DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “the respiratory rate” in line 22 of the claim. A lack of clarity arises as it is unclear whether “the respiratory rate” refers to “the plurality of respiratory rates” or the “final respiratory rate”. For the purposes of examination, it will be interpreted that the limitation is referring to “the final respiratory rate”.
Claims 2-7 are rejected by virtue of dependence on claim 1.
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.
The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent (see MPEP 2106).
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are all within at least one of the four categories.
The independent claim 1 recites:
generate a plurality of respiratory rate estimates from the sensor data, the plurality of respiratory rate estimates comprising PPG-based respiratory rate estimates based on wavelengths of PPG signals extracted from the PPG sensor data and IMU-based respiratory rate estimates based on accelerometer and gyroscope sensor data in parallel;
combine the plurality of respiratory rate estimates into a final PPG-based respiratory rate estimate and a final IMU-based respiratory rate estimate;
select a final respiratory rate from the plurality of respiratory rate estimates based on a set of performance optimization models and a set of rules maintained by a rules engine, the plurality of respiratory rate estimates comprising a final PPG-based respiratory rate estimate and a final IMU-based respiratory rate estimate; and
output the final respiratory rate and a quality estimate associated with the final respiratory rate, wherein the respiratory rate is a continuous respiration rate of a user wearing the set of sensor devices.
The independent claim 8 recites:
applying a set of rules for filtering and processing the sensor data, wherein the set of rules comprises at least one rule for isolating breath-related peaks in PPG signals from the PPG sensor data and identifying motion activity associated with respiration from the IMU sensor data;
generating respiratory rate estimates, including a PPG-based respiratory rate estimate based on the PPG sensor data and an IMU-based respiratory rate estimate based on the IMU sensor data in parallel;
selecting a final respiratory rate from the respiratory rate estimates based on a quality metric, wherein the quality metric comprises a quality score for each respiratory rate estimate indicating reliability of a given respiratory rate estimate; and
identifying the final respiratory rate and the quality score.
The independent claim 15 recites:
applying a set of rules for filtering and processing the sensor data, wherein the set of rules comprises at least one rule for filtering out IMU signal data associated with motion activity unrelated to respiratory activity of a user;
generating respiratory rate estimates, including a PPG-based respiratory rate estimate based on the PPG sensor data and an IMU-based respiratory rate estimate based on the IMU sensor data in parallel;
selecting a final respiratory rate from the respiratory rate estimates based on a quality metric, wherein the quality metric comprises a quality score; and
providing the final respiratory rate and the quality score to a user via a user interface.
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019. See footnotes 14 and 15.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018) (performing a resampled statistical analysis to generate a resampled distribution).
The claimed steps of generating; combining; selecting; outputting; applying; identifying; and providing can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
Examples of ineligible claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.;
claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.
a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
With respect to the pending claims, for example, an experienced physician can perform the claimed step of generating by mentally noting respiratory rate estimates from sensor data. The experienced physician can then further combine mentally the respiratory rate estimates into finalized respiratory rate estimates and can further select one of the finalized respiratory rate estimates based on a set of optimization and rule parameters that the experienced physician has mentally noted and then can output the final respiratory rate using a pen/pencil and paper. Thus, the claims can be readily interpreted as being a mere application of a mental process on a computer.
Regarding the dependent claims 2-7, 9-14 and 16-20, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. For example, dependent claims recite steps (e.g. performing; selecting; updating; filtering; applying) that can be performed in the mind. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
This judicial exception (abstract idea) in claims 1-20 is not integrated into a practical application because:
The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for generating; combining; selecting; outputting; applying; identifying; providing; performing; updating and filtering merely invoke a computer as a tool.
The data-gathering step (receiving and obtaining) does not add a meaningful limitation to the method as they are insignificant extra-solution activity.
There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computer that is used as a tool for generating; combining; selecting; outputting; applying; identifying; providing; performing; updating and filtering.
The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to provide information on respiration rates.
The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer for generating; combining; selecting; outputting; applying; identifying; providing; performing; updating and filtering. The claims do not apply the obtained data to a particular machine. Rather, the data is merely output in an post-solution step.
The additional elements are identified as follows: a processor; a set of sensor devices comprising a photoplethysmography (PPG) sensor and an inertial movement unit (IMU) sensor; a computer-readable medium; a respiratory rate estimator; a respiratory rate fusion model; a selection manager; a user interface; a PPG sensor device; an IMU sensor device; one or more computer storage devices with computer executable instructions; and a computer
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
the non-patent literature cited by applicant:
Ruixuan Dai et al.; RespWatch: Robust Measurement of Respiratory Rate on Smartwatches with Photoplethysmography; Intl Conference on Internet-of-Things Design and Implementation (IoTDI '21), May 18-21, 2021; 13 pages
see Section 2.2; Section 2.3 and Section 3
Delaram Jarchi et al.; Estimation of respiratory rate from motion contaminated photoplethysmography signals incorporating accelerometry; Healthcare Technology Letters, 2019, Vol. 6, Iss. 1; November 20, 2018; 8 pages
see Section 2
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional elements as those claimed. See option III. A. 2. in the Berkheimer memorandum.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4-12, 15-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (CN 112932460 A; cited by applicant; citations refer to the accompanying English machine translation) in view of Rahman (US 20210386318 A1).
With respect to claim 1, Chen discloses a system for respiratory rate monitoring (see p. 7: wearable sensor based on data fusion technology hybrid monitoring system with respiratory frequency monitoring device), the system comprising:
a set of sensor devices generating sensor data (see p. 8: first sensing module #110, second sensing module #120 and third sensing module #130), the set of sensor devices comprising a photoplethysmography (PPG) sensor (see p. 13 and p. 15: third sensing module #130 comprises a pulse oximeter #132 to measure via PPG) and an inertial movement unit (IMU) sensor (see p.8 : first sensing module comprises a first inertia measuring unit #112 which comprises a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer), the sensor data comprising PPG sensor data and IMU sensor data (see p. 14-15: sensor data includes PPG data and IMU data); and
generate, by a respiratory rate estimator, a plurality of respiratory rate estimates from the sensor data (see p. 11-12: a plurality of respiratory frequencies are generated from the sensor data including those from the first inertia measuring unit #112 and the pulse oximeter #132), the plurality of respiratory rate estimates comprising PPG-based respiratory rate estimates based on wavelengths of PPG signals extracted from the PPG sensor data and IMU-based respiratory rate estimates based on accelerometer and gyroscope sensor data in parallel (see p. 11-12: a plurality of respiratory frequencies are generated from the sensor data including those from the first inertia measuring unit #112 and the pulse oximeter #132 are extracted from the data together to give a first respiration frequency and a second respiration frequency);
combine, by a respiratory rate fusion model, the plurality of respiratory rate estimates into a final PPG-based respiratory rate estimate and a final IMU-based respiratory rate estimate (see p. 11-12: the first and second respiration frequency is fused together to obtain a third respiration frequency);
select, by a selection manager, a final respiratory rate from the plurality of respiratory rate estimates based on a set of performance optimization models and a set of rules maintained by a rules engine, the plurality of respiratory rate estimates comprising a final PPG-based respiratory rate estimate and a final IMU-based respiratory rate estimate (see p. 4-5: a Kalman filter data fusion algorithm is used to select a final respiratory rate from both the first respiratory frequency and the second respiratory frequency where it improves the accuracy of the respiratory frequency monitoring); and
output the final respiratory rate via a user interface (see p. 3: display screen used to display the third respiratory frequency), wherein the respiratory rate is a continuous respiration rate of a user wearing the set of sensor devices (see p. 3: display screen can make patient know the value of the parameters in real time for continual monitoring).
Chen further discloses a communication module (see p. 3: communication module to communication with Internet of Things terminal). However Chen does not specifically disclose a processor; and a computer-readable medium storing instructions that are operative upon execution by a processor. Chen does not specifically disclose a quality metric associated with the final respiratory rate.
Rahman discloses a processor and a computer readable medium storing instructions that are operative upon execution by a processor for respiratory rate estimation (see paragraph 0068: a microprocessor #26 in communication with memory #28 controls the function of the monitor and processing measurements made by accelerometers and gyroscope). Rahman further discloses a quality metric (see paragraph 0051-0052: measurement quality determiner #108 estimates quality of biomarkers namely the user’s respiratory frequency).
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 Chen with the teachings of Rahman to have included a processor, a computer readable medium and a quality metric because it would have resulted in the predictable result of controlling the function and processing measurements of sensors (Rahman: see [0068]) and further quantifying the accuracy or reliability of extracted biomarkers such as respiratory rate (Rahman: see [0052]).
With respect to claim 2, all limitations of claim 1 apply in which Rahman further teaches wherein the set of rules further comprises an adaptive peak-to-trough threshold (see paragraph 0082: preceding trough and ending peak are identifies to filter data), wherein an amplitude of a PPG signal greater than the peak- to-trough threshold is acceptable (see paragraph 0084: respiratory-induced amplitude variation is determined from a minima or maxima; and see paragraph 0087: for an estimate to be found reliable it needs to be above a minimum threshold value), and wherein the PPG signal is disregarded as noise where the amplitude of the PPG signal is less than the peak-to-trough threshold (see paragraph 0084: respiratory-induced amplitude variation is determined from a minima or maxima; and see paragraph 0087: for an estimate to be found reliable it needs to be above a minimum threshold value otherwise it is not recorded; see paragraph 0082-0083: preceding trough and ending peak are identifies to filter data where if distinct cardiac cycles cannot be identified then the PPG data does not have a respiratory rate be output).
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 Chen with the teachings of Rahman to have a threshold to determine whether a signal is acceptable or not because it would have resulted in the predictable result of filtering data to be modelled as a plurality of distinct cardiac cycles to indicate whether PPG data is suitable or not (Rahman: see [0082]-[0083]).
With respect to claim 4, all limitations of claim 1 apply in which Rahman further teaches wherein the instructions are further operative to: perform signal processing on IMU-based respiratory signals using time- frequency spectrum (TFS) based tracking and adaptive selection of IMU signal modes based on movement, posture, activity, respiratory rate range, and time-frequency spectral features (see paragraph 0068: biomarkers are extracted using a combination of signal processing and machine learning where time and frequency domain spectral features are utilized along with other adaptive respiratory condition assessors to detect breathing segments in determining a respiratory condition).
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 Chen with the teachings of Rahman to perform signal processing using time-frequency spectrum and adaptive selection because it would have resulted in the predictable result of detecting breathing segments to determine a respiratory condition (Rahman: see [0068]).
With respect to claim 5, all limitations of claim 1 apply in which Rahman further teaches wherein the set of rules further comprises: a breath duration rule, wherein a breath duration is required to be within range of desired duration compared to predetermined set of previous breath intervals (see paragraph 0080: a minimum subject breath rate is utilized to have certain signals from respiratory cycles be retained); tracking and tracing of respiratory frequency using current and previous respiratory rate estimates for accelerometer and gyroscope fusion (see paragraph 000080-0081, transform raw PPG measurement into a filtered trace to use for further processing); and harmonic correction of gyroscope respiratory rate estimate when current PPG or ACC or previous respiratory rate estimates are in low range (see paragraph 0089: after basic filtering when individual cardiac cycles are identified then the maximum and minimum measurement of each peak of amplitude is determined to correct data).
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 Chen with the teachings of Rahman to have a breath duration rule because it would have resulted in the predictable result of only retaining signals that result from cycles with the minimum breath duration (Rahman: see [0080]).
With respect to claim 6, all limitations of claim 1 apply in which the combination of Chen and Rahman further teaches wherein the instructions are further operative to:
select the PPG-based respiratory rate estimate as the final respiratory rate where a quality metric for the IMU-based respiratory rate estimate indicates the IMU- based respiratory rate estimate is less reliable than the PPG-based respiratory rate estimate (Chen: see p. 15-16: when data accuracy of the pulse oximeter is better it is selected for the data fusion algorithm); and
select the IMU-based respiratory rate estimate as the final respiratory rate where the quality metric for the IMU-based respiratory rate estimate is more reliable than the PPG-based respiratory rate estimate (Chen: see p. 15-16: the data accuracy of the first respiration is higher than the second respiratory frequency data therefore the first respiratory frequency is set as the reference quantity).
With respect to claim 7, all limitations of claim 1 apply in which Rahman further teaches wherein the instructions are further operative to: update the set of rules by a respiratory rate performance optimization machine learning model to improve accuracy of respiratory rate calculation (see paragraph 0045: calculation of rate of respiration may be processed via machine learning algorithms).
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 Chen with the teachings of Rahman to have utilized machine learning because it would have resulted in the predictable result of determining reliable data by combining a relatively unreliable data source with additional data sources (Rahman: see [0098]).
With respect to claim 8, Chen discloses a method for respiratory rate monitoring (see p. 7: wearable sensor based on data fusion technology hybrid monitoring system with respiratory frequency monitoring device and method), the method comprising:
receiving photoplethysmography (PPG) sensor data from a PPG sensor device (see p. 13 and p. 15: third sensing module #130 comprises a pulse oximeter #132 to measure via PPG to receive data) and inertial movement unit (IMU) sensor data from an IMU sensor device (see p.8 : first sensing module #110 comprises a first inertia measuring unit #112 which comprises a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer to receive data);
applying a set of rules for filtering and processing the sensor data (see p. 4-5: a Kalman filter data fusion algorithm is used to select a final respiratory rate from both the first respiratory frequency and the second respiratory frequency where it improves the accuracy of the respiratory frequency monitoring), wherein the set of rules comprises at least one rule for isolating breath-related peaks in PPG signals from the PPG sensor data and identifying motion activity associated with respiration from the IMU sensor data (see p. 14: filtering the chest and abdominal cavity movement condition data when breathing is done to normalize the respiratory frequencies over time with both sensor module A #110 and second module C #130);
generating respiratory rate estimates, including a PPG-based respiratory rate estimate based on the PPG sensor data and an IMU-based respiratory rate estimate based on the IMU sensor data in parallel (see p. 11-12: a plurality of respiratory frequencies are generated from the sensor data including those from the first inertia measuring unit #112 and the pulse oximeter #132 are extracted from the data together to give a first respiration frequency and a second respiration frequency);
selecting a final respiratory rate from the respiratory rate estimates (see p. 4-5: a Kalman filter data fusion algorithm is used to select a final respiratory rate from both the first respiratory frequency and the second respiratory frequency where it improves the accuracy of the respiratory frequency monitoring; and see p. 15-16: the data accuracy of the first respiration is higher than the second respiratory frequency data therefore the first respiratory frequency is set as the reference quantity); and
identifying the final respiratory rate (see p. 3: display screen used to display the third respiratory frequency along with the pulse and blood oxygen).
Chen does not specifically disclose that the method is computer implemented. Chen further does not disclose a quality metric, wherein the quality metric comprises a quality score for each respiratory rate estimate indicating reliability of a given respiratory rate estimate.
Rahman discloses computer implemented method (see paragraph 0051: at least one processor may be part of a mobile telephone and/or Internet which is part of a computer where processes a method; and see paragraph 0068: a microprocessor #26 in communication with memory #28 controls the function of the monitor and processing measurements made by accelerometers and gyroscope; and see paragraph 0100: personal computing device). Rahman further teaches determining a quality metric, wherein the quality metric comprises a quality score for each respiratory rate estimate indicating reliability of a given respiratory rate estimate (see paragraph 0052-0055).
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 Chen with the teachings of Rahman to have a method be computer implemented and further have a quality metric because it would have resulted in the predictable result of controlling the function and processing measurements of sensors (Rahman: see [0068]) from a personal computing device (Rahman: see [0068]) and selecting a biomarker that is more accurate or reliable for further downstream functions (Rahman: see [0052]-[0055]).
With respect to claim 9, all limitations of claim 8 apply in which Rahman further teaches wherein applying the set of rules further comprises: filtering out a PPG signal having an amplitude less than an adaptive peak-to- trough threshold (see paragraph 0082-0083: preceding trough and ending peak are identifies to filter data where if distinct cardiac cycles cannot be identified then the PPG data does not have a respiratory rate be output; and see paragraph 0084: respiratory-induced amplitude variation is determined from a minima or maxima; and see paragraph 0087: for an estimate to be found reliable it needs to be above a minimum threshold value otherwise it is not recorded).
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 Chen with the teachings of Rahman to have filtered a PPG signal that is below a threshold value because it would have resulted in the predictable result of filtering data to be modelled as a plurality of distinct cardiac cycles (Rahman: see [0082]-[0083]).
With respect to claim 10, all limitations of claim 8 apply in which Rahman further teaches further comprising: applying a motion activity threshold, wherein motion activity around an interval associated with an IMU-based respiratory signal less than a predetermined threshold value is used for calculating an estimated respiratory rate, and wherein motion activity around the interval greater than the predetermined threshold value is filtered out (see paragraph 0054-0056 and 0066-0068: if a threshold is met for the signal than the signal is accepted and if it does not meet it then it is rejected essentially filtered out).
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 Chen with the teachings of Rahman to have utilized a threshold because it would have resulted in the predictable result of filtering signals that do not meet a threshold level (Rahman: see [0066]-[0068]).
With respect to claim 11, all limitations of claim 8 apply in which Rahman further teaches applying a breath duration rule, wherein a breath duration is required to be within range of desired duration compared to predetermined set of previous breath intervals (see paragraph 0080: a minimum subject breath rate is utilized to have certain signals from respiratory cycles be retained; and see paragraph 0089: after basic filtering when individual cardiac cycles are identified then the maximum and minimum measurement of each peak of amplitude is determined to correct data).
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 Chen with the teachings of Rahman to have a breath duration rule because it would have resulted in the predictable result of only retaining signals that result from cycles with the minimum breath duration (Rahman: see [0080]).
With respect to claim 12, all limitations of claim 8 apply in which the combination of Chen and Rahman further teaches wherein the instructions are further operative to:
selecting the PPG-based respiratory rate as the final respiratory rate where the IMU-based respiratory rate indicates the IMU-based respiratory rate is less reliable than the PPG-based respiratory rate (Chen: see p. 15-16: when data accuracy of the pulse oximeter is better it is selected for the data fusion algorithm); and
selecting the IMU-based respiratory rate as the final respiratory rate where the IMU-based respiratory rate is more reliable than the PPG-based respiratory rate (Chen: see p. 15-16: the data accuracy of the first respiration is higher than the second respiratory frequency data therefore the first respiratory frequency is set as the reference quantity).
Chen does not specifically teach a quality metric.
Rahman further teaches determining a measurement quality (see paragraph 0052-0055).
It would have been obvious to one of ordinary skill in the art to have modified Chen with the teachings of Rahman to have selected a biomarker that has a higher quality metric because it would have resulted in the predictable result of selecting a biomarker that is more accurate or reliable for further downstream functions (Rahman: see [0052]-[0055]).
With respect to claim 15, Chen discloses performing operations (see p. 7: wearable sensor based on data fusion technology hybrid monitoring system with respiratory frequency monitoring device and method) comprising:
obtaining photoplethysmography (PPG) sensor data from a PPG sensor device (see p. 13 and p. 15: third sensing module #130 comprises a pulse oximeter #132 to measure via PPG to obtain data) and inertial movement unit (IMU) sensor data from an IMU sensor device (see p.8 : first sensing module comprises a first inertia measuring unit #112 which comprises a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer);
applying a set of rules for filtering and processing the sensor data, wherein the set of rules comprises at least one rule for filtering out IMU signal data associated with motion activity unrelated to respiratory activity of a user (see p. 14: filtering the chest and abdominal cavity movement condition data when breathing is done to normalize the respiratory frequencies over time with both sensor module A #110 and second module C #130);
generating respiratory rate estimates, including a PPG-based respiratory rate estimate based on the PPG sensor data and an IMU-based respiratory rate estimate based on the IMU sensor data in parallel (see p. 11-12: a plurality of respiratory frequencies are generated from the sensor data including those from the first inertia measuring unit #112 and the pulse oximeter #132 are extracted from the data together to give a first respiration frequency and a second respiration frequency);
selecting a final respiratory rate from the respiratory rate estimates (see p. 4-5: a Kalman filter data fusion algorithm is used to select a final respiratory rate from both the first respiratory frequency and the second respiratory frequency where it improves the accuracy of the respiratory frequency monitoring; and see p. 15-16: the data accuracy of the first respiration is higher than the second respiratory frequency data therefore the first respiratory frequency is set as the reference quantity); and
providing the final respiratory rate and the quality score to a user via a user interface (see p. 3: display screen used to display the third respiratory frequency along with the pulse and blood oxygen).
Chen does not specifically disclose one or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform operations. Chen further does not specifically disclose a quality metric wherein the quality metric comprises a quality score.
Rahman discloses one or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform operations (see paragraph 0051: at least one processor may be part of a mobile telephone and/or Internet which is part of a computer where processes a method; and see paragraph 0068: a microprocessor #26 in communication with memory #28 controls the function of the monitor and processing measurements made by accelerometers and gyroscope; and see paragraph 0100: personal computing device). Rahman further discloses a quality metric wherein the quality metric comprises a quality score (see paragraph 0051-0052: measurement quality determiner #108 estimates quality of biomarkers namely the user’s respiratory frequency where #108 determines a measurement quality).
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 Chen with the teachings of Rahman to have added a computer storage devices with computer-executable instructions and a quality metric because it would have resulted in the predictable result of controlling the function and processing measurements of sensors (Rahman: see [0068]) from a personal computing device (Rahman: see [0068]) and further quantifying the accuracy or reliability of extracted biomarkers such as respiratory rate (Rahman: see [0052]).
With respect to claim 16, all limitations of claim 15 apply in which Rahman further teaches wherein the operations further comprise: applying a peak-to-trough threshold, wherein a PPG signal having an amplitude less than the peak-to-trough threshold is filtered out as noise (see paragraph 0082-0083: preceding trough and ending peak are identifies to filter data where if distinct cardiac cycles cannot be identified then the PPG data does not have a respiratory rate be output; and see paragraph 0084: respiratory-induced amplitude variation is determined from a minima or maxima; and see paragraph 0087: for an estimate to be found reliable it needs to be above a minimum threshold value otherwise it is not recorded).
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 Chen with the teachings of Rahman to have filtered a PPG signal that is below a threshold value because it would have resulted in the predictable result of filtering data to be modelled as a plurality of distinct cardiac cycles (Rahman: see [0082]-[0083]).
With respect to claim 17, all limitations of claim 15 apply in which Rahman further teaches wherein the operations further comprise: applying a motion activity threshold, wherein motion activity around an interval associated with an IMU-based respiratory signal less than a predetermined threshold value is used for calculating an estimated respiratory rate, and wherein motion activity around the interval greater than the predetermined threshold value is filtered out (see paragraph 0054-0056 and 0066-0068: if a threshold is met for the signal than the signal is accepted and if it does not meet it then it is rejected essentially filtered out).
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 Chen with the teachings of Rahman to have utilized a threshold because it would have resulted in the predictable result of filtering signals that do not meet a threshold level (Rahman: see [0066]-[0068]).
With respect to claim 19, all limitations of claim 15 apply in which Chen further teaches wherein the IMU-based respiratory estimate comprises at least one of accelerometer-based respiratory rate estimate generated using sensor data from an accelerometer and gyroscope-based respiratory rate estimate generated based on sensor data generated by a gyroscope (see p.8 : first sensing module comprises a first inertia measuring unit #112 which comprises a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer).
Claims 3, 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Rahman as applied to claims 1, 8 and 15 respectively above, and further in view of Franceschini (US 20250366723 A1).
With respect to claim 3, all limitations of claim 1 apply in which Chen and Rahman do not specifically teach performing signal processing to filter the sensor data, by the respiratory rate estimator, wherein the signal processing includes filtering to remove Mayer-wave in- band noise, transients, and motion artifacts
Franceschini teaches signal processing that includes filtering to remove Mayer-wave in-band noise, transients, and motion artifacts (see paragraph 0096: filter data with motion artifacts and remove Mayer waves and remove high frequency noise indicative of transients).
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 Chen and Rahman with the teachings of Franceschini to remove Mayer waves, motion artifacts and transients because it would have resulted in the predictable result of filtering data to obtain a clean data set (Franceschini: see [0096]).
With respect to claim 13, all limitations of claim 8 apply in which Chen and Rahman do not specifically teach filtering the sensor data for removal of Mayer-wave in-band noise, transients, and motion artifacts.
Franceschini teaches signal processing that includes filtering to remove Mayer-wave in- band noise, transients, and motion artifacts (see paragraph 0096: filter data with motion artifacts and remove Mayer waves and remove high frequency noise indicative of transients).
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 Chen and Rahman with the teachings of Franceschini to remove Mayer waves, motion artifacts and transients because it would have resulted in the predictable result of filtering data to obtain a clean data set (Franceschini: see [0096]).
With respect to claim 18, all limitations of claim 15 apply in which Chen and Rahman do not specifically teach wherein the operations further comprise: filtering the sensor data for removal of Mayer-wave in-band noise, transients, and motion artifacts.
Franceschini teaches signal processing that includes filtering to remove Mayer-wave in- band noise, transients, and motion artifacts (see paragraph 0096: filter data with motion artifacts and remove Mayer waves and remove high frequency noise indicative of transients).
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 Chen and Rahman with the teachings of Franceschini to remove Mayer waves, motion artifacts and transients because it would have resulted in the predictable result of filtering data to obtain a clean data set (Franceschini: see [0096]).
Claims 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Rahman as applied to claims 8 and 15 respectively above, and further in view of Tran (US 20230111811 A1).
With respect to claim 14, all limitations of claim 8 apply in which Rahman further teaches by a machine learning model in real-time (see paragraph 0045: calculation of rate of respiration may be processed via machine learning algorithms).
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 Chen with the teachings of Rahman to have utilized machine learning because it would have resulted in the predictable result of determining reliable data by combining a relatively unreliable data source with additional data sources (Rahman: see [0098]).
Chen and Rahman do not specifically teach updating at least one threshold value.
Tran teaches updating a threshold value (see paragraph 0175: as data is fed to a pattern recognizer each transition probability is updated to until the pattern recognizer predicts a logical relationship up to a specified accuracy threshold).
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 Chen and Rahman with the teachings of Tran to have updated a threshold value because it would have resulted in the predictable result of optimizing a prediction using collected data (Tran: see [0175]).
With respect to claim 20, all limitations of claim 15 apply in which Rahman further teaches wherein the operations further comprise: a respiratory rate performance optimization machine learning model in real-time (see paragraph 0045: calculation of rate of respiration may be processed via machine learning algorithms).
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 Chen with the teachings of Rahman to have utilized machine learning because it would have resulted in the predictable result of determining reliable data by combining a relatively unreliable data source with additional data sources (Rahman: see [0098]).
Chen and Rahman do not specifically teach updating at least one rule.
Tran teaches updating a rule (see paragraph 0175: as data is fed to a pattern recognizer each transition probability is updated to until the pattern recognizer predicts a logical relationship up to a specified accuracy threshold).
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 Chen and Rahman with the teachings of Tran to have updated a rule because it would have resulted in the predictable result of optimizing a prediction using collected data (Tran: see [0175]).
Conclusion
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/N.N.P./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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