DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
2. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 102
3. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
4. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zakharov et al., U.S. Patent Application Publication No. 2022/0104704 A1 (“Zahkarov”).
As to Claim 1, Zahkarov teaches the following:
A sleep monitoring system (“wireless communication system”) 100 (see “The following description relates to sleep monitoring based on wireless signals received by a wireless communication device.” in para. [0002]; and see “FIG. 1 illustrates an example wireless communication system 100.” in para. [0025], and see figs. 1, 4, and 8), comprising:
a receiver (“client device”) 402 disposed in a target field (“space”) 401 (see “FIG. 4 is a diagram showing an example client device 402 operating to monitor motion (e.g., breathing and sleeping behavior) of a person 404 in a space 401.” in para. [0064]);
a transmitter (“access point (AP) device”) 412 disposed within the target field (see “In some implementations, the client device 402 performs one or more operations of a motion detection system by obtaining channel information based on wireless signals 410 transmitted through the space 401 from an access point (AP) device 412 over a period of time, and detecting motion of the person 404 based on the channel information.” in para. [0064]),
wherein the transmitter 412 is configured to transmit a wireless detection signal (“wireless signals”) 410 to the target field 401, a user (“person”) 404 is located between the receiver and the transmitter 412, and the receiver 402 is configured to receive the wireless detection signal through a plurality of communication links (see “In some implementations, the wireless signals 410 may be transmitted because of active sounding by the client device 402. As an example, the client device 402 may transmit, to the AP device 412, requests for the AP device 412 to transmit the wireless signals 410. The requests may include a null data packet frame, a beamforming request, a ping, or a combination thereof. In some implementations, the requests may be sent at a rate in a range from about 5 requests per second to about 15 requests per second (e.g., about 10 requests per second). The AP device 412 responds to the requests made by the client device 402 by transmitting the wireless signals 410 over a time period. The client device 402 obtains channel information based on the wireless signals 410 and detects motion of the person 404 based on the channel information.” in para. [0065]);
a storage unit (“memory”) 820 (see “As shown in FIG. 8, the example wireless communication device 800 includes an interface 830, a processor 810, a memory 820, and a power unit 840.” in para. [0087], and see fig. 8); and
a processing circuit (“processor”) 810 electrically connected to the receiver 402 and the storage unit 820 (see “As shown in FIG. 8, the example wireless communication device 800 includes an interface 830, a processor 810, a memory 820, and a power unit 840.” in para. [0087], and see fig. 8),
wherein the processing circuit 810 is configured to analyze a change of the wireless detection signal within a predetermined time, so as to detect a sleep time of the user within the predetermined time, and to calculate and obtain a sleep quality of the user during the sleep time (see “In some implementations, the client device 402 can detect periodic or quasi-periodic changes in the channel information over a series of time points. The series of time points may be included in the time period during which the wireless signals 410 are transmitted. The client device 402 may identify the breathing behavior of the person 404 based on the periodic or quasi-periodic changes. For example, the client device 402 may calculate a breathing rate or another aspect of breathing behavior.” in para. [0069]; and see “For example, the channel information can be used by the client device 402 to determine the sleeping behavior of the person 404 (e.g., the sleep quality or another aspect of sleeping behavior).” in para. [0075]; and see “The sleeping behavior (e.g., sleep quality) can be determined based on the degree of motion during sleep monitoring. As an example, periods during which the degree of motion is less than the threshold 604 may indicate periods of restful sleep. In some implementations, the client device 402 may continue determining the breathing rate of the person 404 during periods of restful sleep (e.g., during periods of rapid eye movement (REM) sleep). In some examples, the breathing rate of the person 404 may change (e.g., increase) when the person is in restful sleep (e.g., REM sleep).” in para. [0079]).
As to Claim 2, Zahkarov teaches the following:
wherein the processing circuit 810 is further configured to:
obtain, from the wireless detection signal, channel state information (CSI) data of the communication links (see “In the example of FIG. 7A, the motion detection system 702 is installed as part of the operating system core services, and the motion detection systems sends radio control signals to the wireless chip 706 via the wireless driver 704, and receives channel information (e.g., channel state information) and radio information from the wireless chip 706 via the wireless driver 704.” in para. [0083]);
execute a pre-processing process on the CSI data to calculate and obtain CSI amplitude data and CSI phase difference data of the CSI data (see para. [0055]-[0057]);
calculate and obtain activity level data of the user according to the CSI amplitude data (see “As an illustration, in the example of FIG. 5, the parameter for the frequency components ω.sub.1, ω.sub.2, ω.sub.3, and ω.sub.k of the channel information varies from time point t0 to time point t1 to time point t2. The parameter for other frequency components ω.sub.n of the channel information in the example of FIG. 5 is substantially unchanged during time points t0, t1, t2. Viewing the changes in the parameter for the frequency components ω.sub.1, ω.sub.2, ω.sub.3, and ω.sub.k over the series of time points 508 (e.g., in plot 506) shows that the variations in the parameter for the frequency components ω.sub.1, ω.sub.2, ω.sub.3, and ω.sub.k are at least quasi-periodic over the series of time points 508. Furthermore, the variations in the parameter for the frequency components ω.sub.1, ω.sub.2, ω.sub.3, and ω.sub.k are correlated.” in para. [0074]);
calculate and obtain breathing data of the user according to the CSI phase difference data (see “The average rate at which the parameter varies for the frequency components ω.sub.1, ω.sub.2, ω.sub.3, and ω.sub.k can be used to determine the breathing rate of the person 404. As an example, an average breathing rate of the person 404 may be in a range from about 7 breaths per minute to about 35 breaths per minute, and the average rate at which the parameter varies for the frequency components ω.sub.1, ω.sub.2, ω.sub.3, and ω.sub.k may be in a range from about 0.1 Hz to about 0.6 Hz.” in para. [0074]);
execute a sleep time detection process to detect the sleep time of the user within the predetermined time based on the activity level data and the breathing data (see “The sleeping behavior (e.g., sleep quality) can be determined based on the degree of motion during sleep monitoring. As an example, periods during which the degree of motion is less than the threshold 604 may indicate periods of restful sleep. In some implementations, the client device 402 may continue determining the breathing rate of the person 404 during periods of restful sleep (e.g., during periods of rapid eye movement (REM) sleep). In some examples, the breathing rate of the person 404 may change (e.g., increase) when the person is in restful sleep (e.g., REM sleep).” in para. [0079]); and
perform a sleep quality estimation process to detect the sleep quality of the user during the sleep time based on the activity level data and the breathing data (see “The sleeping behavior (e.g., sleep quality) can be determined based on the level of motion during sleep monitoring. For example, in some implementations, a metric indicative of sleep quality can be determined based on a ratio of a total duration of the periods of restful sleep to the total duration of sleep monitoring (e.g., obtained from the starting and ending times).” in para. [0081]).
As to Claim 3, Zahkarov teaches the following:
wherein the processing circuit is further configured to:
store the obtained CSI data into a CSI buffe (see para. [0083])r;
determine whether or not a data volume of the CSI data in the CSI buffer is greater than a data volume set by a data window (see para. [0099]); and
in response to the data volume of the CSI data in the CSI buffer being greater than the data volume set by the data window, execute the pre-processing process according to the CSI data in the CSI buffer (see para. [0099]).
As to Claim 4, Zahkarov teaches the following:
wherein the pre-processing includes:
calculating and obtaining a plurality of amplitudes and a plurality of phases of a plurality of subcarriers of the CSI data (see para. [0055]-[0056]);
selecting a subset associated with the user from the plurality of subcarriers (see para. [0057]);
executing a principal component analysis (PCA) algorithm on the amplitudes and phase differences corresponding to the plurality of subcarriers in the subset to obtain principal components of the plurality of amplitudes and the plurality of phases (see para. [0070]-[0074]); and
executing a noise reduction algorithm on each of the principal components to generate the CSI amplitude data and the CSI phase difference data (see para. [0058]-[0059]).
As to Claim 5, Zahkarov teaches the following:
wherein the step of calculating and obtaining the breathing data of the user according to the CSI phase difference data (see para. [0056]) includes:
executing a Fourier transform algorithm on the CSI phase difference data to obtain a plurality of breathing rate data as the breathing data (see para. [0056]).
As to Claim 6, Zahkarov teaches the following:
wherein the sleep time detection process includes:
determining whether or not data volumes of the activity level data and the breathing data exceed a predetermined data volume (see para. [0065]);
in response to the data volumes of the activity level data and the breathing data exceeding the predetermined data volume, calculating an activity level standard deviation data according to the activity level data, and calculating a breathing variance data according to the breathing data (see para. [0063] and para. [0024]); and
calculating and obtaining the sleep time of the user within the predetermined time period according to the activity level standard deviation data and the breathing variance data (see para. [0063] and para. [0024]).
As to Claim 7, Zahkarov teaches the following:
wherein the activity level standard deviation data and the breathing variance data respectively include a plurality of activity level standard deviations and a plurality of breathing variances corresponding to different multiple time points within the predetermined time, and the step of calculating the sleep time of the user within the predetermined time includes executing for each of the time points (see para. [0063] and para. [0024]):
determining whether or not the activity level standard deviation and the breathing variance corresponding to a current time point are respectively within a sleep activity level range and a sleep breathing variance range: in the affirmative, determining that the current time point is a sleep time point, in the negative, determining that the current time point is a non-sleep time point (see para. [0063] and para. [0024]).
As to Claim 8, Zahkarov teaches the following:
wherein the sleep quality estimation process includes:
classifying the time points within the sleep time into a plurality of sleep stages according to the activity level standard deviation data and the breathing variance data within the sleep time (see “Processing the second channel information to identify a category of sleep can include processing the second channel information to identify a second degree of motion in the space during the second time period; comparing the second degree of motion with threshold values associated with a respective plurality of sleep categories; and identifying the category of sleep based on the comparison. The plurality of sleep categories can include a first category of sleep that is identified if the second degree of motion is below a third threshold, a second category of sleep that is identified if the second degree of motion is above the third threshold and below a fourth threshold, and a third category of sleep that is identified if the second degree of motion is above the fourth threshold (e.g., as shown and described with respect to FIG. 6). The sleep monitoring process can include receiving, at the wireless communication device, third wireless signals transmitted through the space, in which the third wireless signals are received over a third time period; generating third channel information from the third wireless signals; processing the third channel information to identify a degree of motion in the space during the third time period; and terminating the sleep monitoring process in response to a determination that the degree of motion is above a third threshold (e.g., designating an ending time for sleep monitoring, as discussed above). The second channel information can be processed to identify a category of sleep that includes identifying multiple categories of sleep during the second time period, in which the multiple categories of sleep are associated with respective time segments within the second time period (e.g., as shown and described with respect to FIG. 6 or otherwise).” in para. [0102]); and
determining the sleep quality of the user within the sleep time according to a distribution of the sleep stages within the sleep time (see “A graphical representation can be generated to represent the multiple categories of sleep associated with the respective time segments (e.g., as shown in FIG. 6 or otherwise), and the graphical representation can be displayed on a display component of the wireless communication device.” in para. [0102]).
As to Claim 9, Zahkarov teaches the following:
wherein the sleep stages at least include a deep sleep stage and a light sleep stage, the deep sleep stage corresponds to a deep sleep activity level range and a deep sleep breathing variance range, the light sleep stage corresponds to a light sleep activity level range and a light sleep breathing variance range, wherein, in the sleep quality estimating process, each of the time points is classified into the deep sleep stage or the light sleep stage according to the activity level standard deviation data and the breathing variance data within the sleep time (see para. [0099], and para. [0081]).
As to Claim 10, Zahkarov teaches the following:
a communication device (“interface”) 830 electrically connected to the processing circuit, wherein the communication device 830 is configured to be connected to a cloud server through a network, and the obtained sleep quality is sent to the cloud server (see “For example, the interface 830 may be configured to communicate radio frequency (RF) signals formatted according to a wireless communication standard (e.g., Wi-Fi, 4G, 5G, Bluetooth, etc.). In some implementations, the example interface 830 includes a radio subsystem and a baseband subsystem.” in para. [0088]; and see “For example, each device may process received wireless signals to detect motion based on changes in the communication channel. In some cases, another device (e.g., a remote server, a cloud-based computer system, a network-attached device, etc.) is configured to perform one or more operations of the motion detection system. For example, each wireless communication device 102 may send channel information to a specified device, system or service that performs operations of the motion detection system.” in para. [0034]).
As to Claims 11-20, because the subject matter of claims 11-20 directed to a sleep monitoring method is not distinct from the subject matter of claims 1-10 directed to a sleep monitoring system, Zahkarov teaches claims 11-20 for the same reasons as that provided for the rejection of claims 1-10 above.
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVIN NATNITHITHADHA whose telephone number is (571)272-4732. The examiner can normally be reached Monday - Friday 8:00 am - 8:00 am - 4:00 pm.
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/NAVIN NATNITHITHADHA/Primary Examiner, Art Unit 3791 01/09/2026