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 .
The amendment filed 06/04/2025 has been entered. Claims 1-5, 9, 13-14, 16-26, 28, and 30 remain pending in the application. Applicant’s amendments to the Specification, Drawings, and Claims have overcome each and every 112(d) rejection previously set forth in the Non-Final Office Action mailed 02/04/2025.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 9, 13-14, 16-26, and 28 are rejected under 35 U.S.C. 101 because the claimed invention, considering all claim elements both individually and in combination as a whole, is directed to an abstract idea without significantly more.
Claim 1 recites a process, machine, manufacture, or composition of matter, in this case a machine, and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 1 meets the first prong of step 2A analysis as it is directed to an abstract idea, evidenced by the limitations, “receive an acoustic signal representing acoustic vibrations within a cushioning layer supporting at least a portion of the user, wherein the acoustic signal comprises a cardiac signal indicative of one or more cardiac cycles of the user,” and “determining, based on an amplitude of a first heart sound of the cardiac signal and an amplitude of a second heart sound of the cardiac signal, the body position of the user, wherein the first heart sound indicates a beginning of a systole phase of a cardiac cycle and the second heart sound indicates an end of the systole phase of the cardiac cycle.”. These limitations, under broadest reasonable interpretation, encompass subject matter that may be performed by a human using mental steps or with pen and paper, (observing sensor data, determining a “state”). The claim further meets prong 2 of the step 2A analysis because the judicial exception is not integrated into a practical application. The limitations do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). Therefore, prong 2 of the step 2A analysis is satisfied and step 2B must be considered. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element is “one or more processors”. This element is not “significantly more” because it is well-known, routine, and conventional as evidenced by Sayadi et al. (US 2019/0209405 A1), para [0038]: “a processor”. Therefore, this element does not add anything significantly more than a judicial exception.
In view of the above, independent claim 1 fails to recite patent-eligible subject matter under 35 U.S.C 101. Dependent claims 2-5, 9, 16-26 fail to cure the deficiencies of independent claim 1 by merely reciting additional abstract ideas already recited and/or additional elements that are not significantly more. Thus, claims 1, 2-5, 9, 16-26, and 30 are rejected under 35 U.S.C 101.
Claim 28 recites a process, machine, manufacture, or composition of matter, in this case a machine, and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 28 meets the first prong of step 2A analysis as it is directed to an abstract idea, evidenced by the limitations, “receive an acoustic signal representing acoustic vibrations within a cushioning layer supporting at least a portion of the user, wherein the acoustic signal comprises a cardiac signal indicative of one or more cardiac cycles of the user,” and “based on an amplitude of a first heart sound of the cardiac signal and an amplitude of a second heart sound of the cardiac signal, the body position of the user, wherein the first heart sound indicates a beginning of a systole phase of a cardiac cycle and the second heart sound indicates an end of the systole phase of the cardiac cycle.”. These limitations, under broadest reasonable interpretation, encompass subject matter that may be performed by a human using mental steps or with pen and paper, (observing sensor data, determining a “state”). The claim further meets prong 2 of the step 2A analysis because the judicial exception is not integrated into a practical application. The limitations do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). Therefore, prong 2 of the step 2A analysis is satisfied and step 2B must be considered. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In view of the above, claim 28 fails to recite patent-eligible subject matter under 35 U.S.C 101. Thus, claim 28 is rejected under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 3-5, 9, 13-14, 16-18 22, 26, 28 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sayadi et al (US 2019/0209405 A1), hereinafter Sayadi 2019’, in view of Sayadi et al. (US 20200163627 A1), hereinafter Sayadi 2020’.
Regarding claim 1, Sayadi discloses a system for use in monitoring one or more physiological states of a user ([0033]: "physiological state of a user or users that are on the bed"), the one or more physiological states of the user comprising a body position of the user ([0048]: "when a user lies on the bed 112 positioned over the chamber 114A, each of the user's heart beats, breaths, and other movements can create a force on the bed 112 that is transmitted to the chamber 114A.", wherein the signals are indicative of the user's position on the chamber), the system comprising one or more processors ([0038]: “a processor 136”) configured to: receive an acoustic signal representing acoustic vibrations within the cushioning layer ([0033]: "an airbed may collect pressure and acoustic signals for a particular user over a period of time.", [0226]: “acoustic readings to identify physiological states”) wherein the acoustic signal comprises a cardiac signal indicative of one or more cardiac cycles of the user ([0046]: “heart rhythm”).
While Sayadi 2019’ discloses determination the body position of the user based on a cardiac signal ([0226]: “use the stream of pressure readings and the stream of acoustic readings to identify physiological states ”), they fail to disclose specifically determining, based on an amplitude of a first heart sound of the cardiac signal and an amplitude of a second heart sound of the cardiac signal, the body position of the user, wherein the first heart sound indicates a beginning of a systole phase of a cardiac cycle and the second heart sound indicates an end of the systole phase of the cardiac cycle.
Sayadi 2020’ discloses determining, based on an amplitude of a first heart sound of the cardiac signal and an amplitude of a second heart sound of the cardiac signal (Fig 10 element 1025 “peak detection” [0046]: “analyzing the bio-signal's amplitude and phase in different frequency bands,”), the body position ([0041]: “heart beating and can use its corresponding amplitude or phase data to determine where on the substrate the heart is located, thereby assisting in determining in what location, angular orientation, and body position the subject is laying as described and shown herein.”), wherein the first heart sound indicates a beginning of a systole phase of a cardiac cycle and the second heart sound indicates an end of the systole phase of the cardiac cycle ([0090]: “Beat components are determined for each of the enhanced individual cardiac beats. The beat components depend on the cardiac model. For example, the beat components can be P, Q, R, S and T waveforms, diastolic/systolic waveforms”, [0042]: “By analyzing the phase differences in the 0 to 0.5 Hz range, it can be determined if the person is supine, prone or laying on their side, as non-limiting examples.”, wherein phase differences are indicative of body position).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to expand the position detection system disclosed by Sayadi 2019’ to include determination of a body position based on the amplitude of a cardiac signal as disclosed by Sayadi 2020’ in order to improve body position determination (Sayadi 2020’ [0041]: “enabling better isolation of a subject's bio-signal”).
Regarding claim 3, Sayadi 2019’ discloses one or more physiological states of the user comprise at least one a body position of the user; a level of hydration of the user; or one or more abnormal physiological states of the user ([0033]: "no event, heart attack event, fever event, movement disorder event, apnea event, snore event, body cooling even").
Regarding claim 4, Sayadi 2019’ discloses the pressure signal comprises a respiration signal indicative of respiration of the user ([0045]: "the processor 136 can use information collected by the pressure transducer 146 to determine a heart rate or a respiration rate for a person lying in the bed") and the determination of the one or more physiological states of the user is based on the respiration signal ([0033]: “categorize live pressure and/or acoustic signals into a physiological event state”); and the acoustic signal comprises a cardiac signal indicative of one or more cardiac cycles of the user ([0005]: "cardiac signals determined from at least one of the group consisting of the second pressure readings and the second acoustic readings.") and the determination of the one or more physiological states of the user is based on the cardiac signal ([0005]: “At least one of the physiological event classifiers is configured to classify an apnea event using at least cardiac signals”).
Regarding claim 5, Sayadi 2019’ discloses the pressure signal comprises a respiration signal indicative of respiration of the user ([0045]: "and this information can be used to determine the user's heart rate and/or respiration rate."), the determination of the one or more physiological states of the user is based on the respiration signal ([0033]), the one or more physiological states of the user comprise one or more abnormal physiological states of the user determined based on changes in the respiration signal ([0208]: “apnea”), and the one or more abnormal physiological states of the user include at least one of one or more of the following: an interruption in breathing determined in response to a detection of an absence of change in the respiration signal for a predetermined duration ([0208]: "apnea events "); or an irregularity in breathing pattern determined in response to a detection of irregular changes in respiration rate of the respiration signal ([0208]: “difficulty breathing”).
Regarding Claim 9, Sayadi 2019’discloses the acoustic signal comprises a cardiac signal indicative of one or more cardiac cycles of the user ([0005]: "cardiac signals determined from at least one of the group consisting of the second pressure readings and the second acoustic readings."), the determination of the one or more physiological states of the user is based on the cardiac signal ([0277]: “determining out-of-norm physiological signals… This can include a cardiac signal,”), the one or more physiological states of the user comprise one or more abnormal physiological states of the user determined based on changes in the cardiac signal ([0277]), and the one or more abnormal physiological states include at least one of one or more of the following: an interruption in heartbeat determined in response to a detection of an absence of change in the cardiac signal for a predetermined duration ([0284]: “arrhythmias,”); an irregularity in heartbeat determined in response to a detection of irregular changes in heart rate of the cardiac signal ([0284]: “high/low physiological values (heart rate”); and presence of one or more abnormal sounds synchronous with heartbeat of the user.
Regarding claim 13, Sayadi 2019’discloses the one or more physiological states of the user comprise one or more abnormal physiological states of the user determined based on changes in the pressure signal and acoustic signal ([0208]: "uses the reading of pressure/acoustic sensors… identifies physiological events of the user”) wherein the one or more abnormal physiological states include at least one of presence of one or more abnormal sounds synchronous with breathing; death; seizure; or bruxism ([0208]: “heart attacks, fever states, difficulty breathing, apnea events and movement disorders” and [0223]: "a physiological event critical to health is identified (e.g., heart attack, seizure).").
Regarding claim 14, Sayadi 2019’discloses the determination of the one or more physiological states of the user based on the respiration signal and the cardiac signal comprises the determination of at least one of: death is determined in response to a detection of an absence of the cardiac signal or an absence of changes in the cardiac signal whilst the respiration signal continues to be detected or changes in the respiration signal continue to be detected for a predefined time interval; or seizure ([0223]: “seizure”) in response to a detection that the pressure signal or respiration signal exceeds an amplitude threshold and a frequency of the acoustic signal or cardiac signal exceeds a frequency threshold ([0240]: “For example, a classifier may include a threshold value (e.g., pressure, pressure change over time), and an acoustic measure over that threshold may be used to identify one physiological event while acoustic readings under that threshold may be used to determine another physiological event.”)
Regarding claim 16, Sayadi 2019’discloses determining the one or more physiological states of the user comprises: obtaining an input derived from one or both of the pressure signal and acoustic signal ([0227]: "The physiological event analyzer uses one or more machine learning classifiers to classify frames of pressure and/or acoustic readings into physiological events or lack of physiological event."); inputting the input into a machine learning model configured to determine, for each of a set of potential physiological states, a probability that the user has the corresponding physiological state ([0228-0229]: “bed controller can select a vote-winning physiological state”, wherein the votes are weighted and represent the likelihood of the event occurring) and determining the one or more physiological states based on each probability output from the machine learning model ([0229]: “can count the votes for each physiological event and the physiological events with the most votes is the determined physiological event.”).
Regarding claim 17, Sayadi 2019’discloses the pressure signal comprises a respiration signal indicative of respiration of the user ([0045]), and the one or more physiological states of the user comprise a body position of the user detected ([0046]: “the pressure transducer 146 can be used to detect the user's presence on the bed 112, e.g., via a gross pressure change determination and/or via one or more of a respiration rate signal, heart rate signal, and/or other biometric signals”) further based on a relative phase of the pressure signal to the acoustic signal ([0202]: “the digital pressure frames 1924 can overlap. For example, each frame may be 100 ms long, and may overlap the previous digital acoustic frame by 50 ms and may overlap the next digital acoustic frame by 50 ms.”, [0204]: “A event analyzer 1926 can also use the digital acoustic frames 1912 and digital pressure frames 1924 in order to make identifications of physiological events.”, as the system may use physiological signals to determine user presence, the overlapping signal collected by the device may be indicative of body position).
Regarding claim 18, Sayadi 2019’ discloses the one or more processors are further configured to detect a cardiac pressure signal of the user in the pressure signal ([0050]: “such as blood pressure") and wherein the determination of a body position of the user is further based on the cardiac pressure signal ([0046]).
Regarding claim 22, Sayadi 2019’ discloses wherein the one or more processors are further configured to output an alert if the system determines that the body position of the user has remained unchanged for a predetermined duration of time ([0209]: “user may configure the bed to issue an audible alarm if unhealthy events”, wherein the “unhealthy event” determined from live readings may be lack of movement as per [0050]: “tossing and turning movements, rolling movements, limb movements, weight, the presence or lack of presence of a user”).
Regarding claim 26, Sayadi 2019’ discloses the one or more processors are further configured to output a control signal to control an external device based on the user's physiological state ([0067]: “control signal”), wherein the one or more physiological states comprise a level of consciousness of the user ([0049]: “With regard to sleep state, air bed system 100 can determine a user's sleep state by using various biometric signals such as heart rate, respiration, and/or movement of the user.”, wherein “sleep state” is interpreted as a level of consciousness) and wherein the one or more processors are configured to output the control signal in response to determining that the level of consciousness falls within a threshold range ([0067] “transmit the control signals to the other devices in response to information collected by the control circuitry 334, including bed presence of the user 308, sleep state of the user 308, and other factors.”)
Regarding claim 28, Sayadi 2019’ discloses a computer-implemented method ([0171]: “a computing device 1800 and an example of a mobile computing device that can be used to implement the techniques described here”) for determining one or more physiological states of a user, ([0033]: "physiological state of a user or users that are on the bed"), the one or more physiological states of the user comprising a body position of the user ([0048]: "when a user lies on the bed 112 positioned over the chamber 114A, each of the user's heart beats, breaths, and other movements can create a force on the bed 112 that is transmitted to the chamber 114A.", wherein the signals are indicative of the user's position on the chamber), the system comprising one or more processors ([0038]: “a processor 136”) configured to: receive an acoustic signal representing acoustic vibrations within the cushioning layer ([0033]: "an airbed may collect pressure and acoustic signals for a particular user over a period of time.", [0226]: “acoustic readings to identify physiological states”) wherein the acoustic signal comprises a cardiac signal indicative of one or more cardiac cycles of the user ([0046]: “heart rhythm”).
While Sayadi 2019’ discloses determination the body position of the user based on a cardiac signal ([0226]: “use the stream of pressure readings and the stream of acoustic readings to identify physiological states ”), they fail to disclose specifically determining, based on an amplitude of a first heart sound of the cardiac signal and an amplitude of a second heart sound of the cardiac signal, the body position of the user, wherein the first heart sound indicates a beginning of a systole phase of a cardiac cycle and the second heart sound indicates an end of the systole phase of the cardiac cycle.
Sayadi 2020’ discloses determining, based on an amplitude of a first heart sound of the cardiac signal and an amplitude of a second heart sound of the cardiac signal (Fig 10 element 1025 “peak detection” [0046]: “analyzing the bio-signal's amplitude and phase in different frequency bands,”), the body position ([0041]: “heart beating and can use its corresponding amplitude or phase data to determine where on the substrate the heart is located, thereby assisting in determining in what location, angular orientation, and body position the subject is laying as described and shown herein.”), wherein the first heart sound indicates a beginning of a systole phase of a cardiac cycle and the second heart sound indicates an end of the systole phase of the cardiac cycle ([0090]: “Beat components are determined for each of the enhanced individual cardiac beats. The beat components depend on the cardiac model. For example, the beat components can be P, Q, R, S and T waveforms, diastolic/systolic waveforms”, [0042]: “By analyzing the phase differences in the 0 to 0.5 Hz range, it can be determined if the person is supine, prone or laying on their side, as non-limiting examples.”, wherein phase differences are indicative of body position).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to expand the position detection system disclosed by Sayadi 2019’ to include determination of a body position based on the amplitude of a cardiac signal as disclosed by Sayadi 2020’ in order to improve body position determination (Sayadi 2020’ [0041]: “enabling better isolation of a subject's bio-signal”).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Sayadi 2019’ in view of Sayadi 2020’ in view of Benson (US US20150164238A1), hereinafter Benson.
Sayadi discloses the system of claim 30 but fails to disclose the pressure signal represents pressure oscillations at a lower frequency than the acoustic vibrations.
Benson discloses a sensor system embedded within a mattress (abstract) wherein the pressure signal represents pressure oscillations at a lower frequency than the acoustic vibrations ([0213]: “0.5-4 Hz” for the force sensors, [0259]: “70-2000 Hz” for acoustic sensors).
It would have been obvious to a person of ordinary skill in the art to clarify the system disclosed by Sayadi to state that the pressure signal oscillations are of a lower frequency than the acoustic vibrations as disclosed by Benson in order to more accurately characterize physiological events, such as apnea (Benson [0259]) as compared to respiration rate (Benson [0221]).
Claims 19 is rejected under 35 U.S.C. 103 as being unpatentable over Sayadi 2019’ in view of Sayadi 2020’ in view of Muhlsteff et al. (US 20170202463 A1), hereinafter Muhlsteff.
Sayadi 2019’ as modified by Sayadi 2020’ discloses the system of claim 18 but fails to disclose the cardiac pressure signal is detected over a measurement window starting at a start measurement time and ending at an end measurement time, wherein the start measurement time is triggered by a first feature in the cardiac signal representing a start of the systolic phase of the cardiac signal and the end measurement time is triggered by a second feature in the cardiac signal representing an end of the systolic phase of the cardiac signal.
Muhlsteff discloses a system for monitoring physiological signals of a subject (abstract) wherein the cardiac pressure signal is detected over a measurement window starting at a start measurement time and ending at an end measurement time ([0026]), wherein the start measurement time is triggered by a first feature in the cardiac signal representing a start of the systolic phase of the cardiac signal ([0026]: “Preferably, said evaluation unit is configured to determine a start signal of a PAT measure from the sensor signal”) and the end measurement time is triggered by a second feature in the cardiac signal representing an end of the systolic phase of the cardiac signal ([0026]: “and an end signal of the PAT measure from a systolic measurement value of the PPG signal.”).
As Sayadi discloses a system that extracts windows of data from the obtained pressure and acoustic signal (Sayadi [0259]: “raw pressure data can be separated into rolling windows of pressure data and raw acoustic data can be separated into rolling windows of acoustic data”), it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Sayadi to include start and stop times for the measurement window based on systolic blood pressure in order to more efficiently obtain areas of interest for data analysis, increasing the accuracy of the system and decreasing the technical effort (Mulsteff [0026]: “A start time and an end time can thus be determined with low technical effort and high precision.”).
Claims 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Sayadi 2019’ and Sayadi 2020’ in further view Sarabia Carrazo et al. (US 2014/0187871 A1).
Regarding claim 20, Sayadi 2019’ as modified by Sayadi 2020’ discloses the system of claim 18 but fails to disclose the determination of the body position of the user is further based on a phase of the cardiac pressure signal during the systolic phase of a cardiac cycle shown within the cardiac pressure signal.
Sarabia Carrazo discloses a system to monitor physiological signals during sleep (abstract) wherein the determination of the body position of the user is based on a phase of the cardiac pressure signal during the systolic phase of a cardiac cycle shown within the cardiac pressure signal (Figure 3, wherein body position is represented by dotted line and systolic blood pressure is represented by the empty circles, [0022]: “direct relationship between the position measured on the arm and blood pressure”).
Sayadi 2020’ discloses that body position may be determined via a phase of the cardiac signal but does not specifically state that the phase is systolic ([0090]: “Beat components are determined for each of the enhanced individual cardiac beats. The beat components depend on the cardiac model. For example, the beat components can be P, Q, R, S and T waveforms, diastolic/systolic waveforms”, [0042]: “By analyzing the phase differences in the 0 to 0.5 Hz range, it can be determined if the person is supine, prone or laying on their side, as non-limiting examples.”, wherein phase differences are indicative of body position). As Sarabia Carrazo discloses the relationship between systolic blood pressure and body position, it would have been obvious to a person of ordinary skill in the art to modify the position monitoring system disclosed by Sayadi 2019’ as modified by Sayadi 2020’ to further base the detection of position on a phase of the cardiac pressure signal during the systolic phase of a cardiac cycle shown within the cardiac pressure signal in order to improve the accuracy of detection by correlating position to a more precise measurement of blood pressure.
Regarding claim 21, Sayadi 2019’ as modified by Sayadi 2020’ and Sarabia Carrazo discloses the system of claim 20. Sarabia Carrazo discloses the phase of the cardiac pressure signal during the measurement window corresponds to the body position of the user (Fig 3) such that the user lying on one side corresponds to a phase of the cardiac pressure signal where the amplitude is at a local maximum during the systolic phase ([0022]: “direct relationship between the position measured on the arm and blood pressure”, Fig 3: wherein at -4 the position is between 50,000-60,000) and the user lying on an opposite side corresponds to another phase of the cardiac pressure signal where the amplitude is at a local minimum during the systolic phase ([0022], Fig 3: wherein the position decreases to 10,000-20,000, indicating a change in orientation).
Claims 23 - 25 are rejected under 35 U.S.C. 103 as being unpatentable over Sayadi 2019’ in view of Sayadi 2020’ in view of Patel et al. (WO 2018/044959 A1), hereinafter Patel.
Regarding claim 23, Sayadi 2019’ as modified by Sayadi 2020’ discloses the pressure signal comprises a respiration signal indicative of respiration of the user ([0045]: “collected by the pressure transducer 146 to determine a heart rate or a respiration rate for a person lying in the bed 112.”), the determination of the one or more physiological states of the user is based on the respiration signal and the cardiac signal ([0033])). However, Sayadi fails to disclose one or more physiological states of the user comprise a level of hydration of the user.
Patel discloses a sensing system for detecting physiological signals (abstract) wherein one of the physiological states of the user comprises a level of hydration ([0070]: “assess the state of hydration”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Sayadi to detect a level of hydration as disclosed by Patel in order to expand the functionality of the device by detecting additional physiological signals.
Regarding claim 24, Sayadi 2019’ as modified by Sayadi 2020’ and Patel discloses the system of claim 23. Patel further discloses the level of hydration of the user is determined by: detecting, for each cardiac cycle in the cardiac signal, a peak of the cardiac signal across a cardiac cycle; determining across a respiratory cycle comprising a plurality of cardiac cycles, a maximum of the peaks for the plurality of cardiac cycles and a minimum of the peaks for the plurality of cardiac cycles; determining a difference between the maximum and the minimum; determining an overall amplitude of the cardiac signal across the respiratory cycle ([0148]: “In one instance, the cardiovascular change (CC) is applied to determine a hydration level by assessing the individual's TBW based upon a reduction in amplitude of the cardiac waveform as an individual dehydrates.”); and determining a ratio of the difference to the overall amplitude as an indicator of the level of hydration ([0150]: “An increase in respiratory rate and respiratory temperature is correlated with the cardiac waveform, CC and oxygen saturation to assess a trend in hydration, either dehydration or rehydration”).
Regarding claim 25, Sayadi 2019’ as modified by Sayadi 2020’ and Patel discloses the system of claim 23. Sayadi further discloses the one or more processors are further configured to output an alert if the system determines that the level of hydration of the user has exceeded a predetermined hydration level threshold ([0209]: “When the physiological events is identified… can issue an alert,”).
Response to Arguments
Applicant's arguments filed 06/04/2025 with respect to the rejection of claims 1-5, 9, 13-14, 16-26, and 28 under 35 U.S.C. § 101 have been fully considered but they are not persuasive. Applicant argues on page 8 of Applicant’s Remarks that, as amended, claims 1 and 28 are not limited to a mental process as “determining a body position of the user based on an amplitude of a first heart sound and a second heart sound of the cardiac signal, where the first heart sound indicates a beginning of a systole phase of a cardiac cycle and the second heart sound indicates an end of the systole phase of the cardiac cycle is not a mental process that can be "performed in the human mind."”. However, the determining a body position based on an amplitude of a given cardiac signal is a process that may be performed in the human mind. Per applicant’s specification page 7 line 8-13, determination of the body position is determined by noting a relative minimum or maximum of a cardiac signal. Determining a minimum or maximum of a signal is a process that may be performed in the mind. Applicant further argues on page 11 that determination of a body position is in itself a practical application. However, determination of a body position is additionally an abstract idea as it comprises a mathematical calculation (see MPEP 2106.04(a)(2) C: “vi. calculating the difference between local and average data values, In re Abele, 684 F.2d 902, 903, 214 USPQ 682, 683-84 (CCPA 1982).”) that may be performed in the mind. Further, detection of a cardiac signal based on heart sound data is routine, well-known, and conventional as evidenced by paras [0043]: “Acoustic sensors for sensing activity of the heart to determine cardiac mechanical performance, which include typically rhythmicity, fine cardiac temporal cycle” and [0007] of Gavriely et al. (US 20060047213 A1) and thus does not include an additional element.
Applicant further states that the human mind is not equipped to determine the body position of a user as the phases of the heart have a short duration. The detection of the signal is however accomplished via a sensor and analysis of data is completed after the signal has been obtained. Per MPEP 2106.04(a)(2), claims that pertain 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 v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)” are mental processes. As identification of phases of the cardiac cycle from collected sensor data and comparison of amplitudes pertain to collecting information and analyzing it, the limitation constitutes a mental process.
Applicant argues on pages 11-12 that as prior art does not teach the amended limitations, they cannot be considered well-known, routine, or known within the industry. However, the limitations are taught by Sayadi 2019’ as modified by Sayadi 2020’ (see rejection above).
Applicant’s arguments, see Applicant’s Remarks, filed 06/04/2025, with respect to the rejection(s) of claim(s) 1-5, 9, 13-14, 16-26, and 28 under 35 U.S.C. § 102/103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. § 103 (see rejection above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. S20160302737A1
Watson et al. (US 20160302737 A1) – discloses using the amplitude of the systolic/diastolic phase to determine change in position
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/KAVYA SHOBANA BALAJI/Examiner, Art Unit 3791
/DANIEL L CERIONI/Primary Examiner, Art Unit 3791