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 .
Claims 1-20 are hereby under examination.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 1-17 are directed to a “method”, which describes one of the four statutory categories of patentable subject matter, i.e., a process. Claims 18-20 are directed to a “device”, 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 1-20 recite abstract idea, as follows:
(claim 1): …determining, for each of the plurality of mobile-device axes, a ballistocardiogram (BCG) signal based on the motion signal corresponding to that mobile-device axis; selecting, based on a strength of the determined BCG signals, one or more particular mobile-device axes and corresponding motion signals for estimating a user’s tidal volume; determining, based on the one or more selected motion signals, one or more breathing features; and estimating… the user’s current tidal volume.
Claims 18-19: access a plurality of motion signals detected by a motion sensor of a mobile device worn by a user, each representing a motion of the user about one of a plurality of mobile-device axes defined by an orientation of the mobile device; determine, for each of the plurality of mobile-device axes, a ballistocardiogram (BCG) signal based on the motion signal corresponding to that mobile-device axis; select, based on a strength of the determined BCG signals, one or more particular mobile-device axes and corresponding motion signals for estimating a user’s tidal volume; determine, based on the one or more selected motion signals, one or more breathing features; and estimate… the user’s current tidal volume.
Based on the broadest reasonable interpretation, accessing a plurality of motion signals detected by a motion sensor of a mobile device worn by a user can be presented in a graph or data table format on a piece of paper to a person. Then the person can determine, a BCG signal mathematically by studying the given motion signal of plurality of axes. The person can select visually or mathematically, one or more particular mobile-device axes based on a strength of the determined BCG signals and mentally corresponding motion signals for estimating a user’s tidal volume; finally, the person can determine, based on the one or more selected motion signals, one or more breathing features and estimate the user’s current tidal volume on a paper, mathematically.
Prong two: Claims 1-20 do not include additional elements that integrate the abstract into a practical application.
The additional elements are as follows:
Motion sensor
head-worn device, earbuds
mobile device, mobile phone, processors, non-transitory computer readable storage media
watch
Reciting a computer or computer components (mobile device, mobile phone, processors, non-transitory computer readable storage media) simply amounts to reciting a general processor to perform general functions of a computer as above to perform the mental processes of accessing a plurality of motion signals detected by a motion sensor of a mobile device worn by a user can be presented in a graph or data table format on a piece of paper to a person. Then the person can determine, a BCG signal mathematically by studying the given motion signal of plurality of axes. The person can select visually or mathematically, one or more particular mobile-device axes based on a strength of the determined BCG signals and mentally corresponding motion signals for estimating a user’s tidal volume; finally, the person can determine, based on the one or more selected motion signals, one or more breathing features and estimate the user’s current tidal volume are mere instructions to apply the judicial exception to general technology. Such elements do not integrate the exception into a practical application since they are merely instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
Reciting motion sensor, headword device, earbuds, and watch do not integrate the exception into a practical application since it is merely insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality.
Therefore, claims 1-20 are ineligible at step 2A, prong two.
Step 2B of the subject matter eligibility test (see MPEP 2106.05)
Reciting a computer or computer components (mobile device, mobile phone, processors, non-transitory computer readable storage media) simply amounts to reciting a general processor to perform general functions of a computer as above to perform the mental processes of accessing a plurality of motion signals detected by a motion sensor of a mobile device worn by a user can be presented in a graph or data table format on a piece of paper to a person. Then the person can determine, a BCG signal mathematically by studying the given motion signal of plurality of axes. The person can select visually or mathematically, one or more particular mobile-device axes based on a strength of the determined BCG signals and mentally corresponding motion signals for estimating a user’s tidal volume; finally, the person can determine, based on the one or more selected motion signals, one or more breathing features and estimate the user’s current tidal volume are mere instructions to apply the judicial exception to general technology.
Such elements 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’l, 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 Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Reciting motion sensor, headword device, earbuds, and watch do not integrate the exception into a practical application since it is merely insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality.
U.S. Patent Application Publication No. US 20210169417 A1 discloses that motion sensor, headword device, earbuds, and watch are conventional: [0891], “conventional entertainment and/or health sensing ear-buds”; [0983], “conventional clock, watch”; [3031], “While conventional health trackers incorporating motion sensors”
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 includes 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.
Therefore, claims 1-20 are ineligible at step 2B.
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.
Claim 17 is 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.
Regarding claim 17, the phrase "if any" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). For the purpose of examination, it is interpreted that “if any, and one or more frequency-domain features, if any, to a trained machine-learning model” are not part of the claimed limitations.
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.
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, 10, 12, 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over US20190160282A1 (Dieken et. al), hereto referred as Dieken, and in view of US20230009478A1 (Receveur et. al), hereto referred as Receveur.
As to claims 1, 18, and 19, Dieken teaches detecting, by a motion sensor of a mobile device worn by a user, a plurality of motion signals, each representing a motion of the user about one of a plurality of mobile-device axes defined by an orientation of the mobile device (Dieken, [0124], “In some examples, accelerometer utilization manager 800 comprises a motion artifact detection engine 870”; [0047], “Such external sensors may be worn on the patient's body”);
determining, for each of the plurality of mobile-device axes, a ballistocardiogram (BCG) signal (Dieken, [0129], “In some examples, accelerometer 160 enables detection of cardiac information via a seismocardiogram (922 in FIG. 11C) or a ballistocardiogram”) based on the motion signal corresponding to that mobile-device axis;
selecting, based on a strength of the determined BCG signals, one or more particular mobile-device axes and corresponding motion signals for estimating a user’s tidal volume (Dieken, [0124], “this extraction may be implemented via an awareness of motion associated with an X axis or Y axis of an accelerometer sensor having signal power significantly greater than the signal power of a Z axis in the accelerometer sensor, such as where the accelerometer sensor is implanted in some examples such that its Z axis is generally parallel to an anterior-posterior axis of the patient's body. If a patient's respiration signal is largest in a particular axis (not necessarily aligned with one of X, Y, Z), motion artifact can be rejected by filtering signals not aligned with the axis where respiration is largest.”). However, Dieken does not necessarily teach selecting based on a strength of the determined BCG signals. Receveur teaches a relevant art of determining tidal volume (Receveur, title), and teaches selecting based on a strength of the determined BCG signals (Receveur, Based on the RQI assessment, CGy, which had good and consistent signal quality in any posture compared to the other two low-frequency force signals, was selected for the RR estimation and resulted in robust estimations.). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Diekens in view of Receveur to include selecting, based on a strength of the determined BCG signals, one or more particular mobile-device axes because selecting the best quality BCG would result in robust estimations, as recognized by Receveur.
determining, based on the one or more selected motion signals, one or more breathing features (Dieken, [0181], “In general terms, the feature extraction function 1290 determines which features to extract from the different axis signals and/or meta-vectors (e.g. combined axis signals).”); and
However, Dieken does not teach estimating, by providing the one or more breathing features to a trained machine-learning model. Receveur teaches estimating, by providing the one or more breathing features to a trained machine-learning model, the user’s current tidal volume (Receveur, [0065], “To extract low-frequency features, the outputs from the load cells 66, 68, 70, 72 were low-pass filtered with the cut-off at 2 Hz to extract respiratory components of the signal”; [0066], “Ground truth values and features were computed from each window and fed into a machine learning regression model for training and testing at 154”; [0081], “For the estimation of TV from the features extracted in the previous steps, an Extreme Gradient Boosting (XGBoost) model was used.”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Dieken in view of Receveur to include estimating, by providing the one or more breathing features to a trained machine-learning model because doing so would increase software’s technical capabilities such as reducing error, and it would yield a predictable result of outputting the results of providing the user with a tidal volume via a simple substitution of software algorithm.
As to claim 10, Dieken-Receveur teaches selecting, based on a strength of the determined BCG signals, one or more particular mobile-device axes and corresponding motion signals for estimating the user’s tidal volume (Dieken, [0124]) comprises selecting the one mobile-device axis and corresponding motion signal that corresponds to the strongest BCG signal (Receveur, [0089], “Based on the RQI assessment, CGy, which had good and consistent signal quality in any posture compared to the other two low-frequency force signals, was selected for the RR estimation and resulted in robust estimations”.
As to claim 12, Dieken-Receveur teaches selecting, based on a strength of the determined BCG signals, one or more particular mobile-device axes and corresponding motion signals for estimating the user’s tidal volume comprises selecting a strongest BCG signal J-peak amplitude above a particular threshold (Receveur, [0075], "In the ECG-based approach, BCG J-waves were identified as the maximum peak within the 200 ms-400 ms range from ECG R-peaks”; the examiner notes, the threshold is interpreted as being above other peaks).
As to claim 15, Dieken-Receveur teaches the mobile device comprises a mobile phone or a watch placed on the user’s chest (Dieken, [0090], “a garment, belt, wristband, wristwatch, wearable smartphone, wearable smartwatch”).
As to claim 16, Dieken-Receveur teaches comprising: recording audio of the user’s breathing (Dieken, [0176], “such acoustic vibratory sensing may be used to detect sleep disordered breathing (SDB) events”);
synchronizing the recorded audio with the plurality of motion signals (Dieken, [0120], “In some examples, acoustic engine 850 can use other acoustically-sensed information such as an acoustic sensor 1244 as described later in association with at least FIG. 13. This other acoustic information may be used in addition to, or instead of, the acoustic information obtained via one of the example accelerometer sensor implementations.”); and
determining, based on the one or more selected motion signals and the audio of the user’s breathing, one or more breathing feature (Dieken, [0177], “In some examples, acoustic sensor 1244 detects snoring information, which may be used in detection, evaluation, and/or modification of sleep-related information and/or therapy parameters”).
As to claim 17, Dieken-Receveur teaches further comprising: determining, based on the plurality of motion signals, at least one of (1) one or more time-domain features and (2) one or more frequency-domain features (Dieken, [0181], “In some examples, such feature extraction is implemented via a power spectral density parameter 1292 and a frequency threshold parameter 1294”);
and estimating the user’s current tidal volume estimating by providing the one or more breathing features and one or more determined time-domain features, if any, and one or more frequency-domain features, if any, to a trained machine-learning model (Receveur, [0049], "An end-to-end signal processing and machine learning-based prediction algorithm using features extracted from both the cardiac and respiratory components of load cell signals to estimate TV").
Claim 2-6 are rejected under 35 U.S.C. 103 as being unpatentable over Dieken-Receveur as applied to claim 1 above, and further in view of US20210386318A1 (Rahman et. al), hereto referred as Rahman.
Claim 1 is taught as above.
As to claims 2-3, Dieken-Receveur teaches the mobile device comprises a worn device (Dieken, [0090], “a garment, belt, wristband, wristwatch, wearable smartphone, wearable smartwatch”), but does not specify it’s a head-worn device. Rahman teaches a relevant art of detecting respiratory condition (Rahman, title), and teaches a wearable head-worn device (Rahman, [0019], “A pair of earbuds”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dieken-Receveur in view of Rahman because earbuds are a type of wearable device.
As to claim 4, Dieken-Receveur-Rahman teaches the plurality of mobile-device axes comprises a set of three axes in Cartesian coordinates (Dieken, Fig. 5).
As to claim 5, Dieken-Receveur-Rahman teaches determining, for each of the plurality of mobile-device axes, a ballistocardiogram (BCG) signal based on the motion signal corresponding to that mobile-device axis (Dieken, [0129]). However, Dieken does not teach detecting, for each of the plurality of mobile-device axes, a plurality of J-peaks in each motion signals. Receveur teaches detecting a plurality of J-peaks in each motion signals (Receveur, [0075], "In the ECG-based approach, BCG J-waves were identified as the maximum peak within the 200 ms-400 ms range from ECG R-peaks. "). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Dieken in view of Receveur to include detecting, for each of the plurality of mobile-device axes, a plurality of J-peaks in each motion signals because Dieken teaches a plurality of mobile device axes, and detecting J peak is a well understood technique for detecting and segmenting BCG heartbeats for cardiac analysis and PHOSITA would apply it to each axes.
As to claim 6, Dieken-Receveur-Rahman teaches the motion signals correspond to a segment of static motion signals (Dieken, [0113], “Conversely, measurements of acceleration of about 1G (corresponding to the presence of the gravitational component only) may be indicative of rest”; Receveur, [0075], “Here, BCG templates were generated from the first 30 seconds of recording during the baseline period when subjects were staying still and not performing any respiratory tasks”).
Allowable Subject Matter
Claims 7-9, 11, and 13-14 are rejected under 101 rejection, but would have been objected to as being dependent upon a rejected base claim if the 101 rejection is overcome.
As to claim 7, Dieken-Receveur, the closest prior art of record, does not teach determining the segment of static motion signals by: dividing an output of the motion sensor into a plurality of intervals; labeling each interval as either static or not static; determining a longest section of the output of the motion sensor comprising a continuous sequence of static labels; selecting the longest section as the segment of static motion signals, along with other claimed steps or elements. Claims 8-9 are dependent on claim 7.
As to claim 11, Dieken-Receveur does not teach selecting the one mobile-device axis based on a difference between an ensemble-based strongest BCG signal and an average-based strongest BCG signal, along with other claimed steps or elements.
As to claim 13, Dieken-Receveur does not teach selecting the two mobile-device axes and corresponding motion signals that corresponds to the two strongest BCG signals along with other claimed steps or elements.
As to claim 14, Dieken-Receveur does not teach determining a transformation matrix for the mobile sensor and transforming the orientation of the mobile device by the transformation matrix to select the one or more particular mobile-device axes and corresponding motion signals, along with other claimed steps or elements.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELINA S JANG whose telephone number is (571)272-7019. The examiner can normally be reached M-F 9:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson can be reached at (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ELINA SOHYUN JANG/Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791
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