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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 May 28, has been entered. The Examiner acknowledges the amendments made to claims 1-14 and 16-18, the cancellation of claim 15, and the addition of claim 24. Claims 1-14 and 16-24 are currently pending.
Response to Arguments
While Applicant’s arguments, see remarks, filed May 28, 2025, have overcome some of the previous rejections of the claims under 35 USC 112(b), Applicant’s amendments have introduced new indefiniteness issues into the claim, wherein in claims 1, 16 & 24 it is unclear whether there are one or three top cycles determined in the method, as the claim first recites that at least a top three cycles are determined based on the three cycles assumed present in the data, and later recites that a cycle of the at least three cycles is selected as a top cycle based on amplitude thresholds, repeatability of periodicity or both. See 35 USC 112(b) rejections below.
Regarding the rejection of the claims under 35 USC 101, Applicant argues that the Office has failed to establish a prima facie rejection under the substantive law as interpreted by judicial precedent, as the Examiner provides only conclusory statements without any explanation as to why the additional elements do not integrate the judicial exception into a practical application. The Examiner respectfully disagrees. In the previous Office action, the Examiner noted that there are no additional elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. As defined in MPEP 2106.04(d) I. Relevant Considerations for Evaluating whether Additional Elements integrate a Judicial Exception into a Practical Application, it is stated that an additional element is integrated into a practical application if the additional element provides an improvement in the functioning of a computer, or an improvement to other technology or technical field, if it applies or uses the judicial exception to effect a particular treatment, if it implements a judicial exception with, or uses a judicial exception in conjunction with a particular machine or manufacture, if it effects a transformation or reduction of a particular article to a different state or thing, or if it applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Further MPEP 2106.04(d) I. states that limitations that do not integrate a judicial exception into a practical application include merely reciting the words “apply it” or an equivalent, adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)), and/or generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2105.05(h)). Under such evaluation, the additional elements do not integrate the judicial exception into a practical application because the receiving, performing, identifying, determining, approximating, projecting, checking, and estimating steps recited in the claims are each 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. Applicant asserts that the Office’s previous response states that “the claimed invention does not have a practical application” without providing any supporting explanation, but as previously explained, the claimed invention merely recites receiving accelerometer and gyroscope data, performing partial cycle analysis to determine a top three cycles, performing a periodicity transform based selection of at least one top periodicity observed from the partial cycle analysis, and determining a most likely activity of the at least one top periodicity using heuristics, and as such, the claimed invention does not have a practical application (i.e., a specific and/or particular effect or outcome), as the microprocessor merely makes a determination/estimation but there is nothing done with the product of that determination/estimation. Additionally, the claims recite insignificant extra-solution activity and the computer component used is recited at a high level of generality under the Revised Step 2B analysis and thus do not amount to significantly more. The claims are directed to an abstract idea (i.e., mental processes) without significantly more, do not provide an improvement in the technical field as alleged, as Applicant has not provided sufficient evidence of any alleged improvement, and are thus not integrated into a practical application. See 35 USC 101 Rejection below.
Regarding Applicant’s arguments that the rejection of the claims under 35 USC 103 over “Gowda” as modified by “Sethares” is improper because neither Gowda nor Sethares teaches approximating cycle lengths and amplitudes for at least three cycles assumed present in the data, nor teaches using cycle lengths, amplitudes, or periodicity of a motion signal collected by accelerometer or gyroscope data, the Examiner respectfully disagrees.
The Examiner would like to restate that in the claim limitation of “determining the at least top three cycles based on the at least three cycles assumed present in the data based on the fractional cycle markers” as recited in claim 1, the detection of only “at least three cycles” means that each of those three cycles are in the “at least top three” [emphasis added].
Gowda teaches performing analysis of data received from accelerometer and gyroscope sensors to determine a motion signal or signature, wherein the accelerometer and gyroscope sensors generate data samples that represent motion for a plurality of time intervals (i.e., at least three cycles) (see Gowda, par 0032-0039, fig. 1C). Furthermore, Gowda teaches using spectra estimators that perform Fast Fourier Transform (FFT) and peak extraction of data received from the accelerometer and gyroscope sensors that can be used to extract motion signatures to determine an activity that a user is performing (i.e., cycles indicative of motion signatures are determined based upon peak extraction which encompasses cycle lengths and amplitudes) (see Gowda, par 0032, fig. 1C). Moreover, Gowda teaches analysis is performed to determine a motion signal or signature received from the accelerometer and gyroscope sensors, wherein the motion sensors (accelerometer and gyroscope) generate data samples that represent motion for a plurality of time intervals, thus periodicity of the accelerometer and gyroscope data are approximated to determine the motion signals or signatures (see Gowda, par 0032-0039, fig. 1C). Furthermore, since the claims recite determining at least a top three cycles and Gowda teaches determining a plurality of cycles in the accelerometer and gyroscope data, Gowda thus teaches determining at least a top three cycles since Gowda teaches determining a plurality of top motion signals or signatures for a plurality of time intervals based upon spectra analysis. That being said, the Examiner agrees that Gowda as modified by Sethares fails to teach that a cycle is selected as a top cycle based on amplitude thresholds, repeatability of periodicity, or both. Therefore, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art that reads on the newly added claim limitations. See 35 USC 103 rejections below.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 & 24 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 “determining the at least top three cycles based on the at least three cycles assumed present in the data based on the fractional cycle markers, wherein a cycle of the at least three cycles is selected as a top cycle based on amplitude thresholds, repeatability of periodicity or both” at lines 13-16. It is unclear whether there are one or three top cycles determined in the method, as the claim first recites that at least top three cycles are determined based on the three cycles assumed present in the data, and later recites that a cycle of the at least three cycles is selected as a top cycle based on amplitude thresholds, repeatability of periodicity or both (i.e., are there three top cycles determined based on the three cycles assumed present in the data or is there only one top cycle that is determined to be a top cycle from the previously determined three top cycles based on amplitude thresholds, repeatability of periodicity or both?). The Examiner respectfully requests clarification. For examination purposes, it will be interpreted that three cycles assumed present in the data are interpreted as top cycles based upon amplitude thresholds, repeatability of periodicity or both. Claims 16 & 24 are similarly rejected and interpreted.
Dependent claims are similarly rejected and interpreted as their base claim.
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 1-23 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 18 follows.
Regarding claim 18, the claim recites a series of steps or acts, including determining a most likely activity of a user based on an at least one top periodicity using heuristics. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of determining a most likely activity of a user based on an at least one top periodicity using heuristics sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
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 steps of receiving accelerometer data, receiving gyroscope data, performing partial cycle analysis using a microprocessor of a wearable device, identifying waveform peaks, waveform valleys and zero crossing, determining at least top three cycles, approximating an expected periodicity, performing a periodicity transform, projecting data onto a periodicity subspace, checking a projection, and estimating a most likely cycle length. The receiving, performing, identifying, determining, approximating, projecting, checking, and estimating steps are each 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 receiving, performing, identifying, determining, approximating, projecting, checking, and estimating 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)).
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. Therefore, the claim does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change, nor does the method use a particular machine to perform the Abstract Idea.
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.
Regarding claims 1 & 16, the device recited in each claim is a generic device comprising generic components configured to perform the abstract idea. The recited accelerometer and gyroscope are generic sensors configured to perform pre-solutional data gathering activity, and the microprocessor 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 as they generally recite method steps pertaining to data gathering and data processing. The receiving, performing, identifying, determining, approximating, projecting, checking, and estimating 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
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.
Claim(s) 1-14 & 18-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 20190053754--as previously cited--, hereinafter referenced as "Gowda", in view of Non-Patent Literature Document "Periodicity transforms" --as previously cited --, hereinafter referenced as "Sethares", and in further view of US Patent Application Publication 20160161281, hereinafter referenced as “Schuijers”.
With respect to claims 1 & 18, Gowda teaches a body-worn device 100 comprising (see Gowda, fig. 1A):
an accelerometer configured to receive accelerometer data (see Gowda, par 0024-0025, fig. 1A);
a gyroscope configured to receive gyroscope data (see Gowda, par 0024-0025, fig. 1A);
and a microprocessor 120 configured to:
perform partial cycle analysis to determine at least a top three cycles based on at least one of the accelerometer data and gyroscope data (i.e., analysis is performed to determine a motion signal or signature received from the accelerometer and gyroscope sensors, wherein the motion sensors (accelerometer and gyroscope) generate data samples that represent motion for a plurality of time intervals) (see Gowda, par 0032-0039, fig. 1C), wherein the partial cycle analysis comprises:
identifying in the at least one of the accelerometer data or gyroscope data one or more fractional cycle markers, the fractional cycle markers including at least one of waveform peaks, waveform valleys, and zero crossings (i.e., waveform peaks are identified from the motion signature signal to determine the activity the user is performing) (see Gowda, par 0032, fig. 1C);
approximating cycle lengths, CL, and amplitudes, A, of at least three cycles assumed present in the data based on the fractional cycle markers (see Gowda, par 0032, fig. 1C),
determining the at least top three cycles based on the at least three cycles assumed present in the data based on the fractional cycle markers (see Gowda, par 0032, fig. 1C);
approximating an expected periodicity, P, of each cycle of the at least top three cycles based on the cycle lengths, CL (i.e., analysis is performed to determine a motion signal or signature received from the accelerometer and gyroscope sensors, wherein the motion sensors (accelerometer and gyroscope) generate data samples that represent motion for a plurality of time intervals) (see Gowda, par 0032-0039, fig. 1C);
and determine a most likely activity of the at least one top periodicity using heuristics (i.e., determining an activity that the motion signature is representative of) (see Gowda, par 0032, fig. 1C).
Gowda fails to teach a cycle of the at least three cycles is selected as a top cycle based on amplitude thresholds, repeatability of periodicity, or both, performing a periodicity transform (PT)-based selection of at least one top periodicity observed from the partial cycle analysis, the PT-based selection comprising, for each of the expected periodicities, P, of the three cycles, projecting the data onto a periodicity subspace for the respective expected periodicity, P, checking if the projection includes at least a threshold, T, percentage of a total energy in a signal comprised by the data, and if so accepting the cycle to which the periodicity corresponds as a possible cycle present in the data, and estimating a most likely cycle length based on the cycle lengths and amplitudes of the identified possible cycles.
Sethares teaches performing a periodicity transform (PT)-based selection of at least one top periodicity observed from the partial cycle analysis, the PT-based selection comprising, for each of the expected periodicities, P, of the three cycles (i.e., using PT to find the best periodic characterization of the length of the signal) (see Sethares, page 2956, section V):
projecting the data onto a periodicity subspace for the respective expected periodicity, P (see Sethares, page 2953-2955, sections II & III);
checking if the projection includes at least a threshold, T, percentage of a total energy in a signal comprised by the data, and if so accepting the cycle to which the periodicity corresponds as a possible cycle present in the data (see Sethares, page 2957, small to large algorithm used to determine if there were significant periodicities within a threshold);
and estimating a most likely cycle length based on the cycle lengths and amplitudes of the identified possible cycles (see Sethares, page 2956, section V);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gowda such that it is configured to execute performing a periodicity transform (PT)-based selection of at least one top periodicity observed from the partial cycle analysis, the PT-based selection comprising, for each of the expected periodicities, P, of the three cycles, projecting the data onto a periodicity subspace for the respective expected periodicity, P, checking if the projection includes at least a threshold, T, percentage of a total energy in a signal comprised by the data, and if so accepting the cycle to which the periodicity corresponds as a possible cycle present in the data, and estimating a most likely cycle length based on the cycle lengths and amplitudes of the identified possible cycles because it would improve the system of Gowda by permitting the system to locate periodicities within data that can provide a clearer examination of the underlying nature of the signals that can be used to make analyses (see Sethares, pages 2961-2962, section VII).
Gowda as modified by Sethares fails to teach a cycle of the at least three cycles is selected as a top cycle based on amplitude thresholds, repeatability of periodicity, or both.
Schuijers teaches a device, method, and system for counting the number of cycles of a periodic movement of a subject wherein discontinuous accelerometer data is received by the device using an accelerometer (see Schuijers, par 0046, 0050) and the discontinuous accelerometer data is analyzed to determine its periodicity using known/predetermined periodicities stored in a reference table, and based on the determined periodicity of the signal, the periodicity is used to obtain the number of cycles of the signal during a time period (see Schuijers, par 0023-0027). This data can be used to classify the activity of the periodic movement of a subject, such as walking, running, or cycling, as well as the speed by which they are completing the activity (i.e., fast, slow, no movement) (see Schuijers, par 0023).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gowda as modified by Sethares such that a cycle of the at least three cycles is selected as a top cycle based on amplitude thresholds, repeatability of periodicity, or both, because identifying a cycle based on its repeatability of periodicity permits the identification/classification of an activity the subject is executing (see Schuijers, par 0023-0027).
The system of Gowda as modified by Sethares and Schuijers is configured to execute the method as recited in claim 18.
With respect to claims 2 & 20, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 1 and method of claim 18, and Gowda further teaches the most likely activity comprises at least one of: sudden motion, sustained motion, slow chest wall movement, deep chest wall movement, and vigorous vibration (i.e., sustained motion of a physical activity such as walking, biking, swimming, etc., or when a user was previously stationary but has begun to move) (see Gowda, par 0032, 0035, 0050).
With respect to claims 3 & 21, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 1, and Gowda in view of Sethares and Schuijers further teaches the PT-based selection is performed by:
(a) assuming a threshold (T) (see Sethares, page 2957, small to large algorithm);
(b) obtaining signal X of length N via at least one of: the accelerometer data and the gyroscope data (see Gowda, par 0024-0025, fig. 1A);
(c) locating a fractional cycle marker for each cycle of the at least top three cycles (i.e., waveform peaks are identified from the motion signature signal to determine the activity the user is performing) (see Gowda, par 0032, fig. 1C);
(d) approximating a cycle length (CLi) and an amplitude (Ai) for each cycle, wherein i is a cycle (i.e., using PT to find the best periodic characterization of the length of the signal) (see Sethares, page 2956-2957, section V);
(e) approximating the expected periodicities for each cycle (i.e., using PT to find the best periodic characterization of the length of the signal) (see Sethares, page 2956-2957, section V);
(f) relating the expected periodicities to the fractional cycle marker for each cycle;
(g) letting an expected periodicity equal to one (see Sethares, page 2960, section B. signal separation);
(h) removing the linear DC component by removing the projection onto the first expected periodicity equal to one (see Sethares, page 2956-2957, section V);
(i) letting the expected periodicity equal CL 1 (see Sethares, page 2956-2957, section V);
(j) determining whether Al contains at least T percentage of energy in X (see Sethares, page 2957, small to large algorithm);
(k) if Al contains at least T percentage of energy in X, accepting Al and CL1 as a possible cycle (see Sethares, page 2957, small to large algorithm);
(l) repeating steps (i)-(k) for each CLi and A (see Sethares, page 2957, small to large algorithm)i;
and (m) estimating a most likely cycle length based on each cycle length (CLi) and an amplitude (Ai) for each cycle (see Sethares, page 2956, section V).
With respect to claim 4, Gowda in view of Sethares teaches the device of claim 3, and Gowda in view of Sethares further teaches if adjacent periodicities show dominant characteristics due to a variability inside the measurement interval, periodicity bands are formed and a center periodicity inside a band is nominated as the most likely cycle length (see Sethares, page 2961, section C. patterns in astronomical data).
With respect to claim 5, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 1, and Gowda further teaches heuristics is based on at least one of: a particular amplitude range, a particular cycle length, data indicative of continuity, data indicative of a duration of continuity, data indicative of a periodic nature, data indicative of what channels of sensors a signal is dominant in, and a degree of correlation with measurements made in silent intervals (i.e., the determination of breathing condition is based upon the periodic accelerations that can occur during respiration) (see Gowda, par 0046).
With respect to claim 6, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 1, and Gowda further teaches the microprocessor is further configured to measure a signal quality index (SQI) for an incoming signal associated with at least one of the accelerometer data and gyroscope data using the presence or absence of periodicity information associated with the partial cycle analysis (i.e., a signal quality of the sensors is determined and is used to determine whether or not determinations should be used using the signal) (see Gowda, par 0071).
With respect to claim 7, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 1, and Gowda further teaches the accelerometer and gyroscope are housed in a wearable device (see Gowda, fig. 1B, par 0028-0029).
With respect to claim 8, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 7, and Gowda further teaches the wearable device is configured to be worn on a patient's chest (i.e., sensors of the wearable device may be placed on a chest of the user while the wearable device is worn by the user) (see Gowda, par 0028).
With respect to claim 9, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 1, and Gowda further teaches the accelerometer data is received on-demand (i.e., accelerometer data is received for a plurality of time intervals as the user wears the wearable device) (see Gowda, par 0039).
With respect to claim 10, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 1, and Gowda further teaches the gyroscope data is received on-demand (i.e., accelerometer data is received for a plurality of time intervals as the user wears the wearable device) (see Gowda, par 0039).
With respect to claim 11, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 6, wherein sudden changes in SQI for a duration less than a first SQI threshold indicate a posture change (i.e., thresholds are adjusted based upon the signal quality, and determine breathing disturbance scores that is indicative of the activity of a user) (see Gowda, par 0047-0049, 0071) .
With respect to claim 12, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 1, wherein the microprocessor is further configured to determine a signal quality index (SQI) associated with the most likely activity (see Gowda, par 0032, 0035, 0050).
With respect to claim 13, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 1, wherein the heuristics comprise at least one of: a maximum power cycle length associated with the accelerometer data, a maximum power cycle length associated with the gyroscope data, a mean power cycle length associated with the accelerometer data, a mean power cycle length associated with the gyroscope data, a maximum significant estimated cycle length associated with the accelerometer data, a maximum significant estimated cycle length associated with the gyroscope data (i.e., the determination of breathing condition is based upon the motion signature spectra and extracted peaks) (see Gowda, par 0032).
With respect to claim 14, Gowda in view of Sethares and Schuijers teaches the body-worn device of claim 1, and Gowda further teaches the partial cycle analysis is performed by continuously mapping cycles onto regions of maximum amplitudes, minimum amplitudes, and zero crossing intervals and their periodicities (i.e., analysis is performed to determine a motion signal or signature received from the accelerometer and gyroscope sensors, wherein the motion sensors (accelerometer and gyroscope) generate data samples that represent motion for a plurality of time intervals, and the data is analyzed by estimating the spectra of the data and by extracting peaks) (see Gowda, par 0032-0039, fig. 1C).
With respect to claim 19, Gowda in view of Sethares and Schuijers teaches the method of claim 18, and Gowda further teaches the most likely activity is respiration and the top periodicity is a respiration rate (see Gowda, par 0026, 0046).
With respect to claim 22, Gowda in view of Sethares and Schuijers teaches the method of claim 21, and Gowda further teaches the ratios of amplitudes of most likely cycle length indicate a signal quality index (see Sethares, page 2960, section B. signal separation).
With respect to claim 23, Gowda in view of Sethares and Schuijers teaches the method of claim 21, and further teaches the amplitudes of most likely cycle lengths and a presence of each amplitude in relation to a region of interest determine a signal quality index for each estimation (i.e., a signal quality of the sensors is determined and is used to determine whether or not determinations should be used using the signal) (see Sethares, page 2960, section B. signal separation).
With respect to claim 24, Gowda in view of Sethares and Schuijers teaches the method of claim 18 and further teaches a cycle of the at least three approximated cycles is selected as a top cycle based on at least one of amplitude thresholds, repeatability of periodicity, and a valid signal quality index (i.e., discontinuous accelerometer data is analyzed to determine its periodicity using known/predetermined periodicities stored in a reference table, and based on the determined periodicity of the signal, the periodicity is used to obtain the number of cycles of the signal during a time period) (see Schuijers, par 0023-0027).
Claim(s) 16 & 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 20190053754--as previously cited--, hereinafter referenced as "Gowda", in view of Non-Patent Literature Document "Periodicity transforms" --as previously cited --, hereinafter referenced as "Sethares".
With respect to claim 16, Gowda teaches a body-worn device 100 comprising (see Gowda, fig. 1A):
an accelerometer configured to receive accelerometer data (see Gowda, par 0024-0025, fig. 1A);
a gyroscope configured to receive gyroscope data (see Gowda, par 0024-0025, fig. 1A);
and a microprocessor 120 configured to:
perform partial cycle analysis to determine at least a top three cycles based on at least one of the accelerometer data and gyroscope data (i.e., analysis is performed to determine a motion signal or signature received from the accelerometer and gyroscope sensors, wherein the motion sensors (accelerometer and gyroscope) generate data samples that represent motion for a plurality of time intervals) (see Gowda, par 0032-0039, fig. 1C), wherein the partial cycle analysis comprises:
identifying in the at least one of the accelerometer data or gyroscope data one or more fractional cycle markers, the fractional cycle markers including at least one of waveform peaks, waveform valleys, and zero crossings (i.e., waveform peaks are identified from the motion signature signal to determine the activity the user is performing) (see Gowda, par 0032, fig. 1C);
approximating cycle lengths, CL, and amplitudes, A, of at least three cycles assumed present in the data based on the fractional cycle markers (see Gowda, par 0032, fig. 1C),
determining the at least top three cycles based on the at least three approximated cycles, wherein a cycle of the at least three approximated cycles is selected as a top cycle based on a valid signal quality index (i.e., a signal quality of the sensors is determined and is used to determine whether or not determinations should be used using the signal) (see Gowda, par 0071);
and approximating an expected periodicity, P, of each cycle of the at least top three cycles based on a valid signal quality index (i.e., a signal quality of the sensors is determined and is used to determine whether or not determinations should be used using the signal) (see Gowda, par 0071);
and determine a most likely activity of the at least one top periodicity using heuristics (i.e., determining an activity that the motion signature is representative of) (see Gowda, par 0032, fig. 1C).
Gowda fails to teach performing a periodicity transform (PT)-based selection of at least one top periodicity observed from the partial cycle analysis, the PT-based selection comprising, for each of the expected periodicities, P, of the three cycles, projecting the data onto a periodicity subspace for the respective expected periodicity, P, checking if the projection includes at least a threshold, T, percentage of a total energy in a signal comprised by the data, and if so accepting the cycle to which the periodicity corresponds as a possible cycle present in the data, and estimating a most likely cycle length based on the cycle lengths and amplitudes of the identified possible cycles.
Sethares teaches performing a periodicity transform (PT)-based selection of at least one top periodicity observed from the partial cycle analysis, the PT-based selection comprising, for each of the expected periodicities, P, of the three cycles (i.e., using PT to find the best periodic characterization of the length of the signal) (see Sethares, page 2956, section V):
projecting the data onto a periodicity subspace for the respective expected periodicity, P (see Sethares, page 2953-2955, sections II & III);
checking if the projection includes at least a threshold, T, percentage of a total energy in a signal comprised by the data, and if so accepting the cycle to which the periodicity corresponds as a possible cycle present in the data (see Sethares, page 2957, small to large algorithm used to determine if there were significant periodicities within a threshold);
and estimating a most likely cycle length based on the cycle lengths and amplitudes of the identified possible cycles (see Sethares, page 2956, section V);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gowda such that it is configured to execute performing a periodicity transform (PT)-based selection of at least one top periodicity observed from the partial cycle analysis, the PT-based selection comprising, for each of the expected periodicities, P, of the three cycles, projecting the data onto a periodicity subspace for the respective expected periodicity, P, checking if the projection includes at least a threshold, T, percentage of a total energy in a signal comprised by the data, and if so accepting the cycle to which the periodicity corresponds as a possible cycle present in the data, and estimating a most likely cycle length based on the cycle lengths and amplitudes of the identified possible cycles because it would improve the system of Gowda by permitting the system to locate periodicities within data that can provide a clearer examination of the underlying nature of the signals that can be used to make analyses (see Sethares, pages 2961-2962, section VII).
With respect to claim 17, Gowda in view of Sethares teaches the body-worn device of claim 16, wherein a baseline measurement noise amplitude is computed and subtracted from amplitude measurements associated with at least one of accelerometer data and gyroscope data (i.e., noise is computed and metrics from the noise such as noise pollution are calculated) (see Gowda, par 0027).
Conclusion
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/CHARLES A MARMOR II/Supervisory Patent Examiner
Art Unit 3791
/D.J.C./Examiner, Art Unit 3791