DETAILED ACTION
Applicant’s arguments, filed 01/26/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application. Applicant canceled claims 2, 10, and 18 and added claims 21-23.
Claims 1, 3-9, 11-17, and 19-23 are the current claims hereby under examination.
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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-17 and 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Analysis of independent claims 1, 9, and 17:
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 1 is directed to a system, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Claim 9 is directed to a non-transitory computer-program software product, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Therefore, further consideration is necessary regarding claims. Claim 17 is directed to a system, which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 1, 9, and 17 recite an abstract idea. In particular, the claims generally recite
the following:
generate, based on an application of a machine learning model to the EDA signal, a modified physiological signal, wherein the modified physiological signal comprises the EDA signal with a reduction of the artifacts (claims 1, 9, and 17);
determine, based on the modified physiological signal, a physiological measurement comprising a time-invariant or a time-variant spectral analysis of the EDA signal (claims 1, 9, and 17);
determine, based on a change in the physiological measurement with respect to a physiological measurement related to a subject without central nervous system oxygen toxicity exposure satisfying a threshold, the CNS-OT condition (claims 1, 9, and 17); and
determine a change in the physiological measurement with respect to a physiological measurement related to a subject without central nervous system oxygen toxicity exposure satisfies a threshold, wherein the change is caused by stress based on breathing performed by the subject diver during prolonged exposure to HBO2 (claim 17).
These elements recited in claims 1 and 9 are drawn to an abstract idea since they are directed to mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
“generate, based on an application of a machine learning model to the EDA signal, a modified physiological signal, wherein the modified physiological signal comprises the EDA signal with a reduction of the artifacts” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably take a signal and filter out artifacts noise in the data on pen and paper or a generic computer. There is nothing to suggest an undue level of complexity in “generate a modified physiological signal”. Examiner notes that while the claim generally indicates the generated result is “based on” a machine learning model, this generic modifying phrase does not describe the details of the model, how the model is used, what algorithms the model uses, or what the term “based on” requires.
“determine, based on the modified physiological signal, a physiological measurement comprising a time-invariant or a time-variant spectral analysis of the EDA signal” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably take data received from the subject and determine a physiological measurement therefrom. There is nothing to suggest an undue level of complexity in “determine, based on the modified physiological signal, a physiological measurement comprising a time-invariant or a time-variant spectral analysis of the EDA signal”.
“determine, based on a change in the physiological measurement with respect to a physiological measurement related to a subject without central nervous system oxygen toxicity exposure satisfying a threshold, the CNS-OT condition” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably take data, determine a change being under/over an arbitrary threshold, and determine a health condition therefrom. There is nothing to suggest an undue level of complexity in “determine, based on a change in the physiological measurement with respect to a physiological measurement related to a subject without central nervous system oxygen toxicity exposure satisfying a threshold, the CNS-OT condition”.
“determine a change in the physiological measurement with respect to a physiological measurement related to a subject without central nervous system oxygen toxicity exposure satisfies a threshold, wherein the change is caused by stress based on breathing performed by the subject diver during prolonged exposure to HBO2” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably take data, determine a change being under/over an arbitrary threshold, and determine a health condition therefrom. There is nothing to suggest an undue level of complexity in “determine, based on a change in the physiological measurement with respect to a physiological measurement related to a subject without central nervous system oxygen toxicity exposure satisfying a threshold, the CNS-OT condition”.
Prong Two: Claims 1, 9, and 17 do not recite additional elements that integrate the exception
into a practical application. Therefore, the claims are "directed to" the abstract idea. The additional
elements merely:
Recite the words "apply it" or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “one or more processors” (claim 1), "a memory storing processor-executable instructions" (claim 1), “one or more non-transitory computer-readable media storing processor-executable instructions” (claim 9), and “a computing device” (claim 17)) and
Add insignificant extra-solution activity (the pre-solution activity of: using generic data gathering components (e.g., “receive an electrodermal activity (EDA) signal associated with the diver, wherein the EDA signal comprises artifacts" (claim 1), “receive, by a computing device, an electrodermal activity (EDA) signal associated with a diver in an underwater environment breathing hyperbaric oxygen, wherein the EDA signal comprises artifacts” (claim 9), and “a sensor” (claim 17)); the post-solution activity of: (e.g. “cause an output of an indication of the CNS-OT condition to the diver in the underwater environment using a device carried by the diver.” (claims 1 and 9) and “a display” (claim 17)).
As a whole, the additional elements merely serve to gather information to be used by the
abstract idea, while generically implementing it on a computer. There is no practical application because
the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed
remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the
technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the
abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1, 9, and 17 do not include additional elements, alone or in combination, that are
sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the
same reasons as described above. E.g., all elements are directed to implementing the abstract ideas on
generic processing components, the pre-solution activity of using generic data-gathering components,
and generic post-solution activities, which merely facilitate the abstract idea.
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example, “one or more processors” as disclosed in the Applicant’s specification paragraph 0020, “The one or more processors 120 may include one or more of a Central Processing Unit (CPU), an Application Processor (AP), or a Communication Processor (CP)”. As another example, “a sensor” as disclosed in the Applicant’s specification paragraph 0032 “The one or more sensors 102 may include one or more sensors capable of measuring electrodermal activity (EDA) of a user (e.g., subject)”.
Further, "memory storing processor-executable instructions”, “non-transitory computer readable memory”, “a display”, and “a computing module” do not qualify as significantly more because this limitation is simply appending well understood, routine, and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric PowerGroup, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014); SAP Am. v. lnvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a
practical application and do not amount to significantly more than the above-judicial exception (the
abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing
that is not already present when looking at the elements taking individually. There is no indication that
the combination of elements improves the functioning of a computer, for example, or improves any
other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of
elements include a particular solution to a computer-based problem or a particular way to achieve a
desired computer-based outcome. Rather, the collective functions of the claimed invention merely
provide conventional computer implementation, i.e., the computer is simply a tool to perform the
process.
Analysis of the dependent claims:
Claims 3-16, and 19-22 depend from the independent claims. Dependent claims 3-8, 11-16, and 21-22 merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
Further describe the abstract idea (“wherein the machine learning model comprises a deep convolutional autoencoder network” (claims 5, 13, and 19), “wherein the machine learning model is trained based on one or more training data sets” (claims 6 and 14), “wherein the one or more training data sets comprise one or more of: a first training data set comprising EDA signals from a public data set associated with physiological stimuli based on one or more of an auditory task, pain induced by electrical stimulation, a visual detection task, fear conditioning tasks, being shown aversive or neutral pictures, being shown pictures while subjected to auditory distractors, or being shown pictures of facial expressions, a second training data set comprising EDA signals associated with a prevalence of artifacts, a third training data set comprising EDA signals associated with one or more subjects experiencing central nervous system oxygen toxicity (CNS-OT) conditions, and a fourth training data set comprising EDA signals associated with a first sub data set comprising EDA signals associated with a reduction of artifacts collected from both hands of one or more subjects and a second sub data set comprising EDA signals associated with a prevalence of artifacts collected from a first hand of one or more subjects and EDA signals associated with a reduction of artifacts collected from a second hand of one or more subjects” (claims 7 and 15), “wherein the health condition comprises one or more of a risk of seizure, a risk of CNS-OT, or symptoms of CNS-OT” (claims 8, 16, and 20), “wherein the one or more processors and the memory are disposed in the device carried by the diver” (claim 21), “wherein the one or more non-transitory computer-readable media are disposed in the device carried by the diver” (claim 22), and “wherein the sensor and the computing device are disposed in a device comprising the display” (claim 23)); and
Further describe the pre-solution activity (“wherein the change is based on an increase in phasic components of the EDA signal, and wherein the change is caused by stress based on breathing performed by the diver during prolonged exposure to hyperbaric oxygen (HBO2)” (claims 3 and 11), “determine, based on the change in the physiological measurement satisfying the threshold, the health condition, further cause the apparatus to: determine, based on the physiological measurement satisfying a condition, the change in the physiological measurement satisfies the threshold, wherein the condition comprises an autonomic induced elevation of a phasic component of the EDA signal or a non-autonomic induced elevation of a phasic component of the EDA signal; and determine, based on the change in the physiological measurement satisfying the threshold, the CNS-OT condition” (claims 4 and 12)).
Taken alone or in combination, the additional elements do not integrate the judicial exception
into a practical application at least because the abstract idea is not applied, relied on, or used in a
meaningful way. The additional elements do not add anything significantly more than the abstract idea. The collective functions of the additional elements merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. The result of the abstract idea does not cause the computing device and/or application to perform different. Therefore, claims 1, 3-9, 11-17, and 19-22 are rejected as being directed to non-statutory subjection matter.
Examiner notes that claims 5-7 and 13-15 merely recite generic components of a machine learning model, such as the use of one type of machine learning model and that the model is trained on datasets.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 6-9, 14-17, and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Vieluf (US 20230397876), Subramanian et. al. (“Unsupervised Machine Learning Methods for Artifact Removal in Electrodermal Activity”), hereinafter Subramanian, and Ooij et. al. (“Oxygen, the lung and the diver: friends and foes?”), hereinafter Ooij.
Regarding claims 1, 6, and 7, Vieluf discloses an apparatus for predicting a health condition of a subject comprising:
one or more processors (Fig. 1, processors 111; Paragraph 0081); and
a memory storing processor-executable instructions that (Fig. 1, data storage 112; Paragraphs 0082-0083), when executed by the one or more processors, cause the apparatus to:
receive an electrodermal activity (EDA) signal associated with the subject (Fig. 1, sensor 101; Paragraphs 0075 and 0079, wherein EDA data is measured and system 110 receives the data),
determine, based on the modified physiological signal, a physiological measurement comprising a time-invariant or a time-variant spectral analysis of the EDA signal (Paragraph 0075, electrodermal activity; Figs. 2 – 4);
determine, based on a change in the physiological measurement satisfying a threshold, the health condition (Paragraph 0103, wherein a seizure model engine 120 determines a seizure risk based on a probability value measured against a probability threshold; Paragraph 0169, wherein the probability of a risk of seizure is determined by the seizure model engine 120 based on physiological marker changes, such as EDA; In summary, the change in EDA is measured and used by the seizure model engine 120 to determine a seizure risk, wherein a calculated probability based on the EDA data is meeting a probability threshold; Examiner notes that Applicant discloses that a CNS-OT condition is a risk of a seizure); and
cause output of an indication associated with the health condition (Paragraph 0111, “For example, the user computing device 103 and/or the wearable sensor 101 may display the visual indication and/or emit the audible, vibration and/or tactile indication such that the user may perceive the alert of the risk of an imminent seizure”).
Regarding the limitations of claims 1, 6, and 7, Vieluf discloses using a machine learning model as above and pre-processing the EDA data to clean the data (Paragraph 0118). However, Vieluf fails to explicitly disclose using the machine learning model to reduce artifacts in the signal, wherein the machine learning model is trained on one or more training sets and a training data set comprises physiological signals associated with a prevalence of artifacts. Vieluf also fails to explicitly disclose application of the device to a diver underwater breathing hyperbaric oxygen.
However, Subramanian teaches using a machine learning method to remove movement related artifacts from subject EDA data. The machine learning model is trained on one or more training data sets (Section 2B, paragraph 2) comprising a set of physiological signals associated with a prevalence of artifacts (Section 1, paragraph 3). Subramanian discusses this is useful as it did not require manual labeling of training data and removes less true EDA signal compared to other methods (Discussion, paragraphs 1-2). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Vieluf to incorporate the teachings of a machine learning method of Subramanian to automate labeling of training data and remove less true EDA signal.
Ooij teaches the pathophysiological effects when breathing hyperbaric oxygen. Ooij discusses that such conditions are dangerous to divers and there is a need to monitor divers when breathing hyperbaric oxygen (Page 4, paragraphs 4-5) due to risk of seizure (Page 2, paragraph 2). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of determining a seizure/seizure risk of Vieluf and Subramanian to be applied to a diver underwater breathing hyperbaric oxygen of Ooij due to a risk of seizure in these conditions.
Regarding claim 8, Vieluf as modified further discloses wherein the health condition comprises a risk of seizure (Paragraph 0103).
Regarding claim 21, Vieluf as modified further discloses wherein the one or more processors and the memory are disposed in the device carried by the diver (Fig. 1, processor(s) 111 and storage 112; Paragraph 0075, “In some embodiments, the wearable sensor 101 can include, e.g., a smartwatch, a wristband sensor, a chest strap, a smart ring, or other health tracking sensor device, and combinations thereof”).
Regarding claims 9, 14, and 15, Vieluf discloses one or more non-transitory computer-readable media storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to:
receive, by a computing device, an electrodermal activity (EDA) signal associated with a subject (Fig. 1, sensor 101; Paragraphs 0075 and 0079, wherein EDA data is measured and system 110 receives the data);
determine, based on the modified physiological signal, a physiological measurement comprising a time-invariant or a time-variant spectral analysis of the EDA signal (Paragraph 0075, electrodermal activity; Figs. 2 – 4);
determine, based on a change in the physiological measurement satisfying a threshold, the health condition (Paragraph 0103, wherein a seizure model engine 120 determines a seizure risk based on a probability value measured against a probability threshold; Paragraph 0169, wherein the probability of a risk of seizure is determined by the seizure model engine 120 based on physiological marker changes, such as EDA; In summary, the change in EDA is measured and used by the seizure model engine 120 to determine a seizure risk, wherein a calculated probability based on the EDA data is meeting a probability threshold; Examiner notes that Applicant discloses that a CNS-OT condition is a risk of a seizure); and
cause output of an indication associated with the health condition (Paragraph 0111, “For example, the user computing device 103 and/or the wearable sensor 101 may display the visual indication and/or emit the audible, vibration and/or tactile indication such that the user may perceive the alert of the risk of an imminent seizure”).
Regarding the limitations of claims 9, 14, and 15, Vieluf discloses using a machine learning model as above and pre-processing the EDA data to clean the data (Paragraph 0118). However, Vieluf fails to explicitly disclose using the machine learning model to reduce artifacts in the signal, wherein the machine learning model is trained on one or more training sets and a training data set comprises physiological signals associated with a prevalence of artifacts. Vieluf also fails to explicitly disclose application of the device to a diver underwater breathing hyperbaric oxygen.
However, Subramanian teaches using a machine learning method to remove movement related artifacts from subject EDA data. The machine learning model is trained on one or more training data sets (Section 2B, paragraph 2) comprising a set of physiological signals associated with a prevalence of artifacts (Section 1, paragraph 3). Subramanian discusses this is useful as it did not require manual labeling of training data and removes less true EDA signal compared to other methods (Discussion, paragraphs 1-2). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Vieluf to incorporate the teachings of a machine learning method of Subramanian to automate labeling of training data and remove less true EDA signal.
Ooij teaches the pathophysiological effects when breathing hyperbaric oxygen. Ooij discusses that such conditions are dangerous to divers and there is a need to monitor divers when breathing hyperbaric oxygen (Page 4, paragraphs 4-5) due to risk of seizure (Page 2, paragraph 2). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of determining a seizure/seizure risk of Vieluf and Subramanian to be applied to a diver underwater breathing hyperbaric oxygen of Ooij due to a risk of seizure in these conditions.
Regarding claim 16, Vieluf as modified further discloses wherein the health condition comprises a risk of seizure (Paragraph 0103).
Regarding claim 22, Vieluf as modified further discloses wherein the one or more non-transitory computer-readable media are disposed in the device carried by the diver (Fig. 1 and paragraph 0082, processor(s) 111 and storage 112 with instructions stored thereon; Paragraph 0075, “In some embodiments, the wearable sensor 101 can include, e.g., a smartwatch, a wristband sensor, a chest strap, a smart ring, or other health tracking sensor device, and combinations thereof”).
Regarding claim 17, Vieluf discloses a system for predicting a health condition of a subject associated with prolonged exposure to hyperbaric oxygen (HBO2) comprising:
a sensor configured to be affixed to a surface of skin of the subject (Paragraphs 0074-0075), wherein the sensor is configured to receive an electrodermal activity (EDA) signal associated with the subject based on measuring a conductance associated with the surface of skin of the subject (Paragraph 0075, EDA);
a display configured to output an interface to the subject (Paragraph 0111, “For example, the user computing device 103 and/or the wearable sensor 101 may display the visual indication”; Paragraph 0227); and
a computing device in communication with the sensor and the display (Fig. 1, processors 111; Paragraph 0081), wherein the computing device is configured to:
receive, from the sensor, the EDA signal, wherein the EDA signal comprises artifacts (Fig. 1, sensor 101; Paragraphs 0075 and 0079, wherein EDA data is measured and system 110 receives the data);
provide the EDA signal to a machine learning model (Paragraph 0169, “Accordingly, the physiological markers, such as EDA, HR, TEMP, or other ANS measurements may be used to train and implement a machine learning algorithm, e.g., via the seizure model engine 120 to classify a risk of seizure”);
determine, based on the EDA signal, a physiological measurement comprising a time-invariant or a time-variant spectral analysis of the EDA signal (Paragraph 0075, electrodermal activity; Figs. 2 – 4);
determine a change in the physiological measurement satisfies a threshold (Paragraph 0103, wherein a seizure risk is determined; Paragraph 0169, wherein EDA levels are lower in patients with seizures than without; Examiner notes that one of ordinary skill in the art would recognize setting an EDA threshold to differentiate between seizure and non-seizure activity), wherein the change is caused by stress based on breathing performed by the subject during prolonged exposure to HBO2;
determine, based on a change in the physiological measurement satisfying a threshold, the health condition (Paragraph 0103, wherein a seizure model engine 120 determines a seizure risk based on a probability value measured against a probability threshold; Paragraph 0169, wherein the probability of a risk of seizure is determined by the seizure model engine 120 based on physiological marker changes, such as EDA; In summary, the change in EDA is measured and used by the seizure model engine 120 to determine a seizure risk, wherein a calculated probability based on the EDA data is meeting a probability threshold); and
cause output of an indication associated with the health condition (Paragraph 0111, “For example, the user computing device 103 and/or the wearable sensor 101 may display the visual indication and/or emit the audible, vibration and/or tactile indication such that the user may perceive the alert of the risk of an imminent seizure”).
While Vieluf discloses using a machine learning model as above and pre-processing the EDA data to clean the data (Paragraph 0118), Vieluf fails to explicitly disclose using the machine learning model to reduce artifacts in the signal.
Vieluf discloses using a machine learning model as above and pre-processing the EDA data to clean the data (Paragraph 0118). However, Vieluf fails to explicitly disclose using the machine learning model to reduce artifacts in the signal. Vieluf also fails to explicitly disclose application of the device to a diver underwater breathing hyperbaric oxygen.
However, Subramanian teaches using a machine learning method to remove movement related artifacts from subject EDA data and discusses this is useful as it did not require manual labeling of training data and removes less true EDA signal compared to other methods (Discussion, paragraphs 1-2). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Vieluf to incorporate the teachings of a machine learning method of Subramanian to automate labeling of training data and remove less true EDA signal.
Ooij teaches the pathophysiological effects when breathing hyperbaric oxygen. Ooij discusses that such conditions are dangerous to divers and there is a need to monitor divers when breathing hyperbaric oxygen (Page 4, paragraphs 4-5) due to risk of seizure (Page 2, paragraph 2). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of determining a seizure/seizure risk of Vieluf and Subramanian to be applied to a diver underwater breathing hyperbaric oxygen of Ooij due to a risk of seizure in these conditions.
Regarding claim 20, Vieluf as modified further discloses wherein the health condition comprises a risk of seizure (Paragraph 0103).
Regarding claim 23, Vieluf as modified further discloses wherein the sensor and the computing device are disposed in a device comprising the display (Fig. 1 and paragraph 0082, processor(s) 111 and storage 112 with instructions stored thereon; Paragraph 0075, “In some embodiments, the wearable sensor 101 can include, e.g., a smartwatch, a wristband sensor, a chest strap, a smart ring, or other health tracking sensor device, and combinations thereof”; Paragraph 0111, “wearable sensor 101 may display the visual indication and/or emit the audible, vibration and/or tactile indication such that the user may perceive the alert of the risk of an imminent seizure”)
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Vieluf, Subramanian, and Ooij as applied to claims 1 and 9 above, and further in view of Benedek et. al. (“A continuous measure of phasic electrodermal activity”), hereinafter Benedek.
Regarding claims 3 and 11, while Vieluf as modified by Subramanian and Ooij discloses measuring EDA responses to measure seizure risk and other sympathetic activity (Paragraph 0138), Vieluf as modified fails to explicitly disclose measuring an increase in phasic components of the EDA signal.
However, Benedek teaches that measuring phasic activity (i.e., SCRs or skin conductance response) is an adequate indicator of sympathetic activity (Section 2, paragraph 3, “a continuous measure of phasic activity, which is assumed to be an adequate indicator of sympathetic activity”; Section 1, paragraph 2, “The SCR amplitude can therefore be considered as an index of sympathetic activity”), which Benedek discusses is a more accurate measurement of the underlying activity (Section 2, paragraph 2). Examiner notes that a change in the phasic activity could be caused by a variety of reasons; however, how the change occurs does not affect the structure of the invention. Therefore, the combination of Vieluf, Subramanian, Ooij and Benedek discloses this limitation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Vieluf, Subramanian, and Ooij to incorporate measuring phasic activity of Benedek as it is a more accurate measurement of the underlying activity.
Claims 5, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Vieluf, Subramanian, and Ooij as applied to claims 1, 9, and 17 above, and further in view of Kalidas (US 20220015711).
Regarding claims 5, 13, and 19, while Vieluf as modified by Subramanian and Ooij disclose using a machine learning model, Vieluf as modified fails to explicitly disclose the use of a deep convolutional autoencoder network.
However, Kalidas teaches an arrhythmia analysis system that uses a convolutional autoencoder model to reduce noise (Paragraph 0061-0063), and it would have been obvious that the substitution of one known machine learning model to reduce artifacts for another yields predictable results to one of ordinary skill in the art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Vieluf, Subramanian, and Ooij to incorporate the teachings of a machine learning model of Kalidas and the results of reducing artifacts would have been predictable.
Response to Arguments
Applicant’s arguments, see page 10, filed 01/26/2026, with respect to the claim objections have been fully considered and are persuasive. Applicant has amended the claims per the suggestion of the Examiner. The objection of the claims has been withdrawn.
Applicant’s arguments, see page 11, filed 01/26/2026, with respect to the 35 U.S.C. §112(b) rejections have been fully considered and are persuasive. Applicant has amended the claims to include that determining the CNS-OT condition is based on a change between the measurement and another measurement. The rejection of the claims has been withdrawn.
Applicant's arguments, see page 11, filed 01/26/2026, with respect to the 35 U.S.C. §101 rejection have been fully considered but they are not persuasive.
Applicant asserts that a diver underwater could not perform the various steps of the apparatus in their mind. However, the conclusion is not based on whether the user of the apparatus and/or method can perform the task(s) in their mind; rather, the conclusion is based on if the abstract idea is a mental process that could be performed in the human mind, with the aid of pen and paper or a generic computer. There is no suggestion of undue complexity of receiving data, manipulating the data to determine a result, and to output the result, as described above.
Applicant further asserts that by amending the claims for the apparatus and/or method to be performed on a diver in an underwater environment, the judicial exception is integrated into a practical application. Examiner respectfully disagrees. The apparatus as claimed does not necessarily require a diver in an underwater environment for the apparatus and/or the method to work. That is, the apparatus does not have a different structure or method compared to the apparatus being worn by a user not underwater. In that scenario, the apparatus would merely come to a different conclusion regarding the determination and output of a CNS-OT condition.
The rejection above has been updated to reflect the amendments made to the claims. Upon further consideration, independent claim 17 and its dependent claims have been included in the §101 rejection.
Applicant’s arguments, see page 14, filed 01/26/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. §103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made over Vieluf and Subramanian in view of Ooij. The rejections above have been updated to reflect the amendments.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAH MICHAEL HEALY whose telephone number is (703)756-5534. The examiner can normally be reached Monday - Friday 8:30am - 5:30pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Sims can be reached at (571)272-7540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NOAH M HEALY/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791