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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10 October 2025 has been entered.
Response to Amendment
Claims 1-5, 8, 10-14, 17-20, and 23-25 are currently pending. Claims 1, 5, 10, 12, 14, and 17 have been amended. Claims 21 and 22 have been cancelled. Claims 23-25 have been added. Figs. 3a and 3b have been amended to overcome the drawing objections, claims 1, 10, and 14 have been amended to overcome the 35 U.S.C. 112(a) rejections, and claims 1, 5, 10-12, and 14 have been amended to overcome the 35 U.S.C. 112(b) rejections set forth in the Final Office Action mailed on 10 July 2025. The cancellation of claims 21 and 22 have rendered the claim objections set forth in the Final Office Action moot.
Claim Objections
Claim 1 is objected to because of the following informalities:
“a associated” in line 10 of claim 1 should read as “an associated”
Appropriate correction is required.
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 1-5, 8, 10-14, 17-20, and 23-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
STEP 1
Regarding claim 1, the claim recites a series of structural elements, including at least one of an accelerometer or gyroscope. Thus, the claim is directed to a machine, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of:
determine movement data about the user of the wearable device over a time window based on a plurality of movements measured by the at least one of the accelerometer or gyroscope related to the respiration of the user;
dynamically filter the movement data by applying a respective filter weight for each axis of the movement data with an associated bias;
determining a filter weight for each axis of the movement data according to the movement data, wherein the filter weight for each axis is determined based on a covariance of the movement data over the time window between same or different axes of the movement data;
filtering the movement data based on the respective filter weight for each axis of the movement data to obtain filtered movement data;
determining the associated bias for each axis of the movement data according to the filtered movement data;
normalizing the filtered movement data by using the associated bias determined for each axis of the movement data to obtain the dynamically filtered movement data; and
determine a respiratory signal for output regarding the respiration of the user based on the dynamically filtered movement data by removing macro-movements from the dynamically filtered movement data without regard to a position of the wearable device relative to the user or a position of the user
set forth a judicial exception. These steps describe a concept performed in the human mind or by hand (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea. The steps of dynamically filtering, filtering, and normalizing also describe a mathematical process, which is also an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites determining a respiratory signal for output regarding the respiration of the user based on the dynamically filtered movement data by removing macro-movements from the dynamically filtered movement data without regard to a position of the wearable device relative to the user or a position of the user, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The determining of the respiratory signal does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the determined respiratory signal, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional step of: at least one of an accelerometer or gyroscope that is capable of measuring movements related to respiration of a user of the wearable device. The measuring step is well-understood, routine and conventional activities for those in the field of medical diagnostics, as evidenced by Fink ‘996 (US Pub No. 2016/0353996, [0064]). Further, the measuring step is recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
The same rationale applies to claims 10 and 14.
Regarding claims 1, 10, and 14, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited at least one of an accelerometer or gyroscope is a generic sensor configured to perform pre-solutional data gathering activity and the processor is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims. Claim 2 recites that the wearable device is “free of a position sensor,” which is not significantly more (the recitation of the position sensor also performs the pre-solution activity of data gathering as the position sensor provide an orientation of the wearable device or position of the user), claims 3-5, 8, 11, 12, 17-19, and 23-25 recite steps that add to the Abstract Idea as each claim recites mental processing steps that could be performed mentally or by hand. Claims 13 and 20 recite outputting/displaying steps that are merely adding insignificant extra-solution activity to the judicial exception as the steps do not provide an improvement or effect a particular treatment. The steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 8, 10, 11, 14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Galeev et al. ‘096 (US Pub No. 2018/0256096 – previously cited) in view of De Haan et al. ‘102 (US Pub No. 2018/0333102).
Regarding claim 1, Galeev et al. ‘096 teaches a wearable device for determining a respiratory signal using motion data (Title, Abstract), comprising:
at least one of an accelerometer or gyroscope that is capable of measuring movements related to respiration of a user of the wearable device ([0026]-[0027]); and
a processor (Fig. 3 processor-based computing system 300 and [0045]) configured to:
determine movement data about the user of the wearable device over a time window based on a plurality of movements measured by the at least one of the accelerometer or gyroscope related to the respiration of the user ([0011]; “monitoring devices described herein may utilize the one or more accelerometers to monitor a user's respiration rate (i.e., breaths/minute) by counting the number of breaths taken by a user in a predetermined time frame (e.g., 5 seconds, 30 seconds, 5 minutes, 30 minutes, etc.)”);
dynamically filter the movement data by applying a respective filter weight ([0059]; “noise filters”) for each axis of the movement data ([0057]-[0059]) with an associated bias ([0057], [0059]; “magnify” is interpreted as an associated bias. It is noted that the filed specification of the current application does not have a clear definition for “bias.”), further comprising:
determining a filter weight for each axis of the movement data according to the movement data ([0059]; “noise filters”);
filtering the movement data based on the respective filter weight for each axis of the movement data to obtain filtered movement data ([0059]; “noise filters”);
determining the associated bias for each axis of the movement data according to the filtered movement data ([0057]-[0059]; “magnify”);
normalizing the filtered movement data by using the associated bias determined for each axis of the movement data to obtain the dynamically filtered movement data ([0058]; “…the output of each axis of the accelerometer can be assessed and the clearest signal (relatively higher amplitudes, relatively stable frequencies) can be selected for respiratory analysis.”); and
determine a respiratory signal for output regarding the respiration of the user based on the dynamically filtered movement data (Fig. 7 and [0061]) by removing macro-movements from the dynamically filtered movement data (Fig. 5 and [0059]; “Fig. 5 depicts what such a single-axis output looks like after it has been magnified and smoothed using a smoothing filter…and noise has been removed…”) without regard to a position of the wearable device relative to the user or a position of the user (Galeev et al. ‘096 does not mention a position sensor, indicating that the device of Galeev et al. ‘096 is free of a position sensor that provides a position of the wearable device relative to the user or a position of the user.), wherein the respiratory signal comprises at least one of a respiration rate or a respiration type of the user ([0006]-[0007]).
Galeev et al. ‘096 teaches all of the elements of the current invention as mentioned above except for wherein the filter weight for each axis is determined based on a covariance of the movement data over the time window between same or different axes of the movement data.
De Haan et al. ‘102 teaches “the weighted combination has a covariance with the individual detection signals that corresponds, as closely as possible, to a predefined vector…” ([0042]).
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 filter weight for each axis of Galeev et al. ‘096 to include being determined based on a covariance of the movement data over the time window between same or different axes of the movement data as De Haan et al. ‘102 teaches that this would increase signal quality ([0013]).
Regarding claim 2, Galeev et al. ‘096 teaches wherein the wearable device is free of a position sensor that provided an orientation of the wearable device on the user or a position of the user (Galeev et al. ‘096 does not mention a position sensor, indicating that the device of Galeev et al. ‘096 is free of a position sensor that provides a position of the wearable device relative to the user or a position of the user.).
Regarding claim 3, Galeev et al. ‘096 teaches wherein the movements measured by the at least one of the accelerometer or gyroscope are micro-movements ([0008]; “micro-motions”) and the processor is configured to filter out macro-movements when the data is dynamically filtered ([0059]; “Fig. 5 depicts what such a single-axis output looks like after it has been magnified and smoothed using a smoothing filter…and noise has been removed…”).
Regarding claim 4, Galeev et al. ‘096 teaches wherein the movements measured by the at least one of the accelerometer or gyroscope include movement data with three axis components within a coordinate system ([0042]; “one or more three-axis accelerometers”).
Regarding claim 5, Galeev et al. ‘096 teaches wherein the process configured to dynamically filter the movement data by applying the respective filter weight for each axis of the movement data with the associated bias is further configured to:
apply filter weights to each axis component of the movement data with three axis components during the dynamically filtering of the movement data ([0058]; “…the output of each axis of the accelerometer can be assessed and the clearest signal (relatively higher amplitudes, relatively stable frequencies) can be selected for respiratory analysis.”) so that the micro-movements provide the respiratory signal without the position of the wearable device relative to the user or the position of the user being monitored (Galeev et al. ‘096 does not mention a position sensor, indicating that the device of Galeev et al. ‘096 is free of a position sensor that provides a position of the wearable device relative to the user or a position of the user.).
Regarding claim 8, Galeev et al. ‘096 teaches wherein the processor is configured to determine and output an indication of a type of breathing based upon the respiration signal ([0029]; “…a sleep event can be detected by a respiration signal that increases or decreases in amplitude, wavelength, or some other characteristic beyond a threshold.”).
Regarding claim 10, Galeev et al. ‘096 teaches a wearable device for determining a respiratory signal using motion data (Title, Abstract), comprising:
at least one of an accelerometer or gyroscope that is capable of measuring movements related to respiration of a user of the wearable device ([0026]-[0027]); and
a processor (Fig. 3 processor-based computing system 300 and [0045]) configured to:
determine movement data about the user of the wearable device over a time window based on a plurality of movements measured by the at least one of the accelerometer or gyroscope related to the respiration of the user ([0011]; “monitoring devices described herein may utilize the one or more accelerometers to monitor a user's respiration rate (i.e., breaths/minute) by counting the number of breaths taken by a user in a predetermined time frame (e.g., 5 seconds, 30 seconds, 5 minutes, 30 minutes, etc.)”);
determine a filter weight and associated bias for each of the plurality of movements over the current time window, wherein a respective filter weight is determined for each axis of the movement data according to the movement data ([0059]; “noise filters”);
dynamically filter the movement data based on a signal-to-noise ratio by applying a respective filter weight ([0059]; “noise filters”) for each axis of the movement data ([0057]-[0059]) with an associated bias ([0057], [0059]; “magnify” is interpreted as an associated bias. It is noted that the filed specification of the current application does not have a clear definition for “bias.”), further comprising:
apply the respective filter weight for each axis of the movement data ([0059]; “noise filters”) and
reweigh the movement data using the associated bias determined for the current time window ([0059]; Fig. 5 depicts what such a single-axis output looks like after it has been magnified and smoothed using a smoothing filter…and noise has been removed…”; [0057]; “magnify”);
determine a respiratory signal for output regarding the respiration of the user based on the dynamically filtered movement data (Fig. 7 and [0061]) by removing macro-movements from the dynamically filtered movement data (Fig. 5 and [0059]; “Fig. 5 depicts what such a single-axis output looks like after it has been magnified and smoothed using a smoothing filter…and noise has been removed…”) without regard to a position of the at least one of the accelerometer or the gyroscope or a position of the user (Galeev et al. ‘096 does not mention a position sensor, indicating that the device of Galeev et al. ‘096 is free of a position sensor that provides a position of the at least one of the accelerometer or the gyroscope or a position of the user.); and
display an output based upon the respiratory signal (Fig. 1 and [0032]), wherein the respiratory signal comprises at least one of a respiration rate or a respiration type of the user ([0011]; “monitoring devices described herein may utilize the one or more accelerometers to monitor a user's respiration rate (i.e., breaths/minute) by counting the number of breaths taken by a user in a predetermined time frame (e.g., 5 seconds, 30 seconds, 5 minutes, 30 minutes, etc.)”).
Galeev et al. ‘096 teaches all of the elements of the current invention as mentioned above except for wherein the filter weight for each axis is determined based on a covariance of the movement data over the time window between same or different axes of the movement data.
De Haan et al. ‘102 teaches “the weighted combination has a covariance with the individual detection signals that corresponds, as closely as possible, to a predefined vector…” ([0042]).
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 filter weight for each axis of Galeev et al. ‘096 to include being determined based on a covariance of the movement data over the time window between same or different axes of the movement data as De Haan et al. ‘102 teaches that this would increase signal quality ([0013]).
Regarding claim 11, Galeev et al. ‘096 teaches wherein the non-transitory computer-readable medium comprising the program configured to apply the respective filter weight for each axis of the movement data is further configured to apply filter weights to each of three axis components of the movement data during the dynamically filtering of the movement data ([0058]; “…the output of each axis of the accelerometer can be assessed and the clearest signal (relatively higher amplitudes, relatively stable frequencies) can be selected for respiratory analysis.”) so that micro-movements monitored by at least one of the accelerometer or the gyroscope provide the respiratory signal without knowing a position of the wearable device relative to the user or the position of the user being monitored (Galeev et al. ‘096 does not mention a position sensor, indicating that the device of Galeev et al. ‘096 is free of a position sensor that provides a position of the at least one of the accelerometer or the gyroscope or a position of the user.).
Regarding claim 14, Galeev et al. ‘096 teaches a method for determining a motion-related respiratory signal using a wearable device comprising at least one of an accelerometer or gyroscope (Title, Abstract), the method comprising:
determining, by the wearable device, movement data about a user of the wearable device over a current time window based on a plurality of movements measured by the at least one of the accelerometer or gyroscope of the wearable device related to the respiration of the user ([0011]; “monitoring devices described herein may utilize the one or more accelerometers to monitor a user's respiration rate (i.e., breaths/minute) by counting the number of breaths taken by a user in a predetermined time frame (e.g., 5 seconds, 30 seconds, 5 minutes, 30 minutes, etc.)”);
determining a filter weight and associated bias for each of the plurality of movements over the current time window, wherein a respective filter weight is determined for each axis of the movement data and the associated bias according to the movement data ([0059]; “noise filters”);
dynamically filtering the movement data by applying a respective filter weight ([0059]; “noise filters”) for each axis of the movement data ([0057]-[0059]) with the associated bias ([0057], [0059]; “magnify” is interpreted as an associated bias. It is noted that the filed specification of the current application does not have a clear definition for “bias.”), further comprising:
applying the respective filter weight for each axis of the movement data ([0059]; “noise filters”); and
reweighing the movement data using the associated bias determined for the current time window ([0059]; Fig. 5 depicts what such a single-axis output looks like after it has been magnified and smoothed using a smoothing filter…and noise has been removed…”; [0057]; “magnify”); and
determining a respiratory signal for output regarding the respiration of the user based on the dynamically filtered movement data (Fig. 7 and [0061]) without regard to a position of the at least one of the accelerometer or the gyroscope or a position of the user (Galeev et al. ‘096 does not mention a position sensor, indicating that the device of Galeev et al. ‘096 is free of a position sensor that provides a position of the at least one of the accelerometer or the gyroscope or a position of the user.); and
Galeev et al. ‘096 teaches all of the elements of the current invention as mentioned above except for wherein the movement data further comprises covariance of the movement data derived from the plurality of movements measured over the current time window between same or different axes of movement as measured by the wearable device.
De Haan et al. ‘102 teaches “the weighted combination has a covariance with the individual detection signals that corresponds, as closely as possible, to a predefined vector…” ([0042]).
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 movement data of Galeev et al. ‘096 to include covariance of the movement data derived from the plurality of movements measured over the current time window between same or different axes of movement as measured by the wearable device as De Haan et al. ‘102 teaches that this would increase signal quality ([0013]).
Regarding claim 17, Galeev et al. ’096 teaches categorizing an output after the movement data is reweighed ([0029]; “Detected respiration rates and respiration signals can also be analyzed to detect the presence of a sleep event, such as sleep apnea or respiratory arrest. In one embodiment, a sleep event can be detected by a respiration rate that accelerates or decelerates beyond a threshold. In some embodiments, a sleep event can be detected by a respiration signal that increases or decreases in amplitude, wavelength, or some other characteristic beyond a threshold.”) or plotting the output to form a graphical representation of the respiratory signal (Fig. 7).
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Galeev et al. ‘096 in view of De Haan et al. ‘102 further in view of Chung ‘909 (KR Pub No. 2018/0117909 – previously cited).
Regarding claim 12, Galeev et al. ‘096 in view of De Haan et al. ‘102 teaches all of the elements of the current invention as mentioned above except for wherein the non-transitory computer-readable medium comprising the program to reweigh the movement data using the associated bias determined for the current time window is further configured to reweigh the movement data from the at least one of the accelerometer or gyroscope so that a combined output of the three axis components is based upon the filter weights, and each filter weight is a whole number, a percentage, a fraction of an integer, a floating number, or a combination thereof.
Chung ‘909 teaches by attaching a wearable terminal body with a built-in MEMS sensor to the body of the subject, it detects the number of abdominal movements (breathing cycles) during sleep without disturbing the sleep of the subject, and by acquiring the breathing cycle information detected by the wearable terminal body to a smart device using short-range communication and outputting the number of abdominal movements (breathing cycles) of the subject on the screen based on the breathing cycle information, it provides the effect of allowing a monitor who is certain distance away from the subject to check the current breathing (abdominal movement) state of the subject in real time ([0023]). [0026] mentions that the MEMs sensor measured the X, Y, and Z axes of the gyro sensor and the values of the X, Y, and Z axes of the accelerometer.
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 program of Galeev et al. ‘096 in view of De Haan et al. ‘102 to include reweighing the movement data using the associated bias determined for the current time window is further configured to reweigh the movement data from the at least one of the accelerometer or gyroscope so that a combined output of the three axis components is based upon the filter weights, and each filter weight is a whole number, a percentage, a fraction of an integer, a floating number, or a combination thereof as Chung ‘909 teaches that this will allow a monitor to check the current breathing in real time.
Regarding claim 13, Galeev et al. ‘096 teaches wherein the output is a plot of all of the combined outputs that are reweighed so that a graph is created that illustrates the respiratory signal (Fig. 7).
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Galeev et al. ‘096 in view of De Haan et al. ‘102 further in view of Maeta ‘654 (US Pub No. 2020/0279654 – previously cited).
Regarding claim 18, Galeev et al. ‘096 in view of De Haan et al. ‘102 teaches all of the elements of the current invention as mentioned above except for wherein the movement data includes movement data with three axis components within a coordinate system, and the method further comprises:
determining a numerical score for the respiratory signal regarding the respiration of the user, wherein an abnormal respiratory event is determined for the user based on the numerical score.
Maeta ‘654 teaches a “normal range” and an “abnormal range” are from the measurement values of vital signs of a group, as seen in Table 1. The range configured here can be changed in consideration of the area, age, etc., but basically, the range used as a reference is determined based on the measurement values of the vital signs obtained from the majority of the number of people. The configuration of the reference is the same for respiratory rate, oxygen saturation, blood pressure, and heart rate ([0010]). In addition, when the vital information has a measurement value of the respiratory rate, it is possible to obtain score result information on the respiratory rate measured from the same individual to determine whether it is an abnormal value ([0061]).
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 method of Galeev et al. ‘096 in view of De Haan et al. ‘102 to include determining a numerical score for the respiratory signal regarding the respiration of the user, wherein an abnormal respiratory event is determined for the user based on the numerical score as Maeta ‘654 teaches that this will aid in making it more efficient to deal with post-judgement treatment ([0070]).
Regarding claim 19, Galeev et al. ‘096 teaches applying filter weights to each axis component of the movement data with three axis components during the dynamically filtering of the movement data ([0058]; “…the output of each axis of the accelerometer can be assessed and the clearest signal (relatively higher amplitudes, relatively stable frequencies) can be selected for respiratory analysis.”) so that micro-movements monitoring the at least one of the accelerometer or gyroscope provide the movement data that is converted into a respiratory signal without requiring knowledge of a position of the wearable device relative to the user or a position of the user and without the position of the wearable device relative to the user or the position of the user being monitored (Galeev et al. ‘096 does not mention a position sensor, indicating that the device of Galeev et al. ‘096 is free of a position sensor that provides a position of the wearable device relative to the user or a position of the user.).
Regarding claim 20, Galeev et al. ‘096 teaches displaying an output demonstrating the respiratory signal (Fig. 7).
Response to Arguments
35 U.S.C. 101
Applicant argues that the amended claims tie the steps to a physical sensor/device and are not Abstract Idea. Examiner respectfully disagrees, as according to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Applicant argues that the claims are integrated into a practical application that “improves accuracy and robustness of respiratory detection…, eliminate reliance on positional sensors…, and produce a tangible physiological output.” However, the claims do not recite how determining a respiratory signal would provide any of these improvements. There is no change to the technology based on the determined respiratory signal nor is there any improvement based on the determined respiratory signal. Examiner suggests to include more details as to how these steps in the independent claims provide these improvements.
Applicant argues that the combination of hardware and algorithmic steps are significantly more than the abstract idea. Examiner respectfully disagrees, as the hardware, the at least one accelerometer or gyroscope, is a generic sensor configured to perform pre-solution activity of data gathering. Furthermore, according to the Berkheimer analysis, the at least one accelerometer or gyroscope is well-understood, routine, and conventional, as evidenced by Fink ‘996. As such, Applicant’s arguments are not persuasive and the 35 U.S.C. 101 rejection has been maintained.
35 U.S.C. 102(a)(1)
Applicant’s arguments with respect to the 35 U.S.C. 102(a)(1) rejections (“…based on a covariance of the movement data over time…”) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues that Galeev et al. ‘096 does not teach the claimed “associated bias.” However, is noted that the filed specification of the current application does not have a clear definition for “bias.” [0084] of the PGPUB of the current application recites that the axis bias may “increase an amplitude…, cause a phase shift…, shift the respiratory signal up, shift the respiratory signal down, normalize the respiratory signal, or a combination thereof.” To best to the Examiner’s understanding, “bias” is interpreted as “magnifying” as magnifying a signal would increase an amplitude of a respiratory signal, as mentioned in [0047] and Fig. 4C of Galeev et al. ‘096.
Applicant argues that Galeev et al. ‘096 does not teach the “normalizing” step as claimed. However, the selection of the clearest signals as mentioned in [0058] of Galeev et al. ‘096 is interpreted as the “normalizing” step. It is noted that Applicant has failed to explain why this section of Galeev et al. ‘096 does not teach the “normalizing” step.
Applicant argues that Galeev et al. ‘096 does not teach “without regard to a position of the wearable device relative to the user or a position of the user.” However, as previously mentioned, Galeev et al. ‘096 does not mention a position sensor, indicating that the device of Galeev et al. ‘096 is free of a position sensor that provides a position of the wearable device relative to the user or a position of the user. As such, Applicant’s arguments are not persuasive.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AURELIE H TU whose telephone number is (571)272-8465. The examiner can normally be reached [M-F] 7:30-3:30.
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/AURELIE H TU/ Primary Examiner, Art Unit 3791