Office Action 2: Final Rejection (2024-06-25)
Rejection types: 103 - Obviousness

CLAIMS:
AMENDMENTS TO THE CLAIMS

1\. (Currently Amended) A system, comprising:
a plurality of sensors, each of the plurality of sensors configured to engage with an article on which a first user is positioned, and each of the plurality of sensors configured to measure force;
a communication module in wired communication with each of the plurality of sensors, wherein the communication module receives force data transmitted from each of the plurality of sensors; and
an apparatus comprising:at least one data processor; and
at least one memory storing instructions which, when executed by the at least one data processor, cause the apparatus to at least:
receive, from the communication module, the force data;transform, in response to a change in the force data indicative of a change in a load on the article and based on a determination of a plurality of physiological sources on the article, the force data from each of the plurality of sensors into a corresponding plurality of physiological signals; and
determine, based on the plurality of physiological signals, a source physiological signal for each of the plurality of physiological sources;
wherein the determination of the source physiological signal for each of the plurality of physiological sources comprises:

separating into components each of the plurality of physiological signals, wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors; and compositing the separated components for each of the plurality of physiological sources, wherein the compositing forms the source physiological signal for each of the plurality of physiological sources, and wherein transforming the force data comprises time-domain or frequency-domain filtering the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors.
2\. (Original) The system of claim 1, wherein the determination of the source physiological signal for each of the plurality of physiological sources is based on a model for each of the plurality of physiological sources, wherein each model comprises the components, the components defining a contribution of a respective physiological source to each of the plurality of physiological sources.

3\. (Previously Presented) The system of claim 1, wherein, based on the determination of the plurality of physiological sources on the article, the physiological signals are based on the force data from at least two of the plurality of sensors.
4\. (Cancelled).

5\. (Cancelled).

6\. (Currently Amended) The system of claim \[\[5\]\] 1, wherein a source respiratory signal is determined, based on the plurality of respiratory signals, for each of the plurality of physiological sources, and wherein the execution of the instructions further cause the apparatus to at least:
identify, from the source respiratory signal for each of the plurality of physiological sources, one or more respiratory parameters.
7\. (Previously Presented) The system of claim 1, wherein transforming the force data comprises time-domain and/or frequency-domain filtering the force data to obtain a plurality of ballistocardiogram signals corresponding to each of the plurality of sensors.
8\. (Original) The system of claim 7, wherein a source ballistocardiogram signal is determined, based on the plurality of ballistocardiogram signals, for each of the plurality of physiological sources, and wherein the execution of the instructions further cause the apparatus to at least:
identify, from the source ballistocardiogram signal for each of the plurality of physiological sources, one or more ballistocardiogram parameters.
9\. (Previously Presented) The system of claim 1, wherein the change in the force data indicative of the change in the load on the article is determined based on identifying the change in the force data greater than a predefined threshold of force for a predefined duration threshold.

10\. (Previously Presented) The system of claim 1, wherein the determination of the plurality of physiological sources on the article is based on identification of alterations in the physiological signals transformed from the force data.

11-16. (Cancelled)1215

17\. (Currently Amended) A method, comprising:
receiving, by a remote processing device and from a communication module in wired communication with a plurality of sensors, force data, wherein each of the plurality of sensors is configured to engage with a member of an article on which a first user is positioned, and further wherein each of the plurality of sensors is configured to measure force;
transforming, by the remote processing device and in response to a change in the force data indicative of a change in a load on the article and based on a determination of a plurality of physiological sources on the article, the force data from each of the plurality of sensors into a corresponding plurality of physiological signals; anddetermining, by the remote processing device and based on the plurality of physiological signals, a source physiological signal for each of the plurality of physiological sources;
wherein the determination of the source physiological signal for each of the plurality of physiological sources comprises:separating into components each of the plurality of physiological signals, wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors; and

compositing the separated components for each of the plurality of physiological sources, wherein the compositing forms the source physiological signal for each of the plurality of physiological sources, and
wherein transforming the force data comprises time-domain or frequency-domain filtering the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors.

18\. (Original) The method of claim 17, wherein the determination of the source physiological signal for each of the plurality of physiological sources is based on a model for each of the plurality of physiological sources, wherein each model comprises the components, the components defining a contribution of a respective physiological source to each of the plurality of physiological sources.

19\. (Previously Presented) The method of claim 17, wherein, based on the determination of the plurality of physiological sources on the article, the physiological signals are based on the force data from at least two of the plurality of sensors.

20\. (Cancelled).

21\. (Cancelled).

22\. (Currently Amended) The method of claim \[\[21\]\] 17, wherein a source respiratory signal is determined, based on the plurality of respiratory signals, for each of the plurality of physiological sources, and wherein the method further comprises:

identifying, from the source respiratory signal for each of the plurality of physiological sources, one or more respiratory parameters.

23\. (Previously Presented) The method of claim 17, wherein transforming the force data comprises time-domain and/or frequency-domain filtering the force data to obtain a plurality of ballistocardiogram signals corresponding to each of the plurality of sensors.

24\. (Original) The method of claim 23, wherein a source ballistocardiogram signal is determined, based on the plurality of ballistocardiogram signals, for each of the plurality of physiological sources, and wherein the method further comprises:
identifying, from the source ballistocardiogram signal for each of the plurality of physiological sources, one or more ballistocardiogram parameters.

25\. (Previously Presented) The method of claim 17, wherein the change in the force data indicative of the change in the load on the article is determined based on identifying the change in the force data greater than a predefined threshold of force for a predefined duration threshold.

26\. (Previously Presented) The method of claim 17, wherein the determination of the plurality of physiological sources on the article is based on identification of alterations in the physiological signals transformed from the force data.

27-30. (Cancelled)2829

31\. (Currently Amended) An apparatus, comprising:
at least one data processor; and

at least one memory storing instructions which, when executed by the at least one data processor, cause the apparatus to at least:
receive, from a communication module in wired communication with a plurality of sensors, force data, wherein each of the plurality of sensors is configured to engage with a member of an article on which a first user is positioned, and further wherein each of the plurality of sensors is configured to measure force;
transform, in response to a change in the force data indicative of a change in a load on the article and based on a determination of a plurality of physiological sources on the article, the force data from each of the plurality of sensors into a corresponding plurality of physiological signals; and
determine, based on the plurality of physiological signals, a source physiological signal for each of the plurality of physiological sources;
wherein the determination of the source physiological signal for each of the plurality of physiological sources comprises:
separating into components each of the plurality of physiological signals, wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors; and
compositing the separated components for each of the plurality of physiological sources, wherein the compositing forms the source physiological signal for each of the plurality of physiological sources, and

wherein transforming the force data comprises time-domain or frequency- domain filtering the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors.

32-44. (Cancelled)3343

45\. (Currently Amended) A non-transitory computer-readable storage medium including program code, which when executed by at least one data processor, causes operations comprising:
receiving, by a remote processing device and from a communication module in wired communication with a plurality of sensors, force data, wherein each of the plurality of sensors is configured to engage with a member of an article on which a first user is positioned, and further wherein each of the plurality of sensors is configured to measure force;
transforming, by the remote processing device and in response to a change in the force data indicative of a change in a load on the article and based on a determination of a plurality of physiological sources on the article, the force data from each of the plurality of sensors into a corresponding plurality of physiological signals; anddetermining, by the remote processing device and based on the plurality of physiological signals, a source physiological signal for each of the plurality of physiological sources;
wherein the determination of the source physiological signal for each of the plurality of physiological sources comprises:separating into components each of the plurality of physiological signals, wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors; and

compositing the separated components for each of the plurality of physiological sources, wherein the compositing forms the source physiological signal for each of the plurality of physiological sources, and
wherein transforming the force data comprises time-domain or frequency-domain filtering the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors.

46-47. (Cancelled).

OFFICE ACTION:
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 .
Response to Amendment
This office action is responsive to the amendment filed on 6/10/24. As directed by the amendment: claims 1, 6, 17, 22, 31, and 45 have been amended, and claims 5 and 21 have been cancelled. Thus, claims 1-3, 6-10, 17-19, 22-26, 31, and 45 are presently pending in this application.
Response to Arguments
Rejections under 101
Applicant’s arguments with respect to the rejections under 35 U.S.C. 101 have been fully considered and are persuasive. Accordingly, the rejections of the pending claims under 35 U.S.C. 101 have been withdrawn.
Claim Interpretation under 112(f)
Applicant’s arguments with respect to the interpretation of “communication module” and “remote processing device” under 112(f) has been fully considered. Upon further consideration, sufficient structure is recited in the claims to perform the recited functions. Accordingly, the interpretation using 112(f) has been withdrawn and the limitations will be interpreted using the broadest reasonable interpretation.
Prior Art Rejections
Applicant's arguments with respect to the rejections under 35 U.S.C. 102 and 103 have been fully considered but they are not persuasive.
Applicant argues that Mack is silent with respect to “transforming…to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors.” However, Mack is not relied upon for this feature. As stated in the non-final office action, force data from each of the plurality of sensors are processed (i.e., transformed) to determine a variety of data, such as cardiac and respiratory signals, as explained in Young ¶ [0042-0043] (see non-final office action p. 9). Specifically, Young ¶ [0042] states that the “load or other sensors herein utilize macro signals and micro signals and process those signals to determine a variety of data” (emphasis added). Young ¶ [0043] explains that the “variety of data” includes respiratory signals, and that “[respiration] is also detected by the sensors.” The plural sensors indicates that respiratory signals are detected by each sensor, i.e., Young discloses obtaining a plurality of respiratory signals corresponding to each of the plurality of sensors. Additionally, obtaining the plurality of respiratory signals from the raw force data would require processing (i.e., “transforming”). Thus, Young discloses transforming force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors.
While Young additionally discloses time-domain and/or frequency domain filtering the force data, this feature does not appear to be explicitly supported by the provisional application (see non-final office action p. 17). Mack is relied upon only for teaching the “transforming the force data [via] time-domain or frequency domain filtering [of] the force data” feature, which is taught in ¶ [0033, 0063] and Fig. 7, see non-final office action p. 17. Mack further teaches in ¶ [0063] and Fig. 7 that the frequency domain filtering is used to separate respiratory and cardiac output signals. Young ¶ [0043] indicates that respiratory and cardiac signals are analyzed separately. In light of these teachings, one of ordinary skill in the art would understand that it would be advantageous to include the frequency domain filtering taught by Mack to allow the system to better separate the respiratory and cardiac output signals.
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.
Claims 1-3, 6-8, 17-19, 22-24, 31, and 45 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication US-2020/0110194-A1 to Young et al. (hereinafter “Young”) in view of US Patent Application Publication US-2005/0124864-A1 to Mack et al. (hereinafter “Mack”).
Regarding claim 1, Young discloses a system (e.g.,  the system of Fig. 2), comprising
a plurality of sensors (Fig. 2 shows a plurality of sensors 106)
each of the plurality of sensors configured to engage with an article on which a first user is positioned (Fig. 1 shows the plurality of sensors engaged with a bed (i.e., an article) on which a user is positioned)
and each of the plurality of sensors configured to measure force ([0063] states that the sensors may be force sensors and [0030] states that the sensors detect (measure) force)
a communication module in wired (¶ [0036]: “wired”) communication with each of the plurality of sensors (e.g.,  the portion of controller 200 that collects signals from sensors 106 described in [0036])
wherein the communication module receives force data transmitted from each of the plurality of sensors ([0036] states that the communication module collects signals from sensors 106, which are configured to measure force)
and an apparatus (Fig. 2 and [0037], e.g., controller 200, cloud based computer 212, and/or off-site controller 214) comprising:
at least one data processor (Fig. 2 and [0037], e.g., the processing components of controller 200, cloud based computer 212, and/or off-site controller 214)
and at least one memory (the instructions for the processing performed by the at least one processor would necessarily be stored on a memory) storing instructions which, when executed by the at least one data processor, cause the apparatus to at least: receive, from the communication module, the force data ([0036-37] indicate that the apparatus receives force data from the communication module)
transform, in response to a change in the force data indicative of a change in a load on the article ([0053] states that load (force) data can be used to determine presence of a subject on a substrate (indicative of a change in a load on the article); [0053] further states that the system can wake up in response to detecting the presence of a subject on the article)
and based on a determination of a plurality of physiological sources on the article ([0034] states that the system can differentiate between two or more bodies (plurality of physiological sources) on the bed (article)),
[transform] the force data from each of the plurality of sensors into a corresponding plurality of physiological signals (micro signals from the load sensors (force data from each of the plurality of sensors) are processed (transformed) to determine a variety of data, such as cardiac and respiratory signals (plurality of physiological signals), see [0042]-[0043])
and determine, based on the plurality of physiological signals, a source physiological signal for each of the plurality of physiological sources ([0034] states that the system can determine cardiac and respiratory signals for multiple people (each of the plurality of physiological sources))
wherein the determination of the source physiological signal for each of the plurality of physiological sources comprises: separating into components each of the plurality of physiological signals wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors ([0047]; during superposition analysis, the contribution of each individual source (components of each of the plurality of physiological signals at each of the plurality of sensors) is determined) and
compositing the separated components for each of the plurality of physiological sources, wherein the compositing forms the source physiological signal for each of the plurality of physiological sources ([0047]; the resultant responses are added to determine the “true result” (the source physiological signal for each of the plurality of physiological sources)) and
transforming the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors (Young ¶ [0042]: the “load or other sensors herein utilize macro signals and micro signals and process [i.e., transform] those signals to determine a variety of data”; ¶ [0043] states “[respiration] is also detected by the sensors.” — the plural sensors indicates that respiratory signals are detected by each sensor, i.e., obtaining a plurality of respiratory signals corresponding to each of the plurality of sensors; further, obtaining the plurality of respiratory signals from the raw force data would require processing, i.e., transforming).
Although Young discloses time-domain and/or frequency-domain filtering, that feature does not appear to be explicitly supported by the provisional application (‘613 to which Young claims domestic benefit). However, Mack teaches a system that transforms force data (input signal 705) into respiratory signals (chest movement output signal 740) using frequency-domain filtering (2nd order band pass filter 725); see ¶ [0063] and Fig. 7. Paragraph ¶ [0033] states that the vibration sensors, which sense input signal 705, may use load cells, which measure force data).
Mack is considered to be analogous to the claimed invention because they are both within the same field of non-invasive monitoring of physiological signals using sensors attached to articles on which a user is positioned. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young and incorporated the teachings of Mack such that transforming the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors comprises time-domain and/or frequency-domain filtering. Doing so would improve the system by allowing it to better separate respiratory and cardiac output signals from the force data, as indicated by Mack ¶ [0063].
Regarding claim 2, Young in view of Mack teaches the system of claim 1, wherein the determination of the source physiological signal for each of the plurality of physiological sources is based on a model for each of the plurality of physiological sources wherein each model comprises the components, the components defining a contribution of a respective physiological source to each of the plurality of physiological sources (the superposition analysis described in Young [0047] is considered a model for each of the plurality of physiological sources under its broadest reasonable interpretation).
Regarding claim 3, Young in view of Mack teaches the system of claim 1, wherein, based on the determination of the plurality of physiological sources on the article, the physiological signals are based on the force data from at least two of the plurality of sensors (the use of multiple (i.e., at least 2) sensors is necessary to determine cardiac and respiratory signals for multiple people (see Young [0034]), therefore at least two sensors would be used in the superposition analysis described in [0047]).
Regarding claim 6, Young in view of Mack teaches the system of claim 5, wherein a source respiratory signal is determined, based on the plurality of respiratory signals, for each of the plurality of physiological sources (Young [0034] states that respiratory signals (a source respiratory signal) are determined for multiple people (i.e., each of the plurality of physiological sources)), and wherein the execution of the instructions further cause the apparatus to at least: identify, from the source respiratory signal for each of the plurality of physiological sources, one or more respiratory parameters (Young [0034] states that respiration rate and respiration condition (one or more respiratory parameters) can be determined from the source respiratory signals).
Regarding claim 7, Young in view of Mack teaches the system of claim 1, wherein transforming the force data comprises obtaining a plurality of ballistocardiogram signals corresponding to each of the plurality of sensors (Young ¶ [0043] states that the “sensors detect the heart beating” based on the force data (see ¶ [0042]); the signals related to heart beating are considered ballistocardiography signals under the broadest reasonable interpretation and the plural sensors indicates that the ballistocardiography signals are detected by each sensor, i.e., obtaining a plurality of ballistocardiography signals corresponding to each of the plurality of sensors; further, obtaining the plurality of ballistocardiography signals from the raw force data would require processing, i.e., transforming).
Although Young discloses time-domain and/or frequency-domain filtering, that feature does not appear to be explicitly supported by the provisional application.
However, Mack also teaches a system that transforms force data (input signal 705) into ballistocardiogram signals (cardiac output signal 765) using frequency-domain filtering (2nd order low pass filter 745 and/or 3rd order high pass filter 750), see [0064] and Fig. 7. Paragraph [0033] states that the vibration sensors, which sense input signal 705, may use load cells, which measure force data. Mack [0006] states that ballistocardiography is the study of the cardiac system by measuring forces related to the contraction and relaxation of the heart... The cardiac output signal 765 is considered to be a ballistocardiogram signal because it was created by measuring forces related to heart activity using the vibration sensors.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Young by incorporating the teachings of Mack such that transforming the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors comprises time-domain and/or frequency-domain filtering. Doing so would result in the same improvements as discussed in the rejection of claim 1 (see above, in regards to claim 1).
Regarding claim 8, Young in view of Mack teaches the system of claim 7, wherein a source ballistocardiogram signal is determined, based on the plurality of ballistocardiogram signals, for each of the plurality of physiological sources (Young [0034] states that cardiac signals (source ballistocardiogram signal) are determined for multiple people (i.e., each of the plurality of physiological sources)), and wherein the execution of the instructions further cause the apparatus to at least: identify, from the source ballistocardiogram signal for each of the plurality of physiological sources, one or more ballistocardiogram parameters (Young [0034] states that heart rate, heart condition, and beat to beat variation (one or more ballistocardiogram parameters) can be determined).
Regarding claim 17, Young discloses a method (e.g., abstract; “…methods for determining item-specific information…”), comprising:
receiving, by a remote processing device (Fig. 2 and [0037], e.g., the processing components cloud based computer 212, and/or off-site controller 214) and from a communication module (e.g.,  the portion of controller 200 that collects signals from sensors 106 described in [0036]) in wired (¶ [0036]: “wired”) communication with a plurality of sensors (Fig. 2 shows a plurality of sensors 106), force data ([0036-37] indicates that the apparatus receives force data from the communication module),
wherein each of the plurality of sensors is configured to engage with a member of an article on which a first user is positioned (Fig. 1 shows the plurality of sensors engaged with a bed, i.e., an article, on which a user is positioned; [0065] states that sensors 412 can be engaged with various articles, including a bed), and further
wherein each of the plurality of sensors is configured to measure force ([0063] states that the sensors may be force sensors and [0030] states that the sensors detect (measure) force);
transforming, by the remote processing device ([0037] states that cloud-based computer 212 or off-site controller 214 (the remote processing device) can perform processing on data (i.e., transforming) from controller 200) and in response to a change in the force data indicative of a change in a load on the article ([0053] states that load (force) data can be used to determine presence of a subject on a substrate (indicative of a change in a load on the article); [0053] further states that the system can wake up in response to detecting the presence of a subject on the article) and based on a determination of a plurality of physiological sources on the article ([0034] states that the system can differentiate between two or more bodies (plurality of physiological sources) on the bed (article)), [transform] the force data from each of the plurality of sensors into a corresponding plurality of physiological signals (micro signals from the load sensors (force data from each of the plurality of sensors) are processed (transformed) to determine a variety of data, such as cardiac and respiratory signals (plurality of physiological signals), see [0042]-[0043]); and
determining, by the remote processing device ([0037] states that cloud-based computer 212 or off-site controller 214 (the remote processing device) can perform processing on the data from controller 200) and based on the plurality of physiological signals, a source physiological signal for each of the plurality of physiological sources ([0034] states that the system can determine cardiac and respiratory signals for multiple people (each of the plurality of physiological sources));
wherein the determination of the source physiological signal for each of the plurality of physiological sources comprises: separating into components each of the plurality of physiological signals wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors ([0047]; during superposition analysis, the contribution of each individual source (components of each of the plurality of physiological signals at each of the plurality of sensors) is determined); and
compositing the separated components for each of the plurality of physiological sources, wherein the compositing forms the source physiological signal for each of the plurality of physiological sources ([0047]; the resultant responses are added to determine the “true result” (the source physiological signal for each of the plurality of physiological sources)) and
transforming the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors (Young ¶ [0042]: the “load or other sensors herein utilize macro signals and micro signals and process [i.e., transform] those signals to determine a variety of data”; ¶ [0043] states “[respiration] is also detected by the sensors.” — the plural sensors indicates that respiratory signals are detected by each sensor, i.e., obtaining a plurality of respiratory signals corresponding to each of the plurality of sensors; further, obtaining the plurality of respiratory signals from the raw force data would require processing (i.e., transforming).
Although Young discloses time-domain and/or frequency-domain filtering, that feature does not appear to be explicitly supported by the provisional application (‘613 to which Young claims domestic benefit). However, Mack teaches this feature (see above, in regards to claim 1). A substantially similar modification as discussed above can also be made to claim 31, which would result in the same advantages and/or predictable results as indicated above.
Regarding claim 18, see above, in regards to claims 2 and 17.
Regarding claim 19, see above, in regards to claims 3 and 17.
Regarding claim 22, see above, in regards to claims 6 and 17.
Regarding claim 23, see above, in regards to claims 7 and 17.
Regarding claim 24, see above, in regards to claims 8 and 17.
Regarding claim 31, Young discloses an apparatus (e.g., the apparatus of Fig. 2, including controller 200, cloud based computer 212, and/or off-site controller 214), comprising:
at least one data processor (Fig. 2 and [0037], e.g., the processing components of controller 200, cloud based computer 212, and/or off-site controller 214); and at least one memory (the instructions for the processing performed by the at least one processor would necessarily be stored on a memory) storing instructions which, when executed by the at least one data processor, cause the apparatus to at least: receive, from a communication module (e.g.,  the portion of controller 200 that collects signals from sensors 106 described in [0036]) in wired (¶ [0036]: “wired”) communication with a plurality of sensors (Fig. 2 shows a plurality of sensors 106), force data ([0036-37] indicate that the apparatus receives force data from the communication module), wherein each of the plurality of sensors is configured to engage with a member of an article on which a first user is positioned (Fig. 1 shows the plurality of sensors engaged with a bed, i.e., an article, on which a user is positioned), and further
wherein each of the plurality of sensors is configured to measure force ([0063] states that the sensors may be force sensors and [0030] states that the sensors detect (measure) force);
transform, in response to a change in the force data indicative of a change in a load on the article ([0053] states that load (force) data can be used to determine presence of a subject on a substrate (indicative of a change in a load on the article); [0053] further states that the system can wake up in response to detecting the presence of a subject on the article) and based on a determination of a plurality of physiological sources on the article ([0034] states that the system can differentiate between two or more bodies (plurality of physiological sources) on the bed (article)), [transform] the force data from each of the plurality of sensors into a corresponding plurality of physiological signals (micro signals from the load sensors (force data from each of the plurality of sensors) are processed (transformed) to determine a variety of data, such as cardiac and respiratory signals (plurality of physiological signals), see [0042]-[0043]); and
determine, based on the plurality of physiological signals, a source physiological signal for each of the plurality of physiological sources ([0034] states that the system can determine cardiac and respiratory signals for multiple people (each of the plurality of physiological sources));
wherein the determination of the source physiological signal for each of the plurality of physiological sources comprises: separating into components each of the plurality of physiological signals, wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors ([0047]; during superposition analysis, the contribution of each individual source (components of each of the plurality of physiological signals at each of the plurality of sensors) is determined); and
compositing the separated components for each of the plurality of physiological sources, wherein the compositing forms the source physiological signal for each of the plurality of physiological sources ([0047]; the resultant responses are added to determine the true result (the source physiological signal for each of the plurality of physiological sources)), and
transforming the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors (Young ¶ [0042]: the “load or other sensors herein utilize macro signals and micro signals and process [i.e., transform] those signals to determine a variety of data”; ¶ [0043] states “[respiration] is also detected by the sensors.” — the plural sensors indicates that respiratory signals are detected by each sensor, i.e., obtaining a plurality of respiratory signals corresponding to each of the plurality of sensors; further, obtaining the plurality of respiratory signals from the raw force data would require processing (i.e., transforming).
Although Young discloses time-domain and/or frequency-domain filtering, that feature does not appear to be explicitly supported by the provisional application (‘613 to which Young claims domestic benefit). However, Mack teaches this feature (see above, in regards to claim 1). A substantially similar modification as discussed above can also be made to claim 31, which would result in the same advantages and/or predictable results as indicated above.
Regarding claim 45, see above, in regards to claim 17. Additionally, Young teaches a non-transitory computer-readable storage medium including program code, which when executed by at least one data processor, causes operations ([0134] states that the controller can be implemented to follow a computer program (program code) that, when executed, carries out the methods and/or instructions described in the document; the computer program would be stored on a non-transitory computer-readable storage medium).
Claims 9 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Young in view of Mack, further in view of US Patent Application Publication US-2020/0146910-A1 to Demirli et al. (hereinafter “Demirli”).
Regarding claim 9, Young in view of Mack teaches the system of claim 1. Although Young discloses identifying the change in the force data greater than a predefined threshold of force, this feature does not appear to be supported by the provisional application. Further, Young does not explicitly disclose a predefined duration threshold.
However, Demirli teaches a sensor integrated into an article that is able to detect the presence of a user (change in the load on the article) based on a change in pressure data (which is force data since pressure is force applied over a given area) greater than a specified (predefined) threshold; see [0053]. Demirli further teaches that the presence of a user can be determined when the presence persists longer than a predefined (e.g.,  10 minutes) threshold period of time (duration); see [0078].
Demirli is considered to be analogous to the claimed invention because they are both within the same field of non-invasive monitoring of physiological signals using sensors attached to articles on which a user is positioned. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Young to incorporate the teachings of Demirli by using a predefined force threshold and a predefined duration threshold to determine the change in force data indicative of the change in the load on the article. Doing so would improve the system by allowing it to determine whether a person or object above a certain weight is on the article (see Demirli [0053]) and would provide the ability to use the duration threshold to determine that a user is in bed for the evening (see Demirli [0078]).
Regarding claim 25, see above, in regards to claims 9 and 17.
Claims 10 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Young in view Mack, further in view of US Patent Application Publication US-2014/0371635-A1 to Shinar et al. (hereinafter “Shinar”).
Regarding claim 10, Young in view of Mack teaches the system of claim 1 and the determination of the plurality of physiological sources on the article ([0034] states that the system can differentiate between two or more bodies (plurality of physiological sources) on the bed (article)). Young does not explicitly disclose that the determination is based on identification of alterations in the physiological signals transformed from the force data.
However, in [0110], Shinar teaches a system that determines when one person is out of bed (determination of the plurality of physiological sources on an article) by determining that the detected signals are characteristic of a second person (identification of alterations in physiological signals).
Shinar is considered to be analogous to the claimed invention because they are both within the same field of non-invasive monitoring of physiological signals using sensors attached to articles on which a user is positioned. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Young by incorporating the teachings of Shinar by using the physiological signals characteristic of a given user to identify when another user has left the article. Doing so would improve the system's ability to detect which user is on or off of the bed, as noted by Shinar in [0110].
Regarding claim 26, see above, in regards to claims 10 and 17.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.  Accordingly, THIS ACTION IS MADE FINAL.  See MPEP § 706.07(a).  Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action.  In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action.  In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.

APPLICANT REBUTTAL: REMARKS
The Examiner has rejected claims 1-3, 6-8, 17-19, 22-24, 31 and 45 under 35 U.S.C. §102(a)(2) as allegedly being anticipated by U.S. Patent Publication No. 2020/0110194 to Young et al. ("Young") in view of U.S. Patent Publication No. 2005/0124864 to Mack et al. ("Mack"); rejected claims 9 and 25 under 35 U.S.C. §103 as allegedly being unpatentably obvious over Young in view of U.S. Patent Publication No. 2020/0146910 to Demirli et al. ("Demirli"); rejected claims 10 and 26 under 35 U.S.C. §103 as allegedly being unpatentably obvious over Young in view of U.S. Patent Publication No. 2014/037163 5 to Shinar et al. ("Shinar").
Summary of Amendments. With this amendment, claims 1, 3, 9, 10, 17, 19, 25, 26, 31, and 45 have been amended. Claim 2 is canceled, without prejudice or disclaimer. The amendments to the claims are fully supported by the application as filed, and no new matter has been added.
Claim Re
j
ections Under 35 U.S.C.
§
Claims 1-3, 6-8, 17-19, 22-24, 31, and 45 stand rejected under 35 U.S.C. § 103 as being allegedly unpatentable over Young in view of Mack. The remaining claims stand rejected under 35 U.S.C. § 103 as being allegedly unpatentable over Young in view of Mack and additional references. Applicant respectfully traverses these rejections.
Claim 1 has recites a system comprising, inter alia, an apparatus configured to separate into components each of a plurality of physiological signals received from a plurality of sensors, wherein
the separating comprises:
pre-processing each of the plurality of physiological signals to generate, for
each of the plurality of physiological signals,
specifying a state space of model parameters, each of the model parameters
of the state space describing an aspect of the plurality physiological signals,
determining a posterior distribution of the physiological signals given the
respective vectors and the state space of model parameters,
determining a change in at least a first parameter of the model parameters of
the state space,
determining if the change in at least the first parameter of the model
parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefined
threshold, and
using the posterior distribution to estimate, for each of the plurality of
physiological signals, waveforms of the separated components ...
Young, taken alone or in combination with Mack, fails to teach or suggest at least the above noted features of claim 1.
The Examiner relies on Young at paragraph [0047] as allegedly teaching "separating into components each of the plurality of physiological signals..." Applicant disagrees. Young at para. [0047] and elsewhere is silent with respect to the features of claim 1 described above. Mack is similarly silent with respect to these features of claim 1. As such, Young fails to disclose or suggest any separating into components each of a plurality of physiological signals received from a plurality of sensors, "wherein the separating comprises pre-processing each of the plurality of physiological signals to generate, for each of the plurality of physiological signals, specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality physiological signals, determining a posterior distribution of the physiological signals, determining a change in at least a first parameter of the model parameters of the state space, determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being below the predefined threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components ..." as amended claim 1 recites. And Mack fails to cure this deficiency.
In view of the foregoing, claim 1 is thus patentable over Young, taken alone or in combination with Mack. Claims 17, 31, and 45 recite subject matter substantially similar to that of claim 1. At least for the reasons discussed with respect to claim 1, claims 17, 31, and 45 are patentable over Young, taken alone or in combination with Mack.
Claim 2 has been cancelled without prejudice or disclaimer, thus rendering moot its rejection. The remaining claims depend, either directly or indirectly, from one of claims 1, 17, 31, and 45, and are thus patentable over Young, taken alone or in combination with Mack or any of the other cited references, by virtue of their dependencies at least.
In view of the foregoing, the rejections 35 U.S.C. § 103 should be withdrawn.
CONCLUSION

NEXT OFFICE 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 .
Reasons for Allowance
The following is an examiner’s statement of reasons for allowance: The prior art of record fails to disclose or fairly render unpatentable the combination of claim elements. The closest prior art, the combination of US 2020/0110194 A1 to Young et al. and US 2005/0124864 A1 to Mack et al., teaches features of independent claims 1, 17, 31, and 45 (see the 6/25/24 Final Office Action) but fails to teach “specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality of physiological signals, determining a posterior distribution of the plurality of physiological signals, determining a change in at least a first parameter of the model parameters of the state space, determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefine threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components” as claimed in claim 1 and substantially similar limitations in claims 17, 31, and 45.
US 2019/0357854 A1 to Reich & Teixeira teaches the use of a state space model and determining a posterior distribution within biomedical signal processing. However, Reich & Teixeira is not related to sensors configured to measure force and fails to teach “determining a change in at least a first parameter of the model parameters of the state space, determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefine threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components.”
US 2017/0188869 A1 to Kale teaches the use of posterior probabilities to aid in biomedical signal separation. However, Kale fails to teach “specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality of physiological signals” or “determining a change in at least a first parameter of the model parameters of the state space, determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefine threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components.”
Thus, no prior art of record teaches or fairly suggests “specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality of physiological signals, determining a posterior distribution of the plurality of physiological signals, determining a change in at least a first parameter of the model parameters of the state space, determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefine threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components” as claimed in claim 1. Claims 17, 31, and 45 recite substantially similar limitations that avoid the prior art for the same reasons discussed above.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

CITED REFERENCE SUMMARIES: Ref US20200110194: Claims:
1. A method for determining item specific parameters, the method comprising:
generating multiple sensor multiple dimensions array (MSMDA) data from multiple sensors, wherein each of the multiple sensors capture sensor data for one or more items in relation to a substrate, and wherein an item is a subject or an object; for each identified item:
determining relationships between the multiple sensors based on characteristics of the MSMDA data;
determining a location of the item on the substrate based on at least the determined relationships between the multiple sensors;
determining an angular orientation of the item on the substrate based on at least the determined relationships between the multiple sensors; and
determining a body position of the item on the substrate based at least the determined relationships between the multiple sensors, the location of the subject, and the angular orientation of the item.
2. The method of claim 1, further comprising:
identifying a presence and an order of the presence of each item on the substrate.
3. The method of claim 1, further comprising, for each item:
determining weights based on characteristics of the MSMDA data.
4. The method of claim 3, further comprising, for each item:
comparing the weight against a threshold to determine a bed presence status for the item; and issuing a bed presence status if the weight is greater than the threshold.
5. The method of claim 4, wherein the threshold is multiple thresholds and each threshold of the multiple thresholds is different for subsequent items.
6. The method of claim 5, further comprising, for each item:
adjusting each threshold based on the location, angular orientation, and body position of for each identified item.
7. The method of claim 3, further comprising, for each item:
adjusting a threshold based on the location, angular orientation, and body position of each identified item; performing a motion analysis based on characteristics of the MSMDA data; and determining a bed presence status for the item based on the weight, the adjusted threshold, and the motion analysis.
8. The method of claim 1, wherein the determining relationships further comprising:
for each item:
determining, for a given combination of the multiple sensors:
an amplitude change from the MSMDA data;
a rate of change from the MSMDA data;
a phase change from the MSMDA data;
a spectral change from the MSMDA data;
a time-frequency change from the MSMDA data;

sorting the combinations based on defined metrics; and
identifying, for each sorted combination, a determined relationship by:
assigning a positive value to any other sorted combination which has at least one of a similar amplitude change, similar rate of change, similar phase of change, similar spectral change, or similar time-frequency change; and
assigning a negative value to any other sorted combination which has at least one of an opposite amplitude change, opposite rate of change, or opposite phase of change, opposite spectral change, or opposite time-frequency change,
wherein each determined relationship is a pair of combinations.
9. The method of claim 8, wherein the determining a location further comprising:
for each item:
identifying determined relationships having one of a same directional change or an opposite directional change;
selecting directionally related determined relationships which represent a defined surface coverage area; and
mapping the selected directionally related determined relationships to a surface location map to determine the location of the identified item.
10. The method of claim 9, wherein the determining an orientation further comprising:
for each item:
identifying determined relationships having strongest amplitude and opposite phase;
selecting identified determined relationships which represent corners of a defined surface coverage area; and
mapping the selected identified determined relationships to an orientation map to determine the orientation of the identified item.
11. The method of claim 8, wherein the determining the body position further comprising:
for each item:
identifying determined relationships having same directional change or an opposite directional change at the location of the identified item and the angular orientation of the identified item; and
checking the identified determined relationships against a defined body position to determine the body position of the identified item.
12. The method of claim 1, further comprising:
training a classifier based on the MSMDA data to generate at least a location classifier, an angular orientation classifier, and a body position classifier; and making classifications on non-classified MSMDA data using at least the location classifier, the angular orientation classifier, and the body position classifier.
13. The method of claim 12, further comprising:
updating classifiers associated with other multiple sensors with at least the location classifier, the angular orientation classifier, and the body position classifier, wherein the other multiple sensors and the multiple sensors are associated with different substrates.
14. A device comprising:
a substrate configured to support an item, wherein the item is a subject or an object; a plurality of sensors configured to capture sensor data from item actions with respect to the substrate; a processor in connection with the plurality of sensors, the processor configured to: generate multiple sensor multiple dimensions array (MSMDA) data from sensed sensor data; for each identified item:
determine relationships between the plurality of sensors based on characteristics of the MSMDA data;
determine a location of the identified item on the substrate based on at least the determined relationships between the plurality of sensors;
determine an angular orientation of the identified item on the substrate based on at least the determined relationships between the plurality of sensors; and
determine a body position of the identified item on the substrate based at least the determined relationships between the plurality of sensors, the location of the identified item and the angular orientation of the identified item.
15. The device of claim 14, the processor further configured to:
identify a presence of each item and the order of the presence on the substrate.
16. The device of claim 14, the processor further configured to:
for each item:
determine a weight based on characteristics of the MSMDA data;
compare the weight against threshold to determine a bed presence status for the identified item; and
issue a bed presence status if the weight is greater than the threshold.
17. The method of claim 16, wherein the threshold is multiple thresholds and each threshold of the multiple thresholds is different for subsequent items.
18. The device of claim 16, the processor further configured to:
for each item:
adjust the threshold based on the location of the identified item.
19. The device of claim 14, the processor further configured to:
for each item:
determine a weight based on characteristics of the MSMDA data;
adjust a threshold based on the location of the identified item;
perform a motion analysis based on characteristics of the MSMDA data; and
determine a bed presence status for the identified item based on the weight, the adjusted threshold, and the motion analysis.
20. The device of claim 14, the processor further configured to:
for each item:
determine, for a given combination of the plurality of sensors:
an amplitude change from the MSMDA data;
a rate of change from the MSMDA data;
a phase change from the MSMDA data;
a spectral change from the MSMDA data;
a time-frequency change from the MSMDA data;

sort the combinations based on a defined metric; and
identify, for each sorted combination, a determined relationship by:
assignment of a positive value to any other sorted combination which has at least one of a similar amplitude change, similar rate of change, or similar phase of change; and
assignment of a negative value to any other sorted combination which has at least one of an opposite amplitude change, opposite rate of change, or opposite phase of change,

wherein each determined relationship is a pair of combinations.
21. The device of claim 14, the processor further configured to, for each item:
identify determined relationships having one of a same directional change or an opposite directional change; select directionally related determined relationships which represent a defined surface coverage area; and map the selected directionally related determined relationships to a surface location map to determine the location of the identified item.
22. The device of claim 14, the processor further configured to, for each item:
identify determined relationships having strongest amplitude and opposite phase; select identified determined relationships which represent corners of a defined surface coverage area; and map the selected identified determined relationships to an orientation map to determine the angular orientation of the identified item.
23. The device of claim 14, the processor further configured to, for each item:
identify determined relationships having same directional change or an opposite directional change at the location of the identified item and the angular orientation of the identified item; and check the identified determined relationships against a defined body position to determine the body position of the identified item.
24. The device of claim 14, further comprising:
a classifier configured to make classifications on non-classified MSMDA data using at least a location classifier, an angular orientation classifier, and a body position classifier, wherein each of the location classifier, the angular orientation classifier, and the body position classifier is trained and generated based on the MSMDA data.
Ref US20050124864: Claims:
1. A non-invasive system for assessing physiological characteristics of a subject, said system comprising: 
a network of sensors configured to provide electronic output signals indicative of at least one sensed condition, said network including at least one passive sensor selected from the group consistently of vibration, temperature, carbon dioxide, humidity, electromagnetic and light sensors;  data acquisition circuitry for collecting the electronic signals output from said network of sensors;  a processor configured to process the electronic signals, said processor including an amplifier configured to amplify at least one of the electronic signals, and first and second filters configured to receive the amplified electronic signals to produce two processed electronic signals, wherein said first filter includes a band pass filter and said second filter includes a low pass filter and a high pass filter; and  an output device for outputting the processed signals into human-readable data indicative of at least one condition of a subject.
2. The system of claim 1 wherein said system is adapted to assess the quality of sleep of a subject and wherein: 
said data acquisition circuitry is configured to collect the electronic signals output from said network of sensors indicative of heart rate and/or respiratory rate;  said first filter receives signals indicative of chest movement and said second filter receives signals indicative of heart movement; and  said processor includes an algorithm to generate a heart rate and a respiratory rate based on processed electronic signals output at or below about 50 Hz.
3. The system of claim 2, wherein: 
said band pass filter comprises a second order band pass filter, an active filter and a filter with both cutoff frequencies below about 1 Hz;  said low pass filter comprises a second order low pass filter, an active filter and a filter with a cutoff frequency below about 1 Hz; and  said high pass filter is a third order high pass filter, an active filter and has a cutoff frequency below about 50 Hz.
4. The system of claim 1, further comprising a microcontroller for converting the collected electronic signals into a bit stream to be logged into a data file for immediate analysis or for later review.
5. The system of claim 1, wherein said network of sensors includes at least one detector for sensing motion, the bit stream includes electronic signals from said at least detector and said processor processes the bit stream to provide a positional map of the subject.
6. The system of claim 5, wherein said at least one detector comprises a matrix of pressure-sensitive contact switches that are embedded in a mattress pad, block of foam, bed sheet, or a chair, said matrix providing electronic signals corresponding to a positional map of the subject.
7. The system of claim 1, wherein said amplifier comprises an instrumentation amplifier that provides buffering from said network of sensors and an infinite common mode rejection ratio.
8. The system of claim 1, wherein said first filter outputs a first signal corresponding to a respiratory rate on a breath-by-breath basis of the subject and said second filter outputs a second signal corresponding to a heart rate on a beat-by-beat basis of the subject.
9. The system of claim 1, wherein at least some of the sensors are passive sensors requiring no active involvement from the subject and no direct attachment to the subject.
10. The system of claim 9, wherein all of the sensors are passive sensors requiring no active involvement from the subject and no direct attachment to the subject.
11. The system of claim 1, wherein human-readable data comprises at least one of a heart rate waveform and a respiratory waveform.
12. The system of claim 1, wherein the human-readable data comprises an obstructive sleep apnea count.
13. The system of claim 1, wherein human-readable data comprises a subject movement percentage.
14. The system of claim 1, wherein said network of sensors further comprises a movable support including at least one of said sensors.
15. The system of claim 1, wherein human-readable data comprises electromagnetic information.
16. The system of claim 1, wherein at least part of the system is incorporated in one of a mattress pad or pillow, a bed or chair at a sleep lab, a bed or chair at an elder care facility, a bed or chair at a hospital for children or adults, a bed or baby support at a prenatal care facility, and a publicly available health monitoring station.
17. A non-invasive system for assessing physiological characteristics of a subject, said system comprising: 
means for passively detecting physiological characteristics and providing electronic output signals indicative of at least one sensed condition, the physiological characteristics including at least one characteristic selected from the group consisting of vibration, temperature, relative humidity, carbon dioxide, electromagnetic and light;  means for collecting the electronic signals output from said passive detection means;  means for processing the electronic signals, including: 
means for amplifying at least one of the electronic signals; 
first means for filtering the amplified electronic signal to generate a first processed signal; and 
second means for filtering the amplified signal to generate a first processed signal; and 
 means for outputting the processed signals into human-readable data indicative of at least one condition of a subject.
18. The system of claim 17, wherein the first processed signal corresponds to a respiratory rate on a breath-by-breath basis of the subject and the second processed signal corresponds to the heart rate on a beat-by-beat basis of the subject.
19. A method of analyzing physiological characteristics of a subject including cardiac and/or respiratory parameters on a beat-by-beat or breath-by-breath basis, said method comprising the steps of: 
passively detecting physiological characteristics through a network of sensors requiring no conscious input by the subject and being capable of providing electronic output signals indicative of a sensed condition, the network including at least one sensor selected from the group consisting of vibration, position, temperature, relative humidity, carbon dioxide, electromagnetic and light sensors;  collecting the electronic signals output from the network of sensors;  processing the electronic signals such that at least one of the electronic signals is amplified and fed to first and second filters to produce two processed electronic signals, wherein the first filter includes a band pass filter and the second filter includes a low pass filter and a high pass filter; and  outputting the processed signals into human-readable format.
20. The method of claim 19, further comprising processing the processed electronic signals to generate a heart rate and a respiratory rate.
21. The method of claim 19, further comprising converting the collected electronic signals into a bit stream to be logged into a data file for immediate analysis or for later review.
22. The method of claim 21, further comprising processing the bit stream to provide a positional map of the subject relative to the network of sensors.
23. The method of claim 19, wherein the step of amplifying the at least one electronic signal comprises amplifying using an instrumentation amplifier that provides buffering from the network of sensors and an infinite common mode rejection ratio.
24. The method of claim 19, wherein said processing step further comprises the first filter outputting a first signal corresponding to a respiratory rate on a breath-by-breath basis of the subject and the second filter outputting a second signal corresponding to the heart rate on a beat-by-beat basis of the subject.
25. The method of claim 19, wherein the processed signals correspond to at least one of a heart rate waveform and a respiratory waveform.
26. The method of claim 19, wherein the processed signals correspond to an obstructive sleep apnea count.
27. The method of claim 19, wherein the processed signals correspond to a subject movement percentage.
Ref US20200146910: Claims:
1. A bed system comprising:
a mattress comprising an air bladder; at least one pressure sensor in fluid communication with the air bladder, the pressure sensor configured to sense a pressure within the air bladder; a support platform supporting the mattress, the support platform comprising a plurality of legs; a plurality of force sensors, each of the plurality of force sensors coupled to one of the plurality of legs, the force sensors configured to sense a force applied to each leg; and a controller configured to:
receive a pressure stream from the pressure sensor, the pressure stream representing the pressure sensed by the pressure sensor;
receive at least one force stream from at least one of the plurality of force sensors, the at least one force stream representing a force sensed by the force sensor;
generate, from at least the pressure stream and from the at least one force stream a sleeping parameter of a user;
determine that a home automation rule includes a condition that include at least the pressure stream and from the at least one force stream include the sleeping parameter of a user; and
responsive to determining that a home automation rule includes a condition that include at least the pressure stream and from the at least one force stream include the sleeping parameter of a user, sending an instruction to drive a controllable device.
2. The bed system of claim 1, the system further comprising the controllable device, the controllable device configured to:
receive the instruction to drive the controllable device; and responsive to receiving the instruction to drive the controllable device to alter an environment of the user.
3. The bed system of claim 1, wherein the air bladder is a first air bladder, the at least one pressure sensor is a first pressure sensor, the bed system further comprising:
the mattress comprising a second air bladder; and at least one sec
[Truncated to input limit]