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 .
Response to Arguments
Applicant's arguments filed 08/05/2026 have been fully considered but they are not persuasive regarding the rejections of claims 1-14 under 35 USC 101. The Examiner respectfully disagrees that the amendments to the claims (filed 08/05/2026) overcome the rejection under 35 USC 101.
On pg. 11 of Remarks, filed 08/05/2026, the Applicant argues that the claim does not recite a judicial exception. As noted in the non-final rejection filed 1/9/2026, the limitations are directed to an abstract idea mathematical concept and mental process since the action of computing metrics can be performed with equations and mathematical calculations. Further, generating values from the metric is another form calculations. Citing MPEP 2106.04(a)(2)(I): “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989).” As detailed in the non-final office action filed on 1/9/2026, metrics are based on functions, which are mathematical concepts. Further, citing MPEP 2106.04(a)(2)(III): “The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea.” The limitations recited above can be performed with a pen and paper in the human mind since the calculations for physiological metrics are based on functions. It is reasonable that one of ordinary skill could make calculations in their head with using a metric formula.
The Examiner maintains that the claimed subject matter recites mathematical concept and mental process abstract ideas. Further, the Examiner maintains that the recited subject matter in addition to the abstract ideas do not integrate the abstract idea into a practical application or add significantly more to the abstract idea.
Applicant argues on page 14 of Remarks that independent claim 1 as currently presented includes an additional element that reflects an improvement in the functioning of a computer or an improvement to other technology or technical field. The Examiner respectfully disagrees. On pg. 14 of Remarks, the Applicant recites paragraphs [0012] and [0150] of the instant specification as evidence for an improvement to a wearable device. The Examiner notes that the Applicant is relying on details of an algorithm not presented in the independent claim. Further, on pg. 14 of Remarks, the Applicant argues that the recited features “are a technological improvement and have a practical application of providing improved wearable device technology for detection of illnesses and conditions of a user.” The recited features merely link the judicial exception to a field of use (See MPEP 2106.05(h)). The additional limitations merely link the judicial exception to the field of wearable technology. Additionally, the judicial exception abstract idea is relied upon solely as the improvement, and does not include additional limitations that integrate the judicial exception into a practical application. Citing MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below.” The improvement to the field of wearable technology is relied upon entirely by the abstract idea mental process and mathematical concept of calculating physiological metrics. The Examiner notes that the improvements recited on pg. 10 of Remarks, in reference to para. [0171] of the instant specification are not presented in the independent claim of claim 1. Similarly, the Applicant recites additional paragraphs of the specification that are not presented in independent claim 1. The Applicant argues that the additional limitations are an improvement to the processing time on pg. 12 of remarks. However, claim 1 merely recites generic computer structure (processor) for improving processing time. The claim is not directed towards improvements to the computer structure, nor do the claims or instant specification recite specific structure or algorithms for improving the processing of input physiological data. The Examiner maintains that the claim does not integrate the judicial exception into a practical application.
Applicant recites on pg. 13 of Remarks MPEP section 2106.05(d). However, neither the Non-final Office Action filed on 01/09/2026, nor the Final Office Action, filed 02/09/2026, include assertations by the Examiner that the additional limitations (i.e., the limitations that are not recited in step 2A, prong 1 as reciting judicial exceptions) are well-understood, routine, or conventional. The examiner argues that the additional elements of the claim include insignificant extra-solution activity (see MPEP 2106.05(g). Therefore, the argument is moot. Applicant argues on pg. 19 of the Remarks that “[a]s a result of these additional elements, these additional elements provide ‘improved communication reliability, reduced latency, improved user experience related to reduced processing,…”. The Examiner notes that the recited improvements are not presented in claim 1. Claim 1 does not recite structure or any other additional matter besides the judicial exception abstract idea that results in these improvements. Instead, the improvements rely upon the judicial exception. Merely reciting the generic computer implementation of the judicial exception into a processor does amount to significantly more than the abstract idea itself. The Examiner maintains that the claim does not recite significantly more than the abstract idea.
In summary, the Examiner maintains that the subject matter of claim 1 recites an abstract idea judicial exception, and that the additional elements do not integrate the judicial exception into a practical application, or add significantly more to the judicial exception. The Examiner contends that the amendments to the claims do not overcome the rejections under 35 USC 101 (see detailed rejection of amended claims below).
Regarding the rejections of claims 10 and 12 under 35 USC 112(b), the examiner agrees that the amendments to the claim overcome the rejections under 112(b). Therefore, the rejection under 35 USC 112(b) is withdrawn in light of the amended claims.
Applicant’s arguments, see pgs. 20-24, filed 08/05/2026, with respect to the rejection of claims 1-14 have been fully considered and are persuasive. The Examiner agrees that the amendments to claim 1 contain features not taught or suggested by Zhang or Baker. In light of the amendments to claim 1, new grounds of rejection are necessitated (see rejection of amended claim 1 below).
Response to Amendment
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claim 1 is recites a machine (i.e., a system). Therefore, claim 1 is directed to a statutory category of invention.
Step 2A, prong 1
Claim 1 recites: “detect the user is in a sleep interval based at least in part on the physiological data…”. The recited limitation is an abstract idea mental process since one of ordinary skill, such as a physician, could detect that someone is in a sleep interval based on observations of physiological data. “Detect” is recited so generally that observations of data and determinations from those observations could be considered to be detection. One of ordinary skill would be able to make these judgements and determinations in their own mind. Therefore, the recited subject matter is an abstract idea mental process.
Claim 1 also recites: “determine, via the wearable device, a pulse waveform of the user based on the PPG signal.” The recited limitation is an abstract idea mental process since one of ordinary skill could determine a pulse waveform based on observations of a PPG signal. “Determine” is recited so generally that observations of PPG data could make observations to determine a pulse waveform. One of ordinary skill would be able to make these judgements and determinations in their own mind. Therefore, the recited subject matter is an abstract idea mental process.
Claim 1 further recites: “wherein the multi-output neural network comprises a single neural network architecture trained to simultaneously compute a plurality of different, separate outputs including one or more values associated with a first physiological metric and one or more values associated with a second physiological metric.” Calculating values for a metric is a mathematical concept abstract idea in that the values are generated using mathematical formulas and functions. Computing one or more values associated with a physiological metric is so broadly claimed as to encompass a mathematical concept abstract idea. The specification recites calculating average temperature, respiratory rates, acceleration and angular values, sleep metrics, activity metrics, and breathing quality metrics (para. [0213]: “the functions may be stored or transferred over as one or more instructions or code…”). Likewise, the calculations are so broadly claimed that a human could perform the calculations in their head.
Claim 1 further recites: “wherein the one or more values associated with the first physiological metric are indicative of a first probability the user experienced at least one breathing disturbance during the sleep interval.” The recited subject matter is an abstract idea mathematical concept. A value associated with a metric is a calculation based on equations. The probability calculated from this metric is a function of data inputs and relies on mathematical calculations to determine a probability.
Additionally, claim 1 recites, “generate, via a single pass of the multi-output neural network, the plurality of different, separate outputs including the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric…“. The one or more values are being output are mathematical concepts in that they are essentially results of the previous calculations. The recitation is also a mental process abstract idea in that on could generate values in their head based on readings from the accelerometer or the PPG sensor. Similar to the calculations, a human could perform mathematical calculations and obtain results, thus generating one or more values. The Examiner notes that the additional limitations from the amendments to claim 1 still recite a judicial exception.
Step 2A, prong 2
Claim 1 does not include any additional limitations that amount to integration of the abstract idea into a practical application. Claim 1 recites: “a wearable device configured to acquire physiological data from a user, the physiological data comprising at least one of heartbeat data collected via photoplethysmogram (PPG) measurements from one or more light-emitting components and one or more light-receiving components of the wearable device or motion data collected via one or more accelerometers of the wearable device; and one or more processors communicatively coupled with the wearable device, wherein the one or more processors are configured to: receive the physiological data acquired via the wearable device via one or more electronic signals; trigger the wearable device to acquire additional physiological data throughout a time interval that includes the sleep interval, wherein, to trigger the wearable device to acquire the additional physiological data, the one or more processors are configured to: activate the one or more light-emitting components and the one or more light-receiving components to transmit light and generate a PPG signal, respectively, the PPG signal indicative of an amount of the light received by the one or more light-receiving components; receive the additional physiological data acquired via the wearable device via one or more additional electronic signals, the additional physiological data based at least in part on the pulse waveform of the user and comprising at least one of additional heartbeat data or additional motion data; input one or more input streams into the multi-output neural network, wherein one or more input streams comprises at least one of a first input stream corresponding to the heartbeat data or a second input stream corresponding to the motion data; wherein the first physiological metric and the second physiological are distinct and based on a shared set of features from at least one of the first input stream or the second input stream; wherein the multi-output neural network is configured to transfer knowledge between different computations of the plurality of different, separate outputs through the single neural network architecture using the shared set of features; and transmit an instruction to a graphical user interface (GUI) of a user device associated with the wearable device, the instruction configured to cause the GUI to display one or more messages based at least in part on generating the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric, wherein the one or more messages include at least one of a timeline of the at least one breathing disturbance or an index associated with the at least one breathing disturbance.” Acquiring physiological data from a user, collecting heartbeat and motion data, receiving the data, triggering additional data collection, inputting the data into a neural network, and transmitting the data to a GUI amounts to insignificant, extra-solution activity of data gathering (see MPEP 2106.05(g)). The common computer elements and data gathering sensors are recited at a high level of generality to simply amount to generic computer implementation of an abstract idea. Displaying one or more messages on a GUI amounts to insignificant post-solution activity (data displaying and/or reporting). The limitation that the messages as a timeline of the breathing disturbance or index associated with the breathing disturbance merely defines the insignificant post-solution activity, but does not integrate the abstract idea into a practical application. The messages are still insignificant post-solution activity. Lastly, transferring knowledge of computations using a shared set of features (which the instant specification discloses as “input streams” in para. [0127]) is merely generic computer implementation of an abstract idea. The Examiner notes that “transferring knowledge” is equivalent to transferring or transmitting data, which is a generic computer function. Therefore, the additional limitations of claim 1 do not amount to integration of the abstract idea into a practical application. The Examiner notes that the additional limitations from the amendments to claim 1 do not integrate the abstract idea into a practical application.
Step 2B
Claim 1 does not include any additional limitations that amount to integration of the abstract idea into a practical application. Claim 1 recites: “a wearable device configured to acquire physiological data from a user, the physiological data comprising at least one of heartbeat data collected via photoplethysmogram (PPG) measurements from one or more light-emitting components and one or more light-receiving components of the wearable device or motion data collected via one or more accelerometers of the wearable device; and one or more processors communicatively coupled with the wearable device, wherein the one or more processors are configured to: receive the physiological data acquired via the wearable device via one or more electronic signals; trigger the wearable device to acquire additional physiological data throughout a time interval that includes the sleep interval, wherein, to trigger the wearable device to acquire the additional physiological data, the one or more processors are configured to: activate the one or more light-emitting components and the one or more light-receiving components to transmit light and generate a PPG signal, respectively, the PPG signal indicative of an amount of the light received by the one or more light-receiving components; receive the additional physiological data acquired via the wearable device via one or more additional electronic signals, the additional physiological data based at least in part on the pulse waveform of the user and comprising at least one of additional heartbeat data or additional motion data; input one or more input streams into the multi-output neural network, wherein one or more input streams comprises at least one of a first input stream corresponding to the heartbeat data or a second input stream corresponding to the motion data; wherein the first physiological metric and the second physiological are distinct and based on a shared set of features from at least one of the first input stream or the second input stream; wherein the multi-output neural network is configured to transfer knowledge between different computations of the plurality of different, separate outputs through the single neural network architecture using the shared set of features; and transmit an instruction to a graphical user interface (GUI) of a user device associated with the wearable device, the instruction configured to cause the GUI to display one or more messages based at least in part on generating the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric, wherein the one or more messages include at least one of a timeline of the at least one breathing disturbance or an index associated with the at least one breathing disturbance.” Acquiring physiological data from a user, collecting heartbeat and motion data, receiving the data, triggering additional data collection, inputting the data into a neural network, and transmitting the data to a GUI amounts to insignificant, extra-solution activity of data gathering. The common computer elements and data gathering sensors are recited at a high level of generality to simply amount to generic computer implementation of an abstract idea. Displaying one or more messages on a GUI amounts to insignificant post-solution activity (data displaying and/or reporting). The limitation that the messages as a timeline of the breathing disturbance or index associated with the breathing disturbance merely defines the insignificant post-solution activity, but does not amount to significantly more than the judicial exception. The messages are still insignificant post-solution activity. Lastly, transferring knowledge of computations using a shared set of features (which the instant specification discloses as “input streams” in para. [0127]) is merely generic computer implementation of an abstract idea. The Examiner notes that “transferring knowledge” is equivalent to transferring or transmitting data, which is a generic computer function. Therefore, the limitations in addition to the judicial exception do not amount to significantly more than the abstract idea. The Examiner notes that the additional limitations from the amendments to claim 1 do not add significantly more than the abstract idea.
Therefore, the additional limitations of claim 1 do not amount to integration of the abstract idea into a practical application. The Examiner notes that the additional limitations from the amendments to claim 1 still do not integrate the abstract idea into a practical application.
Dependent claims
Claims 2, 3, and 12 further define extra-solution activity for data gathering.
Claims 4-7, 9-11, and 13 further define abstract idea mathematical concept abstract ideas.
Claim 8 further defines generic computer structure.
Claim 14 further defines the mathematical concept and mental process abstract ideas by generally linking the abstract idea to a field of use.
Claim Rejections - 35 USC § 112(b)
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 1-14 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.
Regarding claim 1, the claim recites, “additional physiological data” (line 16 and 24), “additional heartbeat data” (lines 27 and 30), and “additional motion data.” (lines 27 and 31). It is unclear if this physiological data is the same physiological data recited in line 3 of claim 1. If the physiological data are different, it is unclear how the additional physiological data is different than that of the physiological data of line 3. Similarly, it is unclear if the additional heart beat data and additional motion data are the same as that of lines 4 and 7, respectively. The claim is therefore indefinite. It will be interpreted that additional physiological data, additional heartbeat data, and additional motion data are materially the same data as recited in lines 3, 4, and 7, respectively.
Claims 2-14 are rejected due to their dependency from claim 1.
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.
Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20230114833 A1, "Zhang"), Wederhorn et al. (US 20230397851 A1, “Wederhorn”), and Baker et al. (US 20090287070 A1, “Baker”).
Regarding claim 1, Zhang discloses a system for utilizing a multi-output neural network (Abstract; Fig. 1 and 2), comprising: a wearable device (wearable device 104; Fig. 1) configured to acquire physiological data from a user, the physiological data comprising at least one of a heartbeat data collected via photoplethysmogram (PPG) (para. [0043]; "a PPG sensor assembly") measurements from one or more light-emitting components (para. [0071]; “optical transmitter”) and one or more light-receiving components (para. [0071]; “optical receiver”) of the wearable device (wearable device 104; Fig. 1) or motion data collected via one or more accelerometers (para. [0028]; "accelerometer data (e.g., movement/motion data; para. [0075]; "The ring 104 may include one or more motion sensors 245, such as one or more accelerometers); of the wearable device (wearable device 104; Fig. 1); and one or more processors communicatively coupled with the wearable device (para. [0053]; " The processing module 230-a of the ring 104 may include one or more processors (e.g., processing units)"), wherein the one or more processors (Fig. 2; processing module 230-a) are configured to: receive the physiological data acquired via the wearable device via one or more electronic signals (Fig. 2; para. [0053]; "For example, the processing module 230-a may transmit/receive data to/from the modules and other components of the ring 104, such as the sensors"); detect the user is in a sleep interval based at least in part on the physiological data (para. [0032]: "In some aspects, the system 100 may detect periods of time that a user 102 is asleep, and classify periods of time that the user 102 is asleep into one or more sleep stages (e.g., sleep stage classification). For example, as shown in FIG. 1, User 102-a may be associated with a wearable device 104-a (e.g., ring 104-a) and a user device 106-a. In this example, the ring 104-a may collect physiological data associated with the user 102-a, including temperature, heart rate, HRV, respiratory rate, and the like."); a processor configured to activate the one or more light-emitting components and the one or more light-receiving components to transmit light (para. [0067]) and generate a PPG signal, respectively, the PPG signal indicative of an amount of the light received by the one or more light-receiving components (para. [0067]: "The ring 104 may include a PPG system 235. The PPG system 235 may include one or more optical transmitters that transmit light. The PPG system 235 may also include one or more optical receivers that receive light transmitted by the one or more optical transmitters. An optical receiver may generate a signal (hereinafter “PPG” signal) that indicates an amount of light received by the optical receiver.") receive the additional physiological data acquired via the wearable device via one or more additional electronic signals (para. [0012] mentions obtaining and processing both PPG and motion signals; the system of Zhang would be able to acquire both initial and additional physiological data. The physiological data is recited broadly and could include measurements taken after an initial physiological data measurement with the only difference being the time in which the measurements are taken. There is essentially no difference between physiological data and additional physiological data.), the additional physiological data based at least in part on the pulse waveform of the user (para. [0040]: "Example measurements and determinations may include, but are not limited to, user skin temperature, pulse waveforms, respiratory rate, heart rate, HRV, blood oxygen levels, and the like."; para. [0055]: "The processing module 230-a of the ring may be configured to transmit/receive data to/from the user device 106 via the communication module 220-a. Example data may include, but is not limited to, motion data, temperature data, pulse waveforms, heart rate data, HRV data, PPG data."); and comprising at least one of additional heartbeat data (para. [0055]: "PPG data”; As detailed above, what makes heart beat data “additional” is merely a matter of when it is acquired. The system of Zhang would be able to acquire and process additional heartbeat data) or additional motion data (para. [0055]: " motion data"; As detailed above, what makes motion data “additional” is merely a matter of when it is acquired. The system of Zhang would be able to acquire and process additional motion data); input one or more input streams (para. [0116]; "In particular, the motion data collected/processed at 305 through 320, and the PPG data collected and processed at 325 through 335, may be inputted into a machine learning model" into the multi-output neural network (para. [0116]; “machine learning model”), wherein the one or more input streams comprises at least one of a first input stream corresponding to the additional heartbeat data (para. [0116]; Fig. 3; the input stream going from 325 to 335; the input of Zhang could be used for additional or initial heartbeat data since there is no difference between an initial heartbeat measurement or additional heart beat measurement.) or a second input stream corresponding to the additional motion data (para. [0116]; Fig. 3; the input stream going from 305 to 320; The input of Zhang could be used for additional or initial motion data since there is no difference between an initial motion measurements or additional motion measurements), wherein the multi-output neural network comprises a single neural network architecture (para. [0116]; “machine learning model”; The use of “comprises” is open-ended, meaning the disclosure of a machine learning model reads on a single neural network since at least a single neural network is disclosed.),trained to simultaneously compute a plurality of different, separate outputs including one or more values associated with a first physiological metric (para. [0078]; “For example, the processing module 230 may calculate and store various metrics, such as sleep metrics”) and one or more values associated with a second physiological metric (para. [0078; “The ring 104, or other computing/wearable device, may calculate a variety of values/metrics with respect to motion) wherein the first physiological metric and the second physiological metric are distinct and based on a shared set of features from at least one of the first input stream or the second input stream (Fig. 3; The input stream from the PPG data (325) and the motion data (305) are input into 340 to output heart rate (345)); generate, via a single pass of the multi-output neural network, the plurality of different, separate outputs including the one or more values associated with the first physiological metric (para. [0118]; " As such, the machine learning model may be trained to receive PPG data and motion data as inputs, and generate heart rate”) and the one or more values associated with the second physiological metric (para. [0078; “The ring 104, or other computing/wearable device, may calculate a variety of values/metrics with respect to motion); wherein the multi-output neural network is configured to transmit an instruction to a graphical user interface (GUI) (para. [0141]; GUI 500) of a user device associated with the wearable device, the instruction configured to cause the GUI to display one or more messages (The user device may display recommendations and/or information associated with the heart rate data via a message) based at least in part on generating the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric (para. [0025]; " Some electronic devices (e.g., wearable devices 104, user devices 106) may measure physiological parameters of respective users 102, such as photoplethysmography waveforms, continuous skin temperature, a pulse waveform, respiration rate, heart rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry, and/or other physiological parameters"). However, Zhang does not explicitly mention a trigger to acquire additional physiological data throughout a time interval that includes a sleep interval; wherein the one or more values associated with the first physiological metric are indicative of a first probability the user experienced at least one breathing disturbance during the sleep interval; wherein the multi-output neural network is configured to transfer knowledge between different computations of the plurality of different, separate outputs through the single neural network architecture using the shared set of features; or wherein the one or more messages include at least one of a timeline of the at least one breathing disturbance or an index associated with the at least one breathing disturbance.
Wederhorn, in the same field of endeavor of wearable devices, discloses system for detecting a physiological state of a user. Wederhorn discloses a trigger for a wearable device to acquire additional physiological data throughout a time interval that includes the sleep interval (para. [0092]: " Additionally, or alternatively, the user device 106-c may identify that a physiological state of the user, such as a heart rate, a respiratory rate, a sound characteristic, or any combination thereof, satisfies a threshold for triggering the SpO2 measurement. The threshold may be preconfigured, or otherwise defined, at the user device 106-c. For example, if the rate of change of a heart rate of a user exceeds a threshold rate of change, the user device 106-c may trigger an SpO2 measurement at the wearable device 104-d. Similarly, if the rate of change of a respiratory rate of the user exceeds a threshold rate of change, the user device 106-c may trigger an SpO2 measurement at the wearable device 104-d, which is described in further detail with respect to FIG. 4. The user device 106-c may analyze the SpO2 measurements to determine one or more heart rate disturbances, breathing disturbances, or the like; para. [0181]: "Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for determining one or more breathing disturbances associated with the user during a duration associated with the sleep state of the user based at least in part on triggering the oxygen saturation measurement for the user during the duration associated with the sleep state of the user, wherein causing the GUI to display the indication of the measure of the oxygen saturation comprises causing the GUI of the user device to display the one or more breathing disturbances for the user."), receive the additional physiological data acquired via the wearable device via one or more additional electronic signals (para. [0092]; The signal from the SpO2 measurement); and wherein the one or more values associated with the first physiological metric are indicative of a first probability the user experienced at least one breathing disturbance during the sleep interval (para. [0101]: "In some examples, the user device 106-d may categorize (e.g., classify) the breathing disturbances into one or more categories based on breathing disturbance thresholds for each category. For example, the categories may include a very regular breathing pattern 420, a mostly regular breathing pattern 425, an irregular breathing pattern 430, and a very irregular breathing pattern 435, among others. In some examples, the threshold number of breathing disturbances 410 for the very regular breathing pattern 420 may be close to, or at, zero; the threshold number of breathing disturbances 410 for the mostly regular breathing pattern 425 may be between zero and the threshold number of breathing disturbances 410 for the irregular breathing pattern 430 may be between 5 and 10; and the threshold number of breathing disturbances for the very irregular breathing pattern 435 may be over 10. In some other examples, the breathing disturbance 410 thresholds may be tailored to individual users based on individual physiological data and history, and/or based on demographic data from similarly situated users."; the higher the probability a breathing event takes place, the higher the number for an irregular breathing pattern is determined); and wherein the one or more messages (para. [0098]: "messages") include at least one of a timeline of the at least one breathing disturbance (para. [0092]: "For example, the user device 106-c may generate a timeline with the triggered SpO2 measurements. The user device 106-c may display the timeline to a user via a GUI of the user device 106-c.") or an index associated with the at least one breathing disturbance. (para. [0098]: "The user device 106-c may compute the average from the SpO2 measurement report. Additionally, or alternatively, the user device 106-c may display one or more messages based on analyzing the SpO2 measurement report, such as a breathing regularity message 350, which is described in further detail with respect to FIG. 4.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Zhang with the trigger for acquiring additional physiological data during a sleep interval, as disclosed by Wederhorn. One of ordinary skill would recognize that a triggering condition can be used for specific data acquisition only when previous conditions are met. As disclosed by Wederhorn, including a trigger would improve the battery life of the wearable device since it would only acquire data when a condition is met, as opposed to continuous data acquisition that may require a high processing power. Therefore, it would have been an obvious improvement to include a trigger for additional data in the device of Zhang. Additionally, it would have been obvious for one of ordinary skill in the art modify Zhang’s device to predict breathing disturbances, and to display the timeline and index of a disturbance, as disclosed by Wederhorn. Zhang discloses calculating sleep scores for sleep stages. It would have been an obvious improvement to modify Zhang’s device to include message of sleep timelines and indices, as disclosed by Wederhorn, to provide the user feedback about their sleep metrics.
However, neither Zhang nor Wederhorn explicitly disclose where outputs are distinct and separate, based on a shared set of features, or where the neural network is configured to transfer knowledge between different computations of the plurality of different, separate outputs.
Baker, in the same field of endeavor of using neural networks to predict physiological parameters, discloses a neural network used to process physiological data. Baker discloses where outputs are distinct and separate (para. [0020]: “The reader will understand that the systems and methods described herein may be equally adapted to the estimation and display of any physiological parameter of any patient (human or non-human) generated by any monitoring device, including but not limited to pulse rate, blood pressure, temperature, cardiac output, respiration parameters, and measures of blood constituents other than oxygenation.”), based on a shared set of inputs (para. [0036]: “Rather, the neural network, during the process of training, creates a non-linear and complex model that generates an output based on all the inputs.”), and where the neural network is configured to transfer knowledge between different computations of the plurality of different, separate outputs (Fig. 3; The output of the first layer becomes an input of each separate node in the next layer. This is a transfer of data (knowledge between separate computations at each node; para. [0037]: “FIG. 3 illustrates an embodiment of a neural network. The neural network 300 is represented diagrammatically as a series of interconnected nodes. The references 302, 304, 308 designate various network layers, including an input layer 302, an output layer 308 and a hidden layer 304. In FIG. 3 only one hidden layer is represented, but in alternative embodiments the network may comprise two or more hidden layers, each having any number of hidden nodes. In the embodiment shown, the output layer 308 delivers the estimated physiological parameter, in this case SpO.sub.2, in an output 312-1. A second output 312-2 is also illustrated, such as would deliver an estimated accuracy. Depending on the embodiment, the outputs 312 and inputs 310 may be single values or may represent ongoing streams of data.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the system of Zhang to further include where the outputs are distinct and separate, based on a shared set of features, and where the network transfers knowledge between computations of outputs, as disclosed by Baker. One of ordinary skill would recognize that it would have been obvious to include different output types than just heart rate. Further, Baker suggests a plethora of modifications for output types for a multi-output neural network. One of ordinary skill would also recognize that sharing features would improve efficiency, as multiple calculations could be made with the same input. Lastly, one of ordinary skill would recognize that sharing knowledge between nodes would be advantageous in that it would improve the efficiency of the computation time for the device. Therefore, it would have been obvious to modify the device of Zhang to further include the features of Baker.
Regarding claim 2, Zhang, Wederhorn and Baker, in combination, disclose the system of claim 1 (see above). Further, Zhang discloses wherein the physiological data comprises temperature data (para. [0077]; “The ring 104 may store a variety of data described herein. For example, the ring 104 may store temperature data, such as raw sampled temperature data and calculated temperature data.”), wherein the one or more input streams comprises at least one of the first input stream, the second input stream, or a third input stream corresponding to the temperature data (para. [0077]; “The ring 104 may store a variety of data described herein. For example, the ring 104 may store temperature data, such as raw sampled temperature data and calculated temperature data.”). However, Zhang doesn’t expressly disclose wherein the first physiological metric and the second physiological metric are based on the shared set of features from at least one of the first input stream, the second input stream, or the third input stream, or a combination thereof.
Baker discloses wherein the first physiological metric (Fig. 3; 312-1) and the second physiological metric (312-2) are based on the shared set of features from at least one of the first input stream, the second input stream, or the third input stream, or a combination thereof (Fig. 3; para. [0037]: “FIG. 3 illustrates an embodiment of a neural network. The neural network 300 is represented diagrammatically as a series of interconnected nodes. The references 302, 304, 308 designate various network layers, including an input layer 302, an output layer 308 and a hidden layer 304. In FIG. 3 only one hidden layer is represented, but in alternative embodiments the network may comprise two or more hidden layers, each having any number of hidden nodes. In the embodiment shown, the output layer 308 delivers the estimated physiological parameter, in this case SpO.sub.2, in an output 312-1. A second output 312-2 is also illustrated, such as would deliver an estimated accuracy. Depending on the embodiment, the outputs 312 and inputs 310 may be single values or may represent ongoing streams of data.”)
It would have been obvious for one of ordinary skill in the art to further modify the device of Zhang to include multiple input streams that share that share features, as disclosed by Baker, with the system of claim 1, as disclosed by Zhang and Baker in combination. One of ordinary skill would recognize that doing so would improve the efficiency of the system to by sharing input data. This would improve the computation efficiency of the system.
Regarding claim 3, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 2 (see above). Zhang further discloses wherein the physiological data comprises blood oxygen data (para. [0077]; “The ring 104 may store a variety of data described herein. For example, the ring 104 may store temperature data, such as raw sampled temperature data and calculated temperature data.”), wherein the one or more input streams comprises at least one of the first input stream, the second input stream, a third input stream, or a fourth input stream corresponding to the blood oxygen data (para. [0077]; “The ring 104 may store a variety of data described herein. For example, the ring 104 may store temperature data, such as raw sampled temperature data and calculated temperature data.”). However, Zhang doesn’t expressly disclose wherein the first physiological metric and the second physiological metric are based on the shared set of features from at least one of the first input stream, the second input stream, or the third input stream, or a combination thereof.
Baker discloses wherein the first physiological metric (Fig. 3; 312-1) and the second physiological metric (312-2) are based on the shared set of features from at least one of the first input stream, the second input stream, or the third input stream, or the fourth input stream, or a combination thereof (Fig. 3; para. [0037]: “FIG. 3 illustrates an embodiment of a neural network. The neural network 300 is represented diagrammatically as a series of interconnected nodes. The references 302, 304, 308 designate various network layers, including an input layer 302, an output layer 308 and a hidden layer 304. In FIG. 3 only one hidden layer is represented, but in alternative embodiments the network may comprise two or more hidden layers, each having any number of hidden nodes. In the embodiment shown, the output layer 308 delivers the estimated physiological parameter, in this case SpO.sub.2, in an output 312-1. A second output 312-2 is also illustrated, such as would deliver an estimated accuracy. Depending on the embodiment, the outputs 312 and inputs 310 may be single values or may represent ongoing streams of data.”)
It would have been obvious for one of ordinary skill in the art to further modify the device of Zhang to include multiple input streams that share that share features, as disclosed by Baker, with the system of claim 1, as disclosed by Zhang and Baker in combination. One of ordinary skill would recognize that doing so would improve the efficiency of the system to by sharing input data. This would improve the computation efficiency of the system.
Regarding claim 4, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). Zhang further discloses wherein the multi-output neural network includes weights (para. [0034]: “0034] In some aspects, the system 100 may utilize other biological rhythms to further improve physiological data collection, analysis, and processing by phase of these other rhythms. For example, if a weekly rhythm is detected within an individual's baseline data, then the model may be configured to adjust “weights” of data by day of the week.”). However, Zhang never expressly discloses wherein a training accuracy associated with training the multi-output neural is increased during training of the weights.
Baker discloses wherein a training accuracy associated with training the multi-output neural is increased during training of the weights (para. [0035]: “In embodiments of monitoring systems described in this disclosure, a neural network is used to create a more accurate estimate of a physiological parameter. In addition, the neural network may be used to generate an estimate of the accuracy of said physiologic parameter estimate. For example, in an embodiment the neural network is used to combine one or more estimates of a physiologic parameter with one or more associated signal quality metrics, creating a more accurate estimate of said physiologic parameter.” ; para. [0036]: “It should be noted that, when a neural network is used to combine two or more input estimates of a physiologic parameter, a neural network is not performing a simple selection or interpolation between the two input parameters. Rather, the neural network, during the process of training, creates a non-linear and complex model that generates an output based on all the inputs.”; para. [0039]: “To each node of the neural network 300 there corresponds a function F, which may be a nonlinear activation function such as a hyperbolic tangent function or a sigmoid function, as well as an activation level. Moreover, in the embodiment shown each node i of each layer is linked to the nodes j of the next layer; and a weighting Pij weights each link between a node i and a node j. This weighting weights the influence of the result of each node i in the calculation of the result delivered by each node j to which it is linked.”; Baker discloses narratively that the neural network is used to increase accuracy, and training weights are used at the nodes).
It would have been obvious for one of ordinary skill in the art to include the weights specifically for increasing accuracy during training, as disclosed by Baker, with the device of claim 1. One of ordinary skill in the art would recognize that using training weights to make the neural network more accurate would be an improvement to the device of Zhang, which discloses a similar device that already uses weights. Using the weights to increase accuracy would have been obvious for one of ordinary skill in the art, as Baker uses the weights for a similar purpose.
Regarding claim 5, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). Zhang further discloses wherein the multi-output neural network is trained to simultaneously compute one or more values associated with a third physiological metric (para. [0017]; “For instance, the machine learning model may be configured to differentiate between candidate heart rate measurements that are attributable to motion artifacts from candidate heart rate measurements that are indicative of the user's actual heart rate…By way of another example, the machine learning model may be configured to identify time-domain and/or frequency-domain features within the received PPG data and motion data (which may be used to identify candidate heart rate measurements), and may be configured to determine or estimate a heart rate for the user based on the identified features”), wherein the one or more processors are further configured to: generate, via the single pass of the multi-output neural network, the one or more values associated with the third physiological metric, wherein the third physiological metric is distinct from the first physiological metric and the second physiological metric (para. [0193]; The code may include instructions executable by a processor to receive physiological data associated with the user, the physiological data comprising PPG data and motion data collected throughout a first time interval via a wearable device associated with the user, determine a set of candidate heart rate measurements within the first time interval based at least in part on the PPG data, select a first heart rate measurement from the set of candidate heart rate measurements based at least in part on the received motion data, and determine a first heart rate for the user within the first time interval based at least in part on the selected first heart rate measurement). However, Zhang does not expressly disclose wherein the third physiological metric is based on a shared set of features.
Baker, in the same field of endeavor, discloses where the third physiological metric (para. [0017]: “For instance, the machine learning model may be configured to differentiate between candidate heart rate measurements that are attributable to motion artifacts from candidate heart rate measurements that are indicative of the user's actual heart rate…By way of another example, the machine learning model may be configured to identify time-domain and/or frequency-domain features within the received PPG data and motion data (which may be used to identify candidate heart rate measurements), and may be configured to determine or estimate a heart rate for the user based on the identified features”) is based on a shared set of features. (Fig. 3; multiple inputs and multiple outputs are shown for the neural network.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include a third physiological metric based on shared features or inputs, as disclosed by Baker, with the system of claim 1, as disclosed by Zhang in combination with Baker. One of ordinary skill would recognize that doing so would improve the efficiency of the system to by sharing input data. This would improve the computation efficiency of the system.
Regarding claim 6, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim, 1 (see above). Further, Baker discloses wherein the one or more processors are further configured to: train the multi-output neural network based at least in part on inputting the one or more input streams (Fig. 3; Inputs 310-1 – 310-n) into the multi-output neural network and generating the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric (para. [0036]; “Rather, the neural network, during the process of training, creates a non-linear and complex model that generates an output based on all the inputs.”; Para. [0007]; “The pulse oximeter includes: a microprocessor capable of calculating an estimated value of oxygen saturation of a patient's blood at least in part upon information received from a sensor; a neural network module capable of receiving the estimated value as an input and calculating a revised value of oxygen saturation of a patient's blood using a neural network”).
It would have been obvious to one of ordinary skill in the art to combine the neural network configured to be trained with the system of Zhang since doing so would improve the processing speed and accuracy of Zhang’s physiological data. Further, it would have been obvious to use multiple physiological outputs with Baker’s neural network since Baker discloses adapting the neural network for other types of outputs [para. 0020]). Doing this would enhance Zhang’s system by enabling it to predict multiple physiological values and metrics based on multiple physiological data types.
Regarding claim 7, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). Zhang further discloses wherein the one or more processors are further configured to: train the multi-output neural network based on a plurality of features within a training physiological dataset associated with a plurality of users (para. [0119]; the system 200 may train multiple versions of a machine learning model, such as for different demographics of users (e.g., different age groups, varying levels of activity/performance, different skin tones, etc.), for users with varying medical conditions, and the like. In this regard, different models which are tailored to different demographics of users may be used to further fine-tune the ability of the respective models to perform heart rate detection. For example, the system 200 may acquire physiological data from a user who is an avid runner, and may utilize a machine learning model trained on data from other runners to perform heart rate detection for the user.”), wherein the plurality of features includes at least one of a blood oxygen saturation, body temperature, pulse wave amplitude, heart rate, intensity of motion, or ambient noise level (para. [0040]: “Example measurements and determinations may include, but are not limited to, user skin temperature, pulse waveforms, respiratory rate, heart rate, HRV blood oxygen levels, and the like.”).
Regarding claim 8, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). Further, Zhang discloses wherein the one or more processors are further configured to: simultaneously pre-process the one or more input streams prior to inputting the one or more input streams into the multi-output neural network. (para. [0099]; “At 310, the wearable device, the user device, the servers, or any combination thereof, may preprocess the motion data.”; para. [0105]; “At 330, the wearable device, the user device, the servers, or any combination thereof, may preprocess the PPG data.”; Both 310 and 330 process physiological data simultaneously prior to merging the data and calculating the heart rate output).
Regarding claim 9, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). Further, Zhang discloses wherein the second physiological metric is indicative of a second probability the user experienced one of a deep sleep or light sleep during a sleep period (para. [0032]: “Moreover, the machine learning classifier may be configured to classify periods of time into different sleep stages, including an awake sleep stage, a rapid eye movement (REM) sleep stage, a light sleep stage (non-REM (NREM)), and a deep sleep stage (NREM).”; para. [0087]: “In some implementations, each overall score for a user for each respective day (e.g., Sleep Score, Readiness Score) may be determined/calculated based on one or more “contributors,” “factors,” or “contributing factors.” For example, a user's overall Sleep Score may be calculated based on a set of contributors, including: total sleep, efficiency, restfulness, REM sleep, deep sleep, latency, timing, or any combination thereof.”; para. [0089]: “By way of another example, a user's overall Readiness Score may be calculated based on a set of contributors, including: sleep, sleep balance, heart rate, HRV balance, recovery index, temperature, activity, activity balance, or any combination thereof. The Readiness Score may include any quantity of contributors.”; Additionally, para. [0032] discloses “sleep stages,” which discloses that patterns re determined during sleep periods).
Regarding claim 10, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). Further, Zhang discloses wherein the one or more values associated with the first physiological metric (para. [0093]; “PPG data”) and the one or more values associated with the second physiological metric (para. [0093]; “motion data.”) have the same time scale. (para. [0093]; “Additionally, or alternatively, the machine learning model may be configured to identify time-domain and/or frequency-domain features within the received PPG data and motion data; para. [0112]: “In some aspects, the system 200 may use the motion data such as the merged motion data, the motion intensity data, intensity change rate data, or a combination thereof to determine which of the multiple strong PPG signals (e.g., candidate PPG signals) are due to motion, and which PPG signal represents the user's actual heart rate.” Zhang demonstrates that both motion and PPG data are configured to identify the time and frequency domain features, and in [0112], Zhang discloses that the motion and PPF data are compared to determine if heart beat abnormalities are caused by motion. Therefore, the first and second physiological measurements would have the same time scale).
Regarding claim 11, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). Further, Zhang discloses wherein the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric have the same resolution (para. [0093]; “Additionally, or alternatively, the machine learning model may be configured to identify time-domain and/or frequency-domain features within the received PPG data and motion data; para. [0112]: “In some aspects, the system 200 may use the motion data such as the merged motion data, the motion intensity data, intensity change rate data, or a combination thereof to determine which of the multiple strong PPG signals (e.g., candidate PPG signals) are due to motion, and which PPG signal represents the user's actual heart rate.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the first and second physiological measurements of Zhang to have the same resolution. Zhang discloses that the PPG and motion signals are compared in the time and frequency domain to determine if a change in heart rate is caused by the motion data. As disclosed by Zhang in para. [0035], preserving the temporal resolution of the rhythms and assigning a phase to a time helps improve the comparison of signals. One of ordinary skill would recognize that it would have been obvious to have the same resolution for heart beat (PPG) and motion data since this would allow for improved signal comparison.
Due to the lack of specificity in the claim as to what is considered a resolution, the claim is interpreted that a resolution can be any type of resolution. Additionally, the specification recites a “thirty second resolution (e.g., time scale) (para. [0131]).” Therefore, using the broadest reasonable interpretation of a resolution to mean any type of resolution, Zhang’s disclosure of time scales read as resolutions.
Regarding claim 12, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). Further, Zhang discloses wherein the physiological data is acquired throughout the time interval that includes one or more sleep intervals of the user. (para. [0032]; “In this example, the ring 104-a may collect physiological data associated with the user 102-a, including temperature, heart rate, HRV, respiratory rate, and the like. In some aspects, data collected by the ring 104-a may be input to a machine learning classifier, where the machine learning classifier is configured to determine periods of time that the user 102-a is (or was) asleep. Moreover, the machine learning classifier may be configured to classify periods of time into different sleep stages…”).
Regarding claim 13 Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). Further, Zhang discloses wherein the second physiological metric comprises at least one of a sleep staging metric, a blood oxygen metric, a blood pressure metric, a heartbeat metric, or a respiratory rate metric. (para. [0040]; “Example measurements and determinations may include, but are not limited to, user skin temperature, pulse waveforms, respiratory rate, heart rate, HRV, blood oxygen levels, and the like).
Regarding claim 14, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). Further, Zhang discloses wherein the wearable device comprises a wearable ring device. (Fig. 1; para. [0040]; “ring 104”).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20230114833 A1, "Zhang"), Wederhorn et al. (US 20230397851 A1, “Wederhorn”), Baker et al. (US 20090287070 A1, “Baker”), and Pho et al. (US 20220409187 A1, “Pho”).
Regarding claim 9, Zhang, Wederhorn, and Baker, in combination, disclose the system of claim 1 (see above). However, the references do not disclose wherein the second physiological metric is indicative of a second probability the user experienced one of a deep sleep or light sleep during a sleep period ()
Pho, in the same field of endeavor of wearable sensor used to detect physiological events, discloses a wearable device used to detect illness and sleep patterns. Pho discloses wherein each of the first physiological metric and the second physiological metric (para. [0055]: “In some aspects, data collected by the ring 104-a may be input to a classifier, where the classifier is configured to determine illness risk metrics (or other metrics) associated with a likelihood or probability…”) is associated with a probability that the user experienced an associated physiological event (para. [0054]: “illness prediction metrics…”; These are used to predict an associated illness related to the metric.) during a time period (para. [0054]; “In some implementations, techniques described herein may compare physiological data (and rhythm parameters thereof) collected over different time intervals (e.g., first/reference time interval, second/prediction time interval) to identify a satisfaction of deviation criteria, where the satisfaction of one or more deviation criteria may be used to predict illness risk metrics (e.g., “risk scores”), illness prediction metrics, illness severity metrics, illness recovery metrics, and the like.), and the associated physiological event is one of a breathing disturbance, deep sleep, or light sleep. (para. [0047]: “In some aspects, the system 100 may detect periods of time during which a user 102 is asleep, and classify periods of time during which the user 102 is asleep into one or more sleep stages (e.g., sleep stage classification… where the machine learning classifier is configured to determine periods of time during which the user 102-a is (or was) asleep. Moreover, the machine learning classifier may be configured to classify periods of time into different sleep stages, including an awake sleep stage, a rapid eye movement (REM) sleep stage, a light sleep stage (non-REM (NREM)), and a deep sleep stage (NREM)”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and physiological metrics of Zhang with the association between physiological metrics and probability metrics, as disclosed by Pho, since doing so would enable Zhang’s device to not only calculate metrics, but also predict if the user is experiencing a phenomenon. Further, it would have been obvious to one of ordinary skill to try and predict multiple types of physiological phenomenon such as those disclosed in Zhang to increase the range of possible physiological phenomenon the device is capable of detecting. It also would have been obvious to include specific events, such as a breathing disturbance, light sleep, or deep sleep since Pho discloses predicting these events with a similar device. One would recognize that these events could be predicted with a reasonable expectation of success. Therefore, it would have been obvious to include these events in the system of claim 1.
Conclusion
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/O.L.M./Examiner, Art Unit 3796 /ALLEN PORTER/Primary Examiner, Art Unit 3796
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