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 filed 12/31/2025 have been fully considered but are not persuasive or are moot in view of new grounds of rejection.
Applicant argues, “first, claim 11 as amended does not recite a mental process. Claim 11 is directed towards an electrodermal activity (EDA) sensor including a set of electrodes, an analog front end (AFE), and a processor that uses unsupervised machine learning to identify and automatically remove, in real time, artifacts from EDA data collected by the set of electrodes. Claim 11 as amended specifies that the processor is configured to divide the EDA data into segments, extract respective feature vectors from the segments of the EDA data, identify artifacts in the segments of the EDA data based on the respective feature vector, and automatically remove the artifacts from the EDA data in real time using unsupervised machine learning. These steps involve a several-step manipulation of the EDA data and thus cannot practically be performed in the human mind.1 Put differently, the human mind is not equipped to divide EDA data into segments, extract feature vectors from these segments, identify artifacts in these segments, and then automatically remove the artifacts in real time.”
Examiner respectfully disagrees.
Claim 11 recites the following claim limitations:
…configured to identify and remove artifacts from the EDA data collected by the first electrode and second electrode by (mental process – person can use observation, judgement, and evaluation to identify and remove artifacts from collected data):
dividing the EDA data into segments, each segment having a duration corresponding to a timescale of the artifacts (mental process – person can divide EDA data into segments);
extracting respective feature vectors from the segments of the EDA data (mental process – person can extract feature vectors from the EDA data);
identifying, using unsupervised machine learning, the artifacts in the segments of the EDA data based on the respective feature vector (mental process – person can identify artifacts in segments of the EDA data).
The above recited limitation, under broadest reasonable interpretation, covers concepts that can be practically performed in the human mind or with pen and paper (i.e. mental processes),
Further, the limitation, “automatically removing the artifacts from the EDA data to yield corrected EDA data in real time using unsupervised machine learning” is directed toward a mathematical concept, specifically, mathematical relationships and calculations, as also evidenced by Applicant’s specification, which discloses “In step 312, the raw data is processed with three unsupervised machine learning methods. The three unsupervised machine learning methods are isolation forest, KNN distance, and 1-class SVM. Each of the unsupervised machine learning methods analyzes the raw EDA data in short chunks or windows (e.g., of 0.25, 0.5, or 0.75 seconds each) of data, and assigns a score to every window, where a higher score is more likely to indicate an artifact” [0052]).
Further, for the sake of argument, Examiner asserts that even if the recited limitations were not interpreted as a mathematical concept, they would be interpreted as additional elements, and would still not integrate the abstract idea into a practical application, because they would be directed towards using a generic computer element to mimic how the human mind thinks and analyzes problems.
Further, Applicant’s specification discloses “The EDA sensor 120 identifies and removes artifacts caused by surgical cautery, movement, etc. from the clinical EDA data using unsupervised machine learning methods, also called unsupervised learning, conducted by the processor 124” [0051].
Thus, the above recited limitations do not integrate the abstract idea into a practical idea, and is merely using generic computer components to mimic the human mind.
Further, claim 11's recitation of a processor is merely reciting the processor at a high-level of generality. In other words, the computer components are being used as a tool to carry out the system’s functions (See MPEP 2106.05(f)).
Applicant argues, “second, claim 11 as amended recites additional elements that integrate any allegedly abstract ideas into the practical application of using EDA data in a clinical setting.”
Examiner respectfully disagrees. As stated, above, there is nothing in claim 11 which integrates the judicial exception into a practical application.
Applicant argues, “Faul and Liu are non-analogous art. To support an obviousness rejection, a reference must be analogous to the claimed invention; that is, the reference must be (1) "from the same field of endeavor as the claimed invention"; or (2) "reasonably pertinent to the problem faced by the inventor." Faul is in the field of "real-time identification of seizures in an [e]lectroencephalogram (EEG) signal""and does not contemplate electrodermal activity (EDA) data. EEG and EDA data are different.”
In response to applicant's argument that Faul and Liu are nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
In this case, Faul is directed to real-time identification of seizures in an Electroencephalogram (EEG) signal (Faul: [0001]), which is the same field of the inventor’s endeavor (Taratorin: [0035]: method aims at improving accuracy of galvanic skin response (GSR) measurement for real-time detection and processing). It is not required that Faul is directed toward measuring galvanic skin response (GSR) signals. Further Faul and Taratorin are both considered with the same particular problem of providing real-time detection (see above).
In regards to Liu, it is also in the same field of the inventor’s endeavor, since Liu is directed toward optimizing glucose states of a user (Liu: [0002]). Specifically, Liu is also directed toward measuring health signals, similar to Taratorin. Further, Liu is also pertinent to the particular problem with which the inventor was concerned, since Liu aims at providing real-time outputs and feedback (Liu: [0123]).
Thus, Faul and Liu are both analogous art.
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 11-16 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, specifically an abstract idea without significantly more.
Step 1:
Independent claim 11 is directed to an electrodermal activity (EDA) sensor. Thus, it is directed to statutory categories of invention.
Step 2A, Prong 1:
Claim 11 recites the following claim limitation:
…configured to identify and remove artifacts from the EDA data collected by the first electrode and second electrode by (mental process – person can use observation, judgement, and evaluation to identify and remove artifacts from collected data):
dividing the EDA data into segments, each segment having a duration corresponding to a timescale of the artifacts (mental process – person can divide EDA data into segments);
extracting respective feature vectors from the segments of the EDA data (mental process – person can extract feature vectors from the EDA data);
identifying, using unsupervised machine learning, the artifacts in the segments of the EDA data based on the respective feature vector (mental process – person can identify artifacts in segments of the EDA data); and
automatically removing the artifacts from the EDA data to yield corrected EDA data in real time using unsupervised machine learning (mathematical concept – comprises mathematical relationships and calculations Applicant’s specification discloses: “In step 312, the raw data is processed with three unsupervised machine learning methods. The three unsupervised machine learning methods are isolation forest, KNN distance, and 1-class SVM. Each of the unsupervised machine learning methods analyzes the raw EDA data in short chunks or windows (e.g., of 0.25, 0.5, or 0.75 seconds each) of data, and assigns a score to every window, where a higher score is more likely to indicate an artifact” [0052]);
The above recited limitation, under broadest reasonable interpretation, covers concepts that can be practically performed in the human mind or with pen and paper (i.e. mental processes) as well as mathematical concepts (see MPEP § 2106.04(a)(2)).
Step 2A, Prong 2:
Claim 11 recites the following additional elements:
a first electrode configured to electrically couple with a first portion of skin of a person;
a second electrode electrically coupled to the first electrode and configured to electrically couple with a second portion of skin of the person;
an analog front end (AFE), operably coupled to the first electrode and the second electrode, to receive and condition EDA data collected by the first electrode and the second electrode;
a processor,
display, operably coupled to the processor, to display the corrected EDA data in real time.
Regarding the limitations,
“a first electrode configured to electrically couple with a first portion of skin of a person;
a second electrode electrically coupled to the first electrode and configured to electrically couple with a second portion of skin of the person;
an analog front end (AFE), operably coupled to the first electrode and the second electrode, to receive and condition EDA data collected by the first electrode and the second electrode”,
the recited limitations are insignificant extra solution activities. Specifically, the limitations directed to receiving and conditioning EDA data collected by the first electrode and the second electrode are directed towards pre-solution activity since they collect information that will later be used yield corrected EDA data (i.e. mere data gathering and organizing).
Additionally, regarding the limitations
a display, operably coupled to the processor, to display the corrected EDA data in real time”,
Examiner asserts that these limitations are directed to additional elements, specifically insignificant post solution activity (see MPEP 2106.05(g)).
The above recited limitations merely process information and then output the results of the above identified abstract ideas. Additionally, the recited “display” is neither particular enough to meaningfully limit the recited exception nor does it have more than a nominal relationship to the exception. In other words, the breadth of the recited “display” is such that it substantially encompasses all applications of the recited exception (such as moving information around).
There is nothing in the claims which show how displaying the corrected EDA data integrates the judicial exception into a practical application.
Further, there is no evidence of record that would support the assertion that this step is an improvement to a computer or a technological solution to a technological problem.
Additionally, although the limitations, “automatically removing the artifacts from the EDA data to yield corrected EDA data in real time using unsupervised machine learning” are directed to mathematical concepts (see above), Examiner asserts that even if they were interpreted as additional elements, they still would not integrate the abstract idea into a practical application, because they would be directed towards using a generic computer element to mimic how the human mind thinks and analyzes problems.
Further, Applicant’s specification discloses “The EDA sensor 120 identifies and removes artifacts caused by surgical cautery, movement, etc. from the clinical EDA data using unsupervised machine learning methods, also called unsupervised learning, conducted by the processor 124” [0051].
Thus, the above recited limitations do not integrate the abstract idea into a practical idea, and is merely using generic computer components to mimic the human mind.
Claim 11's recitation of a processor is merely reciting the processor at a high-level of generality. In other words, the computer components are being used as a tool to carry out the system’s functions (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 pre-solution activity, insignificant extra solution activity, and mere instructions to apply the abstract idea on a generic computer component. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
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 2 above, the additional elements in the claim amount to no more than insignificant extra solution activity and applying the exception in a general way, as well as establishing an environment for which data is gathered.
Additionally, regarding the electrodes, Applicant’s specification discloses that the electrodes may be Ag/AgCl electrodes [0042], and that they may contain hook-and-loop fasteners [0042] or buckles [0042] as fasteners to secure the electrodes to skin [0042].
Regarding the limitation directed toward the “analog front end (AFE)”, the specifications disclose “AFE 122 receives the analog EDA data of the fluctuations in current across the patch of skin and conditions the EDA data for further processing, for example, by converting the EDA data from the analog domain to the digital domain with an analog-to-digital converter” [0043]. Additionally, Applicant’s specification [0043] discloses “the AFE 122 supplies a source voltage (e.g., about 0.2 V to about 2.5 V DC) to the electrodes 130 to induce a small current across a patch of skin between the electrodes 130, and measures small current fluctuations across the patch of skin, where the fluctuations indicate changes in epidermal conductivity. The AFE 122 receives the analog EDA data of the fluctuations in current across the patch of skin and conditions the EDA data for further processing, for example, by converting the EDA data from the analog domain to the digital domain with an analog-to-digital converter.”
Regarding the AFE supplying voltage to the electrodes and comprising an analog-to-digital converter (as disclosed in Applicant’s specification above), see the below references that disclose similar structure:
Bar-Or et al. (US 2014/0110273) discloses measuring oxidation-reduction potential [0056] using a test strip [0057] comprising of electrodes [0057], and wherein an analog front end [0058] generates a voltage signal [0058] between a reference electrode [0058] and a working electrode [0058], and then a measured voltage signal that represents a potential difference between the two electrodes [0058] is converted from an analog signal [0058] to a digital signal [0058] via an analog to digital converter [0058].
Kimoto et al. (US 10,555,686) discloses a method for measuring impedance of body tissue (Col. 2, lines 6-21) comprising an analog front end (fig. 3: 74) that generates a voltage signal (Col. 9, lines 37 – 41: 74 supplies sinusoidal drive current to first and second drive current electrodes which are sensed) to electrodes (Col. 9, lines 37 – 41), wherein the resulting voltage levels are supplied to an analog to digital unit (76; Col. 9, lines 37-46) which converts analog voltage signal to a digital signal (Col. 9, lines 37-46).
Regarding the limitation directed toward “unsupervised machine learning”, Applicant’s specifications disclose “…unsupervised (machine) learning methods, including isolation forest, K-nearest neighbor distance, and 1-class support vector machine (SVM)” [0009].
Regarding the electrodes and them coupling to a portion of skin using hook-and-loop fasteners or buckles (as disclosed in Applicant’s specification above), see the below reference that discloses similar structure:
Finch (US 4,848,351) discloses electrodes (Col. 2, lines 13-19) comprising hook fasteners (Col. 2, lines 13-19) for securing the electrodes to a patient’s skin (Col. 2, lines 13-20), and wherein the electrodes may be Ag/AgCl electrodes (Col. 2, lines 35-48).
Sankai (US 2011/0166491) discloses a biological signal measuring wearing device [0008] comprising of electrode parts (fig. 6: both 12) that come into close contact with a skin of a wearer (1) through the use of attachment parts (51; [0103]) that each comprise one or more of “Magic Tape (registered trademark) (a hook-and-loop fastener), a zipper (fastener), a snap fastener, an eyehook, a button, magnets, double-sided tape, etc” [0103].
Therefore, the limitations directed to the AFE supplying voltage to the electrodes and comprising an analog-to-digital converter, as well as the limitations directed to the electrode fasteners coupling to a portion of skin using hook-and-loop fasteners or buckles, are well-understood, routine, and conventional, as evidenced by the references above.
As best understood, the limitations directed to “the first electrode”, “the second electrode”, the coupling of the electrodes with skin (e.g. via fasteners), “an analog front end (AFE)”, the AFE receiving and conditioning EDA data (by supplying voltage to the electrodes to measure fluctuations that are converted from analog to digital signals via an analog-to-digital converter), and the “unsupervised machine learning”, are well-understood, routine, and conventional.
Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Thus, none of the claims 11-16 and 20 amount to significantly more than the abstract idea itself. Accordingly, claims 11-16 and 20 are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al., MPEP §2106.04(a)(2), MPEP §2106.04(d)(2),and MPEP §2106.05(g).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 11-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Taratorin et al. (US 2014/0378859) in view of Jeong et al. (US 2019/0231235) in view of in view of Faul et al. (US 2012/0101401) in view of Liu et al. (US 2021/0290113) in view of Kim et al. (US 2018/0103917) in view of Zuckerman Stark et al. (US 2018/0310877).
Taratorin discloses an electrodermal activity (EDA) sensor ([0002]: galvanic skin response measurement is interpreted as EDA; [0012]: EDA sensor is electrode pair measuring GSR signals) comprising:
a first electrode (fig. 4A: combination of 20a and 20b; [0057]: each electrode pair comprises of rings 20a-20b) configured to electrically couple [0057] with a first portion of skin (fig. 4B) of a person (fig. 4B);
a second electrode fig. 4A: second pair of electrodes on left side; [0057]: each electrode pair comprises of rings 20a-20b) electrically coupled to the first electrode (fig. 5: channels 26a-26d represent electrodes and are electrically connected to one another; [0059]) and configured to electrically couple with a second portion of skin of the person (fig. 4B);
an analog/digital converter([0064]: A/D converter), operably coupled to the first electrode and the second electrode ([0064]: A/D convertors receive signals from channels i.e. electrodes; [0060]), to receive [0064] and condition EDA data collected by the first electrode and the second electrode ([0064]: A/D converters filters out noise from the electrodes; [0060]);
a processor ([0057]: signal acquisition and processing system 24; [0038]),
operably coupled to the analog/digital converter ([0049]: 24 comprises channels which are connected to A/D connector; [0053]) and
configured to identify [0012, 0031] and remove [0031, 0066] artifacts [0031] from the EDA data collected by the first electrode and second electrode [0031] by:
dividing the EDA data into segments ([0012]: GSR signals are divided based on electrode pairs; [0031]: time dynamics of recorded GSR signals are compared to determine correlation between events),
each segment having a duration corresponding to a timescale of the artifacts
([0012]: time dynamic of recorded GSR signals are measured so that noise and motion artifacts that are not correlated with each other are removed; [0031]: nonsynchronized GSR signals are considered artifacts since they’re not synchronized in different locations between the electrodes and are considered to have a timescale of the artifacts so they can be removed from the recorded data once detected);
extracting respective features ([0031]: recorded GSR levels that are from multiple channels) from the segments of the EDA data ([0031]: events that show high temporal correlation in at least two channels are considered to be physiological GSR, which means that nonsynchronized GSR signals are removed);
identifying, using digital signal processing algorithms [0038], the artifacts in the segments of the EDA data based on the respective feature ([0031]: nonsynchronized GSR signals are detected based on the synchronized data; [0012]: artifacts are not correlated between multiple electrode pairs and therefore are able to be removed); and
removing the artifacts from the EDA data to yield corrected EDA data ([0031]: nonsynchronized GSR signals are removed from the recorded data);
an electronic system ([0052]: electronic system receives feedback signals), operably coupled to the processor ([0052]: microprocessor calculates statistical parameters which are used for feedback), to output [0052] the corrected EDA data in real time ([0039]: provides real-time detection of GSR; [0052]: real-time statistical parameters of the synchronous GSR signals are provided to an electronic system).
Taratorin fails to disclose
an analog front end, operably coupled to the first electrode and the second electrode, to receive and condition EDA data collected by the first electrode and the second electrode;
a processor,
operably coupled to the AFE and
configured to identify and remove artifacts from the EDA data…by:
extracting respective feature vectors from the segments of the EDA data;
identifying, using unsupervised machine learning, the artifacts in the segments of the EDA data based on the respective feature vector; and
automatically removing the artifacts from the EDA data in real time using unsupervised machine learning to yield corrected EDA data; and
a display, operably coupled to the processor, to display the corrected EDA data in real time.
Regarding the limitations,
“an analog front end, operably coupled to the first electrode and the second electrode, to receive and condition EDA data collected by the first electrode and the second electrode;
a processor, operably coupled to the AFE”,
Jeong teaches an analogous electronic device (fig. 3: 301) for measuring GSR [0147]
comprising of
a first electrode (362) and
a second electrode (371), and
comprising
an analog front end [0073], operably coupled to the first electrode and the second electrode ([0072]: sensor module 276 is coupled to electrodes for measuring biometric information such as GSR; [0073]: AFE receives and modulates analog signal from sensor module 276), to receive [0073] and condition [0073] EDA data collected by the first electrode and the second electrode ([0112]: electrodes 362 and 371 may measure GSR; [0073, 0112]);
a processor (fig. 2: 201; [0101]), operably coupled to the AFE ([0073]: 201 may include AFE; fig. 2: 201 comprises of 220 and would therefore be coupled to AFE).
Jeong further teaches that either an analog-to-digital converter may be used or an analog front end [0073], which would receive/modulate [0073] analog signal received from the sensor module [0073].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the electrodermal activity (EDA) sensor taught by Taratorin, to provide an analog front end, operably coupled to the first electrode and the second electrode, to receive and condition EDA data collected by the first electrode and the second electrode; a processor, operably coupled to the AFE, as taught by Jeong, because an analog front end may be used to receive/modulate analog signal received from the sensor module.
Regarding the limitations,
“a processor, …configured to identify and remove artifacts from the EDA data…by:
extracting respective feature vectors from the segments of the EDA data;
identifying, using…machine learning, the artifacts in the segments of the EDA data based on the respective feature vector”,
Faul teaches real-time identification of seizures [0001] in an electroencephalogram (EEG) signal [0001], and teaches:
a processor [0095] configured to identify [0093] and remove artifacts [0194] from EEG data [0194] collected by a first electrode [0002] and second electrode [0002] by:
dividing the EEG data into segments ([0031-0033]: EEG signal is segmented into epochs), each segment having a duration [0131-0132] corresponding to a timescale of the artifacts ([0114)]: epochs affected by artifacts are removed and therefore must have a duration corresponding to a timescale of the artifacts so the correct epoch is removed),
wherein the duration is about 0.5 seconds ([0070]: approximately 8 seconds);
extracting respective feature vectors ([0035-0039]: feature vector extracted and repeated for each epoch) from the segments of the EEG data [0031-0036];
identifying, using machine learning ([0178]: support vector machine), the artifacts ([0093]: SVM receives feature vector to identify artifacts) in the segments of the EEG data based on the respective feature vector ([0093]: feature vector is passed through SVM to identify artifacts; [0178]: feature data comprises a feature vector for each epoch which is inputted into the SVM classifier; [0194]: more advanced method of removing artifacts is implemented through the SVM classifier; [0037]).
Faul further teaches that passing a feature vector through the SVM classifier identifies artifacts [0093] and provides useful information regarding the artifacts to a clinician [0093]. Faul also teaches that short-term artifacts may be removed at a pre-processing stage [0194], but more advanced artifacts should be removed using the SVM classifier [0194], which is better at detecting artifacts that are harder to distinguish ([0194]: artifacts may have characteristics that are similar to seizure data).
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the electrodermal activity (EDA) sensor yielded by the proposed combination, to provide a processor, configured to identify and remove artifacts from the EDA data by: extracting respective feature vectors from the segments of the EDA data; identifying, using machine learning, the artifacts in the segments of the EDA data based on the respective feature vector, as taught by Faul which teaches removing artifacts from EEG data, and wherein the duration is about 0.5 seconds, because passing a feature vector through the SVM classifier identifies artifacts and provides useful information regarding the artifacts to a clinician, and also because short-term artifacts may be removed at a pre-processing stage, but more advanced artifacts should be removed using the SVM classifier, which is better at detecting artifacts that are harder to distinguish.
Regarding the limitation, “identifying, using unsupervised machine learning, the artifacts in the segments of the EDA data…”, Liu teaches a glucose monitoring device [0005], and teaches identifying ([0082]: anomalies in glucose level readings are filtered using isolation forests), using unsupervised machine learning ([0082]: isolation forest is an unsupervised machine learning), artifacts in segments of glucose level readings ([0082]: anomalies in glucose level readings are removed using the isolation forest).
Liu further teaches that any density-based technique [0082], such as isolation forests [0082], may be used as a filter so that anomalies in glucose levels are removed [0082].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the electrodermal activity (EDA) sensor yielded by the proposed combination, to provide identifying, using unsupervised machine learning, the artifacts in the segments of the EDA data, as taught by Liu identifying artifacts using unsupervised machine learning in the segments of glucose level readings, because any density-based technique, such as isolation forests, may be used as a filter so that anomalies are removed.
Regarding the limitations, “automatically removing the artifacts from the EDA data in real time using unsupervised machine learning to yield corrected EDA data”, Kim teaches measuring EEG signals [0012], and teaches automatically removing artifacts from EEG data [0124] in real time using [0124]unsupervised machine learning [0124] to yield corrected EEG data [0124]. Kim further teaches that SVMs can be used to automatically learn and perform a number of functions [0124], including artifact removal.
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the electrodermal activity (EDA) sensor yielded by the proposed combination, to provide a display, operably coupled to the processor, to provide automatically removing the artifacts from the EDA data in real time using unsupervised machine learning to yield corrected EDA data, as taught by Kim, because SVMs can be used to automatically learn and perform a number of functions, including artifact removal.
Regarding the limitation, “a display, operably coupled to the processor, to display the corrected EDA data in real time”, Zuckerman Stark teaches monitoring nociception [0001] using a GSR sensor [0029], and teaches a display (fig. 1A: 120) operably coupled to a processor (fig. 1A: 110a), to display corrected EDA data ([0141]: physiological parameter may be received from a GSR sensor; fig. 2: step 220 shows physiological parameters from step 210 being filtered; [0139]: parameter changes caused by administered drugs are filtered) in real time (fig. 2: step 270 of displaying data is done after step 220 of filtering data; [0171]: recorded signals and parameter trends are displayed; [0186]: real-time display of values).
Zuckerman Stark further teaches that various sensor parameters [0171] may be displayed on the user interface [0171].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the electrodermal activity (EDA) sensor yielded by the proposed combination, to provide a display, operably coupled to the processor, to display the corrected EDA data in real time, as taught by Zuckerman Stark, because various sensor parameters may be displayed on the user interface.
In re claim 12, the proposed combination fails to yield wherein the AFE is further configured to supply a voltage of about 0.2 V to about 2.5 V to the first electrode.
Jeong teaches wherein the AFE is further configured to supply a voltage of about 0.2 V to about 2.5 V to the first electrode ([0073]: AFE modulates analog signal and is part of 201 which applies voltage to an electrode; [0185]: voltage of about .5V to 2V may be applied to both the second and third electrodes; [0176]: GSR is measured using the second and third electrode; [0191]: features from embodiments may be combined into one component).
Jeong further teaches that GSR may be measured by applying a specified voltage between the second and third electrode [0112] and then measuring an electric current between the second and third electrodes [0112].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the electrodermal activity (EDA) sensor yielded by the proposed combination, to provide wherein the AFE is further configured to supply a voltage of about 0.2 V to about 2.5 V to the first electrode, as taught by Jeong, because that GSR may be measured by applying a specified voltage between the second and third electrode and then measuring an electric current between the second and third electrodes.
In re claim 13, the proposed combination yields (all mapping directed to Taratorin unless otherwise stated) wherein:
the first electrode comprises a first fastener (20a) to secure the first electrode against the first portion of skin (fig. 4B); and
the second electrode comprises a second fastener (fig. 4A: 20a on the second electrode) to secure the second electrode against the second portion of skin (fig. 4B).
In re claim 14, the proposed combination yields (all mapping directed to Taratorin unless otherwise stated) wherein:
the first portion of skin is on a first proximal phalange (fig. 4B: proximal phalange is phalange closest to wrist on F1) of a first finger (F1) on a hand (fig. 4B); and
the second portion of skin is on a second proximal (fig. 4B: proximal phalange is phalange closest to wrist on F2) phalange of a second finger (F2) on the hand (fig. 4B).
In re claim 15, the proposed combination fails to yield a system for tracking a nociceptive state of the person, the system comprising: the EDA sensor of claim 11, wherein:
the processor is further configured to determine the nociceptive state of the person in real time based at least in part on the corrected EDA data; and
the display is further configured to display a real-time indication of the nociceptive state of the person.
Zuckerman Stark teaches a system (fig. 1A: combination of 100a and 150a; [0139]) for tracking a nociceptive state [0100, 0139] of a person [0186], the system comprising: the EDA sensor of claim 11 (see the proposed combination yielded in re claim 11 above), wherein:
the processor is further configured to determine the nociceptive state of the person in real time ([0186]: real-time derived nociception scale values were recorded and sent to an external PC) based at least in part on the corrected EDA data ([0013]: nociception scale is determined using at least three physiological parameters; [0022]: one of the physiological parameters may be received from the GSR sensor [0141]; [0139]); and
the display is further configured to display a real-time indication [0171, 0186] of the nociceptive state of the person [0171, 0186].
Zuckerman Stark further teaches that having an accurate assessment of a patient’s nociception level allows for more balanced pain management [0009].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the electrodermal activity (EDA) sensor yielded by the proposed combination, to yield a system for tracking a nociceptive state of the person, the system comprising: the EDA sensor of claim 11, wherein: the processor is further configured to determine the nociceptive state of the person in real time based at least in part on the corrected EDA data; and the display is further configured to display a real-time indication of the nociceptive state of the person, as taught by Zuckerman Stark, because having an accurate assessment of a patient’s nociception level allows for more balanced pain management.
In re claim 16, the proposed combination fails to yield a sensor to measure a heart rate and a heart rate variability of the person, wherein the processor is further configured to determine the nociceptive state of the person based at least in part on the heart rate and the heart rate variability.
Zuckerman Stark teaches
a sensor ([0033]: sensor measures at least three physiological parameters of a patient) to measure
a heart rate [0038] and
a heart rate variability [0038] of the person [0038, 0033],
wherein the processor is further configured to determine the nociceptive state of the person based at least in part on
the heart rate [0152] and
the heart rate variability [0152].
Zuckerman Stark further teaches that at least three parameters should be used to determine the nociceptive state [0013], and that parameters disclosed in table 1 may be combined [0031], therefore the parameters of heart rate, heart rate variability, and GSR can be used to determine the nociceptive state of the person (see above).
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the electrodermal activity (EDA) sensor yielded by the proposed combination, to yield a sensor to measure a heart rate and a heart rate variability of the person, wherein the processor is further configured to determine the nociceptive state of the person based at least in part on the heart rate and the heart rate variability, as taught by Zuckerman Stark, because at least three parameters should be used to determine the nociceptive state, for instance, heart rate, heart rate variability, and GSR can be used to determine the nociceptive state of the person.
In re claim 20, regarding the limitations, “wherein the duration is about 0.5 seconds”, see the proposed combination yielded in re claim 1 above, as taught by Faul.
Additionally, it would have been obvious to one having ordinary skill in the art at the time the invention was made to provide wherein the duration is about 0.5 seconds, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art.
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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/RUMAISA RASHID BAIG/Examiner, Art Unit 3796 /William J Levicky/ Primary Examiner, Art Unit 3796
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