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 .
Claim Objections
Claims 5, 11, and 17 are objected to because of the following informalities: “wherein the predefined threshold values for the quality index are an application specific” should be “wherein the predefined threshold values for the quality index are an application specific[s]”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 contains the term “context specific features are based on an initial context from day-to-day activities”. The specification fails to define what these features are. Paragraph [0029] of the provided specification merely states “the set of context specific features may vary according to the stimulus from activities of the user”. The same issue is present in claims 7 and 13
Claims not explicitly rejected above are rejected because they depend from claims rejected above as failing to comply with the written description requirement.
Claims 1-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation " the selected one or more electrodes" in page 3 line 11. There is insufficient antecedent basis for this limitation in the claim. It is recommended the claim be amended to say “the selected set of electrodes”. The same issue is present in claims 7 and 13.
Claim 1 recites the limitation " the set of artifacts of the received EEG signals" in page 3 lines 4-5 . There is insufficient antecedent basis for this limitation in the claim. It is recommended the claim be amended to say “the set of artifacts of the set of received EEG signals”. The same issue is present in claims 7 and 13.
Claim 1 recites the limitation " the set of application specific features " in page 2 lines 19-20 . There is insufficient antecedent basis for this limitation in the claim. It is recommended the claim be amended to say “the set of context specific features”. The same issue is present in claims 7 and 13.
Claim 4 recites the limitation "the context specific features" . There is insufficient antecedent basis for this limitation in the claim. It is recommended the claim be amended to say “the set of context specific features”. The same issue is present in claims 10 and 16.
Claim 5 recites the limitation "the predefined threshold values". There is insufficient antecedent basis for this limitation in the claim. It is recommended the claim be amended to say “the predefined threshold value”. The same issue is present in claims 11 and 17.
Claims not explicitly rejected above are rejected because they depend from claims rejected above as indefinite.
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.
Claim(s) 1, 3-5, 7, 9-11, 13,and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Le (US 20190320979 A1) in view of Azemi (US 20230225659 A1 – cited by applicant), Tamburro (Automated detection and removal of flat line segments and large amplitude fluctuations in neonatal electroencephalography) and Nakagawa (US 20100331661 A1).
In regards to claim 1 Le teaches a processor-implemented method comprising:
receiving, via an Input/Output (I/O) interface, a multitude of bioelectric signals of a user from a plurality of electrodes within a wearable Ear- Electroencephalography (EEG) device ([0026] EEG sensors are used at ear region), wherein the plurality of electrodes includes a ground electrode and a reference electrode ([0068] “The instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a patient computer or mobile device, or any suitable combination thereof. Other systems and methods of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions”, [0070] “EEG sensor is in ear canal, wherein each of the pair of EEG electrodes is placed within an ear canal of the user”, ground and reference electrodes are inherent to EEG electrodes);
collecting, via one or more hardware processors, a set of context specific features from a mobile application within a mobile device, wherein the set of context specific features are based on an initial context from day-to-day activities of the user collected automatically from the wearable Ear-EEG device ([0015] “Signal features can, in variations, be indicative of properties of the user (e.g., a time-dependent bioparameter, cognitive state, basal bioelectrical output, movements, head gestures, etc.), properties of the biomonitoring device (e.g., contact quality, physical orientation, positional stability in relation to the user, power levels, etc.), and any other suitable user and/or device characteristics”)
determining, via the one or more hardware processors, a contact of each of the plurality of electrodes with the user in order to receive the multitude of bioelectric signals based on a set of impedance values, wherein the set of impedance values is an impedance between each of the plurality of electrodes and a skin-body interface of the user ([0039] “assess the contact of one or more sensors (e.g., based on real-time impedance measurements”);
collecting one or more characteristics of the multitude of bioelectric signals that is at least one of (1) in time domain, or (2) in frequency domain, or (3) in both time and frequency domain ([0038] “Generated signal quality metrics preferably indicate a quality of the signal content (e.g., taking into account the amount of noise in the signal, taking into account the type of noise in the signal, taking into account a signal-to-noise ratio, taking into account a signal artifact, other signal parameter, etc.)”, [0045] “The parameter associated with a frequency range is herein equivalently referred to as a frequency feature. In preferred variations, the parameter is a power, but can additionally or alternatively include any suitable parameter, such as—but not limited to—a frequency, phase, time, or amplitude”).
evaluating, via the one or more hardware processors, a quality index value of each of the received set of EEG signals based on a predefined threshold value for the mobile application using a pre-trained machine learning model, wherein one or more characteristics of the received multitude of bioelectric signals along with the set of application specific features are used to determine the signal quality of the received set of EEG signals ([0038-0039] “Generated signal quality metrics preferably indicate a quality of the signal content (e.g., taking into account the amount of noise in the signal, taking into account the type of noise in the signal, taking into account a signal-to-noise ratio, taking into account a signal artifact, other signal parameter, etc.)”, [0055] In one variation of the signal quality model, the signal quality model includes a multi-dimensional probabilistic model (e.g., a Gaussian model) that functions as a reference model (e.g., is trained on “good” or “clean” data), [0041] “The signal quality model is further preferably determined based on data having a signal quality above a predetermined threshold (“good” data), as determined for instance, by an expert or professional trained in proper EEG electrode placement (e.g., EEG technician, physician, neuroscientist, etc.)”), [0042] signal features are used to determine quality contact);
classifying, via the one or more hardware processors, the selected set of electrodes based on the evaluated quality index value of the set of EEG signals and a set of artifacts of the set of EEG signals, wherein a continuous detection of the set of artifacts of the received EEG signals is carried out ([0055] metric contains artifacts, [0043] “In variations including a sliding window process, the set of features are preferably updated more frequently than every second (e.g., every eighth second, every quarter second, every half second, etc.), but can alternatively be updated less frequently than every second.”, windowing process allows for continuous detection);
and re-calibrating, via the one or more hardware processors, the ear-EEG device based on the evaluated quality index value of the set of EEG signals received from the selected one or more electrodes ([0064] teaches weighting the data based on signal quality).
Le fails to teach selecting, via the one or more hardware processors, a set of electrodes of the plurality of electrodes which receives a set of EEG signals based on one or more characteristics of the multitude of bioelectric signals and the collected set of context specific features. Azemi teaches selecting electrodes based on if they are producing good quality data in a specific situation ([0031] “As described herein, each user may have a different shape and size of an ear canal. Accordingly, an active electrode having one predetermined position on the housing may produce a signal of higher strength in one situation (e.g., for one user or at a particular time) but may not produce a good quality signal in another situation (e.g., for a different user or at a different time). As a result, a customized wearable electronic device in which the electrodes are placed at particular locations for a particular user, to obtain a good quality measurement from the particular user, may be useful. However, such a customized device may be expensive. A solution to this problem, as described in the present disclosure, is to place a number of active electrodes on a surface of a housing of a wearable electronic device, and to dynamically select a subset of the electrodes that is best able to measure the biosignal for a given user, at a given time, and possibly under a given set of other conditions (e.g., ambient conditions)”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of Le to include a step of selecting electrodes that have the best contact/signal quality to use in data collection like the method of Azemi. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of increasing signal accuracy by using electrodes that produce accurate signals.
Modified Le fails to teach verifying, via the one or more hardware processors, the received multitude of bioelectric signals from the plurality of electrodes based on a flat channel detection technique to have a predefined optimum voltage level, wherein one or more electrodes of the plurality of electrodes are removed pertaining to the multitude of bioelectric signals having a predefined saturated voltage level using the flat channel detection technique. Tamburro teaches disregarding EEG signals that have flat voltages (Background “Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to include a step of disregarding electrodes with a voltage of zero for a prolonged period of time like the method of Tamburro. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of increasing signal accuracy by using electrodes that produce accurate signals.
Modified Le teaches sending an alert to the user to recalibrate the Ear-EEG device if the sensors are not in contact with their skin ([0061]” Block S130 can also function to inform the user that contact is suboptimal (e.g., without providing further instructions on specific sensor placement changes) and encouraging the user to adjust the biomonitoring device”). However, Modified Le fails to teach checking, via the one or more hardware processors, the selected set of electrodes of the plurality of electrodes to see it they are lesser than a predefined minimum number. Nakagawa teaches that at least one reference and two active electrodes are needed to detect brain signals ([0011] The embodiments allow one electrode suitable as the reference and two electrodes that are the minimum necessary to detect the brain waves). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to alert the user to adjust the device if less than one reference and two active electrodes (as taught by Nakagawa) are selected. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of allowing for the user to adjust the device to ensure proper data collection.
In regards to claim 3 modified Le teaches the processor-implemented method of claim 1, wherein the set of impedance values between the user skin-body and each of the plurality of electrodes is a predefined minimum low impedance conductive path compared to a predefined threshold value of impedance, in order to receive the multitude of bioelectric signals (Le [0041] The signal quality model is further preferably determined based on data having a signal quality above a predetermined threshold (“good” data), as determined for instance, by an expert or professional trained in proper EEG electrode placement (e.g., EEG technician, physician, neuroscientist, etc.)., [0038] teaches Impedance is a quality measurement).
In regards to claim 4 modified Le teaches the processor-implemented method of claim 1, wherein the quality index value of each of the received set of EEG signals varies according to the context specific features of the mobile application (Le [0039] Signal features are used to determine quality index, [0015] “Signal features can, in variations, be indicative of properties of the user (e.g., a time-dependent bioparameter, cognitive state, basal bioelectrical output, movements, head gestures, etc.), properties of the biomonitoring device (e.g., contact quality, physical orientation, positional stability in relation to the user, power levels, etc.), and any other suitable user and/or device characteristics”).
In regards to claim 5 modified Le teaches the processor-implemented method of claim 1, wherein a framework is created to compute a set of quality metrics for each of the selected one or more electrodes receiving the set of EEG signals at an instant and check if the set of quality metrics are in range of the predefined threshold value, and wherein the predefined threshold values for the quality index are an application specific ([0055] gaussian model has predetermined thresholds).
In regards to claim 7 Le teaches system comprising: a memory storing instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces ([0068] “The instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a patient computer or mobile device, or any suitable combination thereof. Other systems and methods of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions”);, wherein the one or more hardware processors are configured by the instructions to carry out the steps of:
receiving, via an Input/Output (I/O) interface, a multitude of bioelectric signals of a user from a plurality of electrodes within a wearable Ear- Electroencephalography (EEG) device ([0026] EEG sensors are used at ear region), wherein the plurality of electrodes includes a ground electrode and a reference electrode ([0068] “The instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a patient computer or mobile device, or any suitable combination thereof. Other systems and methods of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions”, [0070] “EEG sensor is in ear canal, wherein each of the pair of EEG electrodes is placed within an ear canal of the user”, ground and reference electrodes are inherent to EEG electrodes);
collecting, via one or more hardware processors, a set of context specific features from a mobile application within a mobile device, wherein the set of context specific features are based on an initial context from day-to-day activities of the user collected automatically from the wearable Ear-EEG device ([0015] “Signal features can, in variations, be indicative of properties of the user (e.g., a time-dependent bioparameter, cognitive state, basal bioelectrical output, movements, head gestures, etc.), properties of the biomonitoring device (e.g., contact quality, physical orientation, positional stability in relation to the user, power levels, etc.), and any other suitable user and/or device characteristics”)
determining, via the one or more hardware processors, a contact of each of the plurality of electrodes with the user in order to receive the multitude of bioelectric signals based on a set of impedance values, wherein the set of impedance values is an impedance between each of the plurality of electrodes and a skin-body interface of the user ([0039] “assess the contact of one or more sensors (e.g., based on real-time impedance measurements”);
collecting one or more characteristics of the multitude of bioelectric signals that is at least one of (1) in time domain, or (2) in frequency domain, or (3) in both time and frequency domain ([0038] “Generated signal quality metrics preferably indicate a quality of the signal content (e.g., taking into account the amount of noise in the signal, taking into account the type of noise in the signal, taking into account a signal-to-noise ratio, taking into account a signal artifact, other signal parameter, etc.)”, [0045] “The parameter associated with a frequency range is herein equivalently referred to as a frequency feature. In preferred variations, the parameter is a power, but can additionally or alternatively include any suitable parameter, such as—but not limited to—a frequency, phase, time, or amplitude”).
evaluating, via the one or more hardware processors, a quality index value of each of the received set of EEG signals based on a predefined threshold value for the mobile application using a pre-trained machine learning model, wherein one or more characteristics of the received multitude of bioelectric signals along with the set of application specific features are used to determine the signal quality of the received set of EEG signals ([0038-0039] “Generated signal quality metrics preferably indicate a quality of the signal content (e.g., taking into account the amount of noise in the signal, taking into account the type of noise in the signal, taking into account a signal-to-noise ratio, taking into account a signal artifact, other signal parameter, etc.)”, [0055] In one variation of the signal quality model, the signal quality model includes a multi-dimensional probabilistic model (e.g., a Gaussian model) that functions as a reference model (e.g., is trained on “good” or “clean” data), [0041] “The signal quality model is further preferably determined based on data having a signal quality above a predetermined threshold (“good” data), as determined for instance, by an expert or professional trained in proper EEG electrode placement (e.g., EEG technician, physician, neuroscientist, etc.)”), [0042] signal features are used to determine quality contact);
classifying, via the one or more hardware processors, the selected set of electrodes based on the evaluated quality index value of the set of EEG signals and a set of artifacts of the set of EEG signals, wherein a continuous detection of the set of artifacts of the received EEG signals is carried out ([0055] metric contains artifacts, [0043] “In variations including a sliding window process, the set of features are preferably updated more frequently than every second (e.g., every eighth second, every quarter second, every half second, etc.), but can alternatively be updated less frequently than every second.”, windowing process allows for continuous detection);
and re-calibrating, via the one or more hardware processors, the ear-EEG device based on the evaluated quality index value of the set of EEG signals received from the selected one or more electrodes ([0064] teaches weighting the data based on signal quality).
Le fails to teach selecting, via the one or more hardware processors, a set of electrodes of the plurality of electrodes which receives a set of EEG signals based on one or more characteristics of the multitude of bioelectric signals and the collected set of context specific features. Azemi teaches selecting electrodes based on if they are producing good quality data in a specific situation ([0031] “As described herein, each user may have a different shape and size of an ear canal. Accordingly, an active electrode having one predetermined position on the housing may produce a signal of higher strength in one situation (e.g., for one user or at a particular time) but may not produce a good quality signal in another situation (e.g., for a different user or at a different time). As a result, a customized wearable electronic device in which the electrodes are placed at particular locations for a particular user, to obtain a good quality measurement from the particular user, may be useful. However, such a customized device may be expensive. A solution to this problem, as described in the present disclosure, is to place a number of active electrodes on a surface of a housing of a wearable electronic device, and to dynamically select a subset of the electrodes that is best able to measure the biosignal for a given user, at a given time, and possibly under a given set of other conditions (e.g., ambient conditions)”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the system of Le to include a step of selecting electrodes that have the best contact/signal quality to use in data collection like the method of Azemi. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of increasing signal accuracy by using electrodes that produce accurate signals.
Modified Le fails to teach verifying, via the one or more hardware processors, the received multitude of bioelectric signals from the plurality of electrodes based on a flat channel detection technique to have a predefined optimum voltage level, wherein one or more electrodes of the plurality of electrodes are removed pertaining to the multitude of bioelectric signals having a predefined saturated voltage level using the flat channel detection technique. Tamburro teaches disregarding EEG signals that have flat voltages (Background “Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the system of modified Le to include a step of disregarding electrodes with a voltage of zero for a prolonged period of time like the method of Tamburro. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of increasing signal accuracy by using electrodes that produce accurate signals.
Modified Le teaches sending an alert to the user to recalibrate the Ear-EEG device if the sensors are not in contact with their skin ([0061]” Block S130 can also function to inform the user that contact is suboptimal (e.g., without providing further instructions on specific sensor placement changes) and encouraging the user to adjust the biomonitoring device”). However, Modified Le fails to teach checking, via the one or more hardware processors, the selected set of electrodes of the plurality of electrodes to see it they are lesser than a predefined minimum number. Nakagawa teaches that at least one reference and two active electrodes are needed to detect brain signals ([0011] The embodiments allow one electrode suitable as the reference and two electrodes that are the minimum necessary to detect the brain waves). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the system of modified Le to alert the user to adjust the device if less than one reference and two active electrodes (as taught by Nakagawa) are selected. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of allowing for the user to adjust the device to ensure proper data collection.
In regards to claim 9 modified Le teaches the system of claim 7, wherein the set of impedance values between the user skin-body and each of the plurality of electrodes is a predefined minimum low impedance conductive path compared to a predefined threshold value of impedance, in order to receive the multitude of bioelectric signals (Le [0041] The signal quality model is further preferably determined based on data having a signal quality above a predetermined threshold (“good” data), as determined for instance, by an expert or professional trained in proper EEG electrode placement (e.g., EEG technician, physician, neuroscientist, etc.)., [0038] teaches Impedance is a quality measurement).
In regards to claim 10 modified Le teaches the system of claim 7, wherein the quality index value of each of the received set of EEG signals varies according to the context specific features of the mobile application (Le [0039] Signal features are used to determine quality index, [0015] “Signal features can, in variations, be indicative of properties of the user (e.g., a time-dependent bioparameter, cognitive state, basal bioelectrical output, movements, head gestures, etc.), properties of the biomonitoring device (e.g., contact quality, physical orientation, positional stability in relation to the user, power levels, etc.), and any other suitable user and/or device characteristics”).
In regards to claim 11 modified Le teaches the system of claim 7, wherein a framework is created to compute a set of quality metrics for each of the selected one or more electrodes receiving the set of EEG signals at an instant and check if the set of quality metrics are in range of the predefined threshold value, and wherein the predefined threshold values for the quality
In regards to claim 13 Le teaches one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause the steps of:
receiving, via an Input/Output (I/O) interface, a multitude of bioelectric signals of a user from a plurality of electrodes within a wearable Ear- Electroencephalography (EEG) device ([0026] EEG sensors are used at ear region), wherein the plurality of electrodes includes a ground electrode and a reference electrode ([0068] “The instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a patient computer or mobile device, or any suitable combination thereof. Other systems and methods of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions”, [0070] “EEG sensor is in ear canal, wherein each of the pair of EEG electrodes is placed within an ear canal of the user”, ground and reference electrodes are inherent to EEG electrodes);
collecting, via one or more hardware processors, a set of context specific features from a mobile application within a mobile device, wherein the set of context specific features are based on an initial context from day-to-day activities of the user collected automatically from the wearable Ear-EEG device ([0015] “Signal features can, in variations, be indicative of properties of the user (e.g., a time-dependent bioparameter, cognitive state, basal bioelectrical output, movements, head gestures, etc.), properties of the biomonitoring device (e.g., contact quality, physical orientation, positional stability in relation to the user, power levels, etc.), and any other suitable user and/or device characteristics”)
determining, via the one or more hardware processors, a contact of each of the plurality of electrodes with the user in order to receive the multitude of bioelectric signals based on a set of impedance values, wherein the set of impedance values is an impedance between each of the plurality of electrodes and a skin-body interface of the user ([0039] “assess the contact of one or more sensors (e.g., based on real-time impedance measurements”);
collecting one or more characteristics of the multitude of bioelectric signals that is at least one of (1) in time domain, or (2) in frequency domain, or (3) in both time and frequency domain ([0038] “Generated signal quality metrics preferably indicate a quality of the signal content (e.g., taking into account the amount of noise in the signal, taking into account the type of noise in the signal, taking into account a signal-to-noise ratio, taking into account a signal artifact, other signal parameter, etc.)”, [0045] “The parameter associated with a frequency range is herein equivalently referred to as a frequency feature. In preferred variations, the parameter is a power, but can additionally or alternatively include any suitable parameter, such as—but not limited to—a frequency, phase, time, or amplitude”).
evaluating, via the one or more hardware processors, a quality index value of each of the received set of EEG signals based on a predefined threshold value for the mobile application using a pre-trained machine learning model, wherein one or more characteristics of the received multitude of bioelectric signals along with the set of application specific features are used to determine the signal quality of the received set of EEG signals ([0038-0039] “Generated signal quality metrics preferably indicate a quality of the signal content (e.g., taking into account the amount of noise in the signal, taking into account the type of noise in the signal, taking into account a signal-to-noise ratio, taking into account a signal artifact, other signal parameter, etc.)”, [0055] In one variation of the signal quality model, the signal quality model includes a multi-dimensional probabilistic model (e.g., a Gaussian model) that functions as a reference model (e.g., is trained on “good” or “clean” data), [0041] “The signal quality model is further preferably determined based on data having a signal quality above a predetermined threshold (“good” data), as determined for instance, by an expert or professional trained in proper EEG electrode placement (e.g., EEG technician, physician, neuroscientist, etc.)”), [0042] signal features are used to determine quality contact);
classifying, via the one or more hardware processors, the selected set of electrodes based on the evaluated quality index value of the set of EEG signals and a set of artifacts of the set of EEG signals, wherein a continuous detection of the set of artifacts of the received EEG signals is carried out ([0055] metric contains artifacts, [0043] “In variations including a sliding window process, the set of features are preferably updated more frequently than every second (e.g., every eighth second, every quarter second, every half second, etc.), but can alternatively be updated less frequently than every second.”, windowing process allows for continuous detection);
and re-calibrating, via the one or more hardware processors, the ear-EEG device based on the evaluated quality index value of the set of EEG signals received from the selected one or more electrodes ([0064] teaches weighting the data based on signal quality).
Le fails to teach selecting, via the one or more hardware processors, a set of electrodes of the plurality of electrodes which receives a set of EEG signals based on one or more characteristics of the multitude of bioelectric signals and the collected set of context specific features. Azemi teaches selecting electrodes based on if they are producing good quality data in a specific situation ([0031] “As described herein, each user may have a different shape and size of an ear canal. Accordingly, an active electrode having one predetermined position on the housing may produce a signal of higher strength in one situation (e.g., for one user or at a particular time) but may not produce a good quality signal in another situation (e.g., for a different user or at a different time). As a result, a customized wearable electronic device in which the electrodes are placed at particular locations for a particular user, to obtain a good quality measurement from the particular user, may be useful. However, such a customized device may be expensive. A solution to this problem, as described in the present disclosure, is to place a number of active electrodes on a surface of a housing of a wearable electronic device, and to dynamically select a subset of the electrodes that is best able to measure the biosignal for a given user, at a given time, and possibly under a given set of other conditions (e.g., ambient conditions)”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of Le to include a step of selecting electrodes that have the best contact/signal quality to use in data collection like the method of Azemi. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of increasing signal accuracy by using electrodes that produce accurate signals.
Modified Le fails to teach verifying, via the one or more hardware processors, the received multitude of bioelectric signals from the plurality of electrodes based on a flat channel detection technique to have a predefined optimum voltage level, wherein one or more electrodes of the plurality of electrodes are removed pertaining to the multitude of bioelectric signals having a predefined saturated voltage level using the flat channel detection technique. Tamburro teaches disregarding EEG signals that have flat voltages (Background “Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to include a step of disregarding electrodes with a voltage of zero for a prolonged period of time like the method of Tamburro. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of increasing signal accuracy by using electrodes that produce accurate signals.
Modified Le teaches sending an alert to the user to recalibrate the Ear-EEG device if the sensors are not in contact with their skin ([0061]” Block S130 can also function to inform the user that contact is suboptimal (e.g., without providing further instructions on specific sensor placement changes) and encouraging the user to adjust the biomonitoring device”). However, Modified Le fails to teach checking, via the one or more hardware processors, the selected set of electrodes of the plurality of electrodes to see it they are lesser than a predefined minimum number. Nakagawa teaches that at least one reference and two active electrodes are needed to detect brain signals ([0011] The embodiments allow one electrode suitable as the reference and two electrodes that are the minimum necessary to detect the brain waves). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to alert the user to adjust the device if less than one reference and two active electrodes (as taught by Nakagawa) are selected. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of allowing for the user to adjust the device to ensure proper data collection.
In regards to claim 15 modified Le teaches one or more non-transitory machine-readable information storage mediums of claim 13, wherein the set of impedance values between the user skin-body and each of the plurality of electrodes is a predefined minimum low impedance conductive path compared to a predefined threshold value of impedance, in order to receive the multitude of bioelectric signals (Le [0041] The signal quality model is further preferably determined based on data having a signal quality above a predetermined threshold (“good” data), as determined for instance, by an expert or professional trained in proper EEG electrode placement (e.g., EEG technician, physician, neuroscientist, etc.)., [0038] teaches Impedance is a quality measurement).
In regards to claim 16 modified Le teaches the one or more non-transitory machine-readable information storage mediums of claim 13, wherein the quality index value of each of the received set of EEG signals varies according to the context specific features of the mobile application (Le [0039] Signal features are used to determine quality index, [0015] “Signal features can, in variations, be indicative of properties of the user (e.g., a time-dependent bioparameter, cognitive state, basal bioelectrical output, movements, head gestures, etc.), properties of the biomonitoring device (e.g., contact quality, physical orientation, positional stability in relation to the user, power levels, etc.), and any other suitable user and/or device characteristics”).
In regards to claim 17 modified Le teaches the one or more non-transitory machine-readable information storage mediums of claim 13, wherein a framework is created to compute a set of quality metrics for each of the selected one or more electrodes receiving the set of EEG signals at an instant and check if the set of quality metrics are in range of the predefined threshold value, and wherein the predefined threshold values for the quality
Claim(s) 2, 8, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Le (US 20190320979 A1) in view of Azemi (US 20230225659 A1 – cited by applicant) in view of Tamburro (Automated detection and removal of flat line segments and large amplitude fluctuations in neonatal electroencephalography) in view of Nakagawa (US 20100331661 A1), as applied to claims 1, 7, and 13, further in view of Coleman (US 20150199010 A1) in view of Xie (US 20200409409 A1).
In regards to claim 2 modified Le teaches the processor-implemented method of claim 1. Modified Le fails to teach wherein the initial context is a prompt message from the mobile application to the Ear-EEG device via the mobile device. Coleman teaches the user inputting an activity type which is then used to select algorithms for analysis of EEG data (0070] The user may select a specific activity type (e.g. meditation exercise) from a screen displayed by the application; [0056] analysis pipelines according to activity type). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to include a step of prompting the user to input the activity they are engaging in and using that as a signal feature like the method of Coleman. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of increasing signal accuracy by accounting for the activity type the user is engaging in while the device is being calibrated.
Modified Le fails to teach an alert to wear the Ear-EEG device is sent to the user in case of an absence of the prompt message from the mobile application. Xie teaches alerting the user to wear a device when no data input is received ([0068] no human body data is acquired, [0071] alerts user that it is unworn). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to include a step of prompting the user to put on the device if it is unworn. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of remining the user to put on the device if it is not to allow for the EEG data to be collected.
In regards to claim 8 modified Le teaches the system of claim 7. Modified Le fails to teach wherein the initial context is a prompt message from the mobile application to the Ear-EEG device via the mobile device. Coleman teaches the user inputting an activity type which is then used to select algorithms for analysis of EEG data (0070] The user may select a specific activity type (e.g. meditation exercise) from a screen displayed by the application; [0056] analysis pipelines according to activity type). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to include a step of prompting the user to input the activity they are engaging in and using that as a signal feature like the method of Coleman. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of increasing signal accuracy by accounting for the activity type the user is engaging in while the device is being calibrated.
Modified Le fails to teach an alert to wear the Ear-EEG device is sent to the user in case of an absence of the prompt message from the mobile application. Xie teaches alerting the user to wear a device when no data input is received ([0068] no human body data is acquired, [0071] alerts user that it is unworn). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the system of modified Le to include a step of prompting the user to put on the device if it is unworn. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of remining the user to put on the device if it is not to allow for the EEG data to be collected.
In regards to claim 14 modified Le teaches the one or more non-transitory machine-readable information storage mediums of claim 13. Modified Le fails to teach wherein the initial context is a prompt message from the mobile application to the Ear-EEG device via the mobile device. Coleman teaches the user inputting an activity type which is then used to select algorithms for analysis of EEG data (0070] The user may select a specific activity type (e.g. meditation exercise) from a screen displayed by the application; [0056] analysis pipelines according to activity type). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to include a step of prompting the user to input the activity they are engaging in and using that as a signal feature like the method of Coleman. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of increasing signal accuracy by accounting for the activity type the user is engaging in while the device is being calibrated.
Modified Le fails to teach an alert to wear the Ear-EEG device is sent to the user in case of an absence of the prompt message from the mobile application. Xie teaches alerting the user to wear a device when no data input is received ([0068] no human body data is acquired, [0071] alerts user that it is unworn). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to include a step of prompting the user to put on the device if it is unworn. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of remining the user to put on the device if it is not to allow for the EEG data to be collected.
Claim(s) 6, 12, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Le (US 20190320979 A1) in view of Azemi (US 20230225659 A1 – cited by applicant) in view of Tamburro (Automated detection and removal of flat line segments and large amplitude fluctuations in neonatal electroencephalography) in view of Nakagawa (US 20100331661 A1), as applied to claims 1, 7, and 13, further in view of Miller (US 10786206 B1).
In regards to claim 6 modified Le teaches the processor-implemented method of claim 1. Modified Le fails to teach if the quality index value drops below a predefined lowest threshold for the given mobile application by the user for a minimum number of electrodes, an EEG signal validation and an electrode selection are done again; and if the quality index value is less than the predefined threshold value the electrode selection is carried out again. Miller teaches recalibrating sensors until if the quality is below a selected threshold (Fig 25 step 2540). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to include a step of recalibrating the device via electrode choosing and recalibration via the processor until the quality is within a certain threshold like the method of Miller. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of achieving the most accurate EEG data acquisition.
In regards to claim 12 modified Le teaches the system of claim 7. Modified Le fails to teach if the quality index value drops below a predefined lowest threshold for the given mobile application by the user for a minimum number of electrodes, an EEG signal validation and an electrode selection are done again; and if the quality index value is less than the predefined threshold value the electrode selection is carried out again. Miller teaches recalibrating sensors until if the quality is below a selected threshold (Fig 25 step 2540). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to include a step of recalibrating the device via electrode choosing and recalibration via the processor until the quality is within a certain threshold like the method of Miller. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of achieving the most accurate EEG data acquisition.
In regards to claim 18 modified Le teaches the one or more non-transitory machine-readable information storage mediums of claim 13. Modified Le fails to teach if the quality index value drops below a predefined lowest threshold for the given mobile application by the user for a minimum number of electrodes, an EEG signal validation and an electrode selection are done again; and if the quality index value is less than the predefined threshold value the electrode selection is carried out again. Miller teaches recalibrating sensors until if the quality is below a selected threshold (Fig 25 step 2540). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the of the claimed invention to modify the method of modified Le to include a step of recalibrating the device via electrode choosing and recalibration via the processor until the quality is within a certain threshold like the method of Miller. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of achieving the most accurate EEG data acquisition.
Conclusion
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/LUCY EPPERT/ Examiner, Art Unit 3791
/ADAM J EISEMAN/ Primary Examiner, Art Unit 3791
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