the at least one physiological sensor (local server, Para. 0106, “Reference is made to a system disclosed in FIG. 1 of the present disclosure. As depicted, a subject predisposed to agitation wears a wearable device for collecting data related to such as and not limited to sympathetic nervous system activity. The data collected by the wearable device are transmitted to at least a local server (e.g., via a network). In a network deployment, the local server in a non-limiting manner may comprise a server computer, a personal computer (PC), a tablet PC, a laptop computer, a desktop computer, a control system, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by the local server. The local server includes a processor (not shown) and a memory (not shown) operatively coupled to the processor”) storing a first algorithm that, when executed by the processor, causes the processor to run the first algorithm to determine a real-time (Para. 0171) estimate of a state of a nervous system based on the sensed data (Para. 0142, “The said processor is configured to receive, from a first monitoring device (8001) attached to a subject, physiological data of sympathetic nervous system activity in the subject. The first monitoring device (8001) is an automated monitoring device. The processor is capable of analyzing the physiological data to detect an anomaly from a reference pattern of sympathetic nervous system activity to determine a probability of an occurrence of an agitation episode of the subject. For the purpose, the processor executes at least one machine learning model”), the processing device configured to output the real-time estimate of the state of the nervous system (Para. 0142, “The processor (802) is further capable of sending a signal to a second monitoring device (8002) to notify the second monitoring device of the probability of the occurrence of the agitation episode of the subject such that treatment can be provided to decrease sympathetic nervous system activity in the subject”); and a computing device having a second computation power greater than the first computation power, the computing device operably coupled to processing device of the wearable device and including a processor and memory storing a second algorithm that, when executed by the processor, causes the processor to run the second algorithm to determine periodic updates, the computing device configured to communicate the periodic updates to the processing device of the wearable device to improve subsequent determinations of the real-time estimate of the state of the nervous system of the user (Para. 0108, “With the collected data, an Ecological Momentary Assessment (EMA) is conducted and a report is generated by a processing unit of the system (e.g., a processor in the network server, or a processor 802 shown in FIG. 8.) For EMA data is collected from the subject. EMA also includes providing prompts to the subject, patches and updates as well. The obtained and stored data at the network server is used for training purpose to effectively monitor and predict an episode of impending agitation. The processing unit (e.g., a processor in the network server, or a processor 802 shown in FIG. 8) is configured to diagnose an impending agitation episode in a subject and to send a signal to a compatible device monitored by, for example, a caregiver alerting the caregiver about an impending agitation episode in the subject. The signal can also be sent to a remote compatible device (not shown in FIG. 1) monitored by a caregiver alerting the caregiver to an impending agitation episode in the subject. The compatible device monitored by, for example, a caregiver is also referred to as the second monitoring device 8002 in FIG. 8”).
Regarding Claim 2, Yocca teaches the system according to claim 1, wherein the at least one physiological sensor includes at least one skin conductance sensor and wherein the continuously variable non-neural physiological condition is skin conductance (Para. 0188, “Physiological signals can include, for example, change in skin conductance (GSR)”).
Regarding Claim 3, Yocca teaches the system according to claim 2, wherein the real-time estimate of the state of the nervous system is at least one of: an identification of an autonomic nervous system activation (Para. 0405) or an estimate of a state of sympathetic arousal (Para. 0188).
Regarding Claim 6, Yocca teaches the system according to claim 1, wherein the processing device is further configured to receive an external input and wherein the processor is caused to determine the real-time estimate of the state of the nervous system based on the sensed data and the external input (Para. 0108, “With the collected data, an Ecological Momentary Assessment (EMA) is conducted and a report is generated by a processing unit of the system (e.g., a processor in the network server, or a processor 802 shown in FIG. 8.) For EMA data is collected from the subject. EMA also includes providing prompts to the subject, patches and updates as well. The obtained and stored data at the network server is used for training purpose to effectively monitor and predict an episode of impending agitation. The processing unit (e.g., a processor in the network server, or a processor 802 shown in FIG. 8) is configured to diagnose an impending agitation episode in a subject and to send a signal to a compatible device monitored by, for example, a caregiver alerting the caregiver about an impending agitation episode in the subject. The signal can also be sent to a remote compatible device (not shown in FIG. 1) monitored by a caregiver alerting the caregiver to an impending agitation episode in the subject. The compatible device monitored by, for example, a caregiver is also referred to as the second monitoring device 8002 in FIG. 8”).
Regarding Claim 8, Yocca teaches the system according to claim 1, wherein both the first and second algorithms perform estimation (Para. 0111, “The early warning algorithm is based on machine learning. In an implementation of the disclosure is included an early warning module (included in the network server (4), or included in the memory 801 of the apparatus 800 and executable by the processor 802 in FIG. 8) implementing the said algorithm. In some implementations, the early warning module can also be included in the wearable device or the data collection module. In other words, the training of the machine learning model and the predicting/analyzing using the machine learning model can be performed by the network server, the local server, the wearable device, and/or the data collection module”) and, wherein, the second algorithm is utilized to provide updated parameters to the first algorithm for determining the real-time estimate of the state of the nervous system based on the sensed data using the first algorithm (Para. 0108, “The data collection module is configured to communicate with the network server and the local server for transmission of the collected data. With the collected data, an Ecological Momentary Assessment (EMA) is conducted and a report is generated by a processing unit of the system (e.g., a processor in the network server, or a processor 802 shown in FIG. 8.) For EMA data is collected from the subject. EMA also includes providing prompts to the subject, patches and updates as well. The obtained and stored data at the network server is used for training purpose to effectively monitor and predict an episode of impending agitation”).
Regarding Claim 16, Yocca teaches the system according to Claim 1, wherein the first algorithm includes at least one neural network (Para. 0144 states that instances labeled as machine learning could be neural networks).
Regarding Claim 17, Yocca teaches the system according to claim 16, wherein a first neural network of the at least one neural network is configured to model how the estimated nervous system state at least one of: evolves with time or relates to observations (Para. 0037, “(b) establishing a baseline value of at least one physiological parameter by training at least one machine learning model using the first physiological data”), and wherein a second neural network of the at least one neural network is configured to estimate the nervous system state (Para. 0143, “The processor (802) is configured to receive an indication associated with the agitation episode after sending the signal to the second monitoring device and further train the at least one machine learning model based on the indication. The said indication indicates one of (1) whether or not the agitation episode occurs, (2) when the agitation episode occurs, (3) a degree of the agitation episode, (4) a time period for which the agitation episode lasts, or (5) a symptom of the agitation episode”).
Regarding Claim 18, Yocca teaches the system according to claim 16, wherein the computing device is configured to re-train the at least one neural network and wherein updated neural network weights are included in the updates provided from the computing device to the processing device (Para. 0145, “Each physiological parameter from the plurality of physiological parameters is associated with a weight from a plurality of weights of the mathematical model (e.g., machine learning model). The medium includes code to cause the processor to determine the reference pattern of at least one physiological parameter from the plurality of physiological parameters based on the at least one mathematical model (e.g., machine learning model). The code includes code to cause the processor to receive an indication associated with the agitation episode after sending the signal to the second monitoring device and thus train the at least one mathematical model (e.g., machine learning model) to adjust the reference pattern of the at least one physiological parameter and a weight associated with the at least one physiological parameter”).
Regarding Claim 19, Yocca teaches the system according to claim 1, further comprising: at least one second physiological sensor of the wearable device or connected to the wearable device, the at least one second physiological sensor configured to sense a second continuously variable non-neural physiological condition and to output second sensed data indicative of the second continuously variable non-neural physiological condition, wherein the first algorithm is configured to estimate the state of the nervous system based on the sensed data and the second sensed data (Para. 0385 or Para. 0406, “SCR [Skin Conductance Response] will be recorded using the Biopac MP150 system, using 11-mm inner diameter Ag/AgCl electrodes filled with isotonic electrode paste. The electrodes will be attached to the middle phalanges of the fourth and fifth fingers of the non-dominant hand. SCR waveforms will be analyzed with Acknowledge software or MATLAB, with base-to-peak difference assessed for the largest deflection in the window one to four seconds following stimulus onset”).
Regarding Claim 20, Yocca teaches the system according to claim 19, wherein the first and second continuously variable non- neural physiological conditions are skin conductance at different bodily locations (Para. 0406, “SCR [Skin Conductance Response] will be recorded using the Biopac MP150 system, using 11-mm inner diameter Ag/AgCl electrodes filled with isotonic electrode paste. The electrodes will be attached to the middle phalanges of the fourth and fifth fingers of the non-dominant hand. SCR waveforms will be analyzed with Acknowledge software or MATLAB, with base-to-peak difference assessed for the largest deflection in the window one to four seconds following stimulus onset).
Regarding Claim 21, Yocca teaches the system according to claim 19, wherein the first continuously variable non-neural physiological condition is skin conductance and wherein the second continuously variable non- neural physiological condition is heart rate (Para. 0385).
Regarding Claim 22, Yocca teaches a control system, comprising: the system according to claim 1 (see rejection to Claim 1); and at least one of an output device configured to receive the real-time estimate of the state of the nervous system output from the processing device and to provide an output based thereon (Para. 0192, “(c) sending a signal from the device to a remote compatible device monitored by a caregiver alerting the caregiver to an impending agitation episode in the subject”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4-5, 9-10, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 20220202373 awarded to Yocca et al, hereinafter Yocca, in view of WO Publication 20161108804 awarded to Burton, hereinafter Burton.
Regarding Claims 4-5, Yocca teaches the system of Claim 1. Yocca does not teach wherein the at least one sensor includes at least one blood cortisol sensor and wherein the continuously variable non-neural physiological condition is a user's blood cortisol level, wherein the estimate of the state of the nervous system is an estimate of a state of cortisol-related energy production. Yocca does teach monitoring cortisol (Para. 0507), and separately a need to monitor a person’s sleep/energy levels to monitor agitation levels (Para. 0100).
However, in the art of patient monitoring, Burton teaches monitoring blood cortisol (“Point of care body fluid measures such as melatonin (i.e. the rhythm of the melatonin concentration provides an optimal circadian phase marker for humans) or Cortisol (i.e. measures via periodic testing of samples of saliva, blood/plasma or urine or associated blood/plasma, saliva or urine sampling test trips and related point of care testing systems, with option of automatic integration or interface with mobile health monitoring or tracking systems);”) for the purposes of detecting the energy levels of a subject (“Block 1 incorporates inputs including new and current environments time-zones and/or solar daylight conditions, temperature changes, information on population studies or health information such as typical sleep deprivation or sleep urge/propensity associated with various degrees of circadian clock asynchrony (including incorporation as part of self-learning algorithm processes (i.e.. but not limited to self-learning processes per examples .);”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yocca by Burton, i.e by adding cortisol monitoring to the system of Yocca as in Burton, for the predictable purpose of combining known prior art elements to improve the similar device of Yocca in the same way the device of Burton.
Regarding Claims 9-10 and 15, Yocca teaches the system of Claim 1. Yocca does not teach wherein the first algorithm includes a forward filter algorithm and wherein the second algorithm includes the forward filter algorithm and a backward smoothing algorithm, wherein the computing device is configured to repeatedly run the forward filter and backward smoothing algorithms to obtain updated model parameters, and wherein the updated model parameters are included in the updates provided from the computing device to the processing device, or wherein at least one of the first algorithm or the second algorithm is based on a decomposition model wherein the sensed data is decomposed into a tonic component, a phasic component, and a noise component.
However, in the art of patient monitoring, Burton teaches the usage of forward filters (“Using this forward equation source reconstruction analysis approach the present invention can determine optimal EEG signal processing (i.e. adapting frequency, phase and/or amplitude of sensor signals) applicable to sensor monitoring system's specific electrode location”) and backward smoothing to update model parameters (“Frequency bands of interest can be derived by way of 8-order Butterworth forward-backward MR filters (avoid phase delays and improve frequency selectivity); .sup.80. Data acquisition sampling rate s should preferably be greater than 2000 samples per second and signal to noise monitoring capabilities should be capable of distinguishing the smallest possible HFOs, which can be as few of a few microvolts amplitude or less”) and wherein an algorithm uses a phasic component decomposition model (“The present invention provides broad-based analysis types and variants comprising any of or any combination of stochastic and/or stationary and/or linear and/or deterministic and/or non-stationary and/or non-linear dynamic analysis variants of wavelet decomposition analysis”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yocca by Burton, i.e. by using the algorithms of Burton in the system of Yocca, for the predictable purpose of simply substituting one known way of algorithmically processing for another.
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 20220202373 awarded to Yocca et al, hereinafter Yocca, in view of NPL to Wickramasuriya et al, “A State-Space Approach for Detecting Stress from Electrodermal Activity”, In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) July 18, 2018, pp. 3562-3567, hereinafter Wickramasuriya, cited on Applicant’s IDS dated 01/31/2022.
Regarding Claims 11-13, Yocca teaches the system according to Claim 1. Yocca does not teach wherein the first algorithm includes a sparse recovery algorithm and wherein the second algorithm includes a sparse recovery algorithm and a further estimation algorithm, wherein the further estimation algorithm includes an expectation maximization algorithm or a coordinate descent algorithm, and wherein the sparse recovery algorithm includes a least squares algorithm or a Bayesian filter algorithm.
However, in the art of stress monitoring, Wickramasuriya teaches the usage of interacting expectation maximization and Bayesian filter algorithms to monitor combined heart rate and skin conductance data (see the “I. Introduction” section).
It would have been obvious to one of ordinary skill in the art before the effective fling date of the claimed invention to modify Yocca by Wickramasuriya, i.e. by using the algorithmic system of Wickramasuriya to replace the algorithmic systems of Yocca, for the predictable purpose of simply substituting one known set of algorithmic processing for another.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 20220202373 awarded to Yocca et al, hereinafter Yocca, in view attached NPL awarded to Benedek et al (Psychophysiology, 2010, Vol. 47, pp647-658), hereinafter Benedek.
Regarding Claim 14, Yocca teaches the system of Claim 1. Yocca does not teach wherein at least one of the first algorithm or the second algorithm is based on a poral valve model.
However, in the art of skin conductance processing, Benedek teaches the usage of a poral valve model to process skin conductance (see the fifth paragraph of “The Shape of the SCR” section).
It would have been obvious to one of ordinary skill in the art before the effective fling date of the claimed invention to modify Yocca by Benedek, i.e. by using the using the poral valve model of Benedek to process the SCR data of Yocca, for the predictable purpose of simply substituting one known set of algorithmic processing for another.
Conclusion
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.
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/JLM/
Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792