DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 2 and 12 are objected because of the following informalities: Claims 2 and 12 include the limitation “where H(f, t) denotes the acoustic CSI, Hi(f, t) denotes a component contributed by a ith scatterer” which includes a grammatical error. The limitation should read “where H(f, t) denotes the acoustic CSI, Hi(f, t) denotes a component contributed by an ith scatterer,” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-5 and 12-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2 and 12 include an equation for H(f,t). However, the claims do not define what the variables f and t represent. For the purposes of examination, they will be viewed as frequency and time as explained in Para. [0051] of the specification. The claims also do not define the term Hj (f,t). For the purposes of examination, it will be viewed as a component contributed by a jth scatterer.
Claims 3 and 13 include an equation for
ρ
(
f
,
τ
)
. However, the term
σ
i
2
is not defined. For the purposes of examination, it will be viewed as the variance of the scattering terms as defined in Para. [0055].
Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35
U.S.C. 112 (pre-AIA ), second paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Mai (US 20200406860 A1) in view of Zamora Esquivel (US 20200226856 A1).
With respect to claims 1 and 11,
Mai teaches,
A system for detecting presence of a subject in a vehicle cabin, comprising: (Para[0192] teaches “Bot(s) and Origin(s) may also be utilized in a wireless monitoring system for monitoring an object or detecting an event related to a vehicle. The wireless monitoring system may be referred to as a car presence detection (CPD) system or vehicle wireless monitoring system.”)
one or more transmitters, (Para. [0193] teaches “For example, based on wireless signals transmitted between Bot(s) and Origin(s) of the vehicle wireless monitoring system, the system can identify a motion of an object in the car 201 (e.g. in the front seats 211, back seats 212, trunk 213 of the car 201), or a motion of an object in a proximity of the car 201. In one embodiment, the system can identify a motion, a gesture, and/or a breathing of a driver 202 at a front seat 211 in the car 201.”Para. [0200] teaches “via the transmitter 312, a wireless signal through a wireless multipath channel impacted by a motion of an object in the venue.”)
and at least one receiver configured to receive a plurality of acoustic multipath signals scattered by the subject; (Para. [0208] teaches “In particular, the channel information extractor 420 in the Origin 400 is configured for receiving the wireless signal through the wireless multipath channel impacted by the motion of the object in the venue.” Para. [0275] teaches “A method/system/software/device of a vehicle wireless monitoring system: wherein the wireless signal comprises at least one of: wireless local area network (WLAN) signal, WWAN signal, WPAN signal, WBAN signal, WiFi signal, WiFi 4/5/6/7/8 signal, IEEE 802.11 signal, IEEE 802.11n/ac/ax/be signal, cellular communication signal, 3G/4G/LTE/5G/6G/7G/8G signal, IEEE 802.15 signal, IEEE 802.16 signal, Bluetooth signal, Bluetooth Low Energy (BLE) signal, RFID signal, Zigbee signal, UWB signal, WiMax signal, unicast signal, multicast signal, broadcast signal, laser signal, LIDAR signal, radar signal, light signal, infra-red signal, ultra-violet signal, acoustic signal, ultra-sound signal, radio signal, electromagnetic (EM) wave, microwave signal, millimeter wave (mmWave) signal, radio frequency (RF) signal with a carrier frequency higher than 100 kHz, and another wireless signal.” (i.e. wireless signal can be an acoustic signal.))
a controller configured to generate one or more driving signals to control the one or more transmitters to transmit the acoustic signal; (Para. [0196] teaches “FIG. 3 illustrates an exemplary block diagram of a first wireless device, e.g. a Bot 300, of a wireless monitoring system, according to one embodiment of the present teaching. The Bot 300 is an example of a device that can be configured to implement the various methods described herein. As shown in FIG. 3, the Bot 300 includes a housing 340 containing a processor 302, a memory 304, a transceiver 310 comprising a transmitter 312 and receiver 314, a synchronization controller 306, a power module 308, an optional carrier configurator 320 and a wireless signal generator 322.”)
and a processor coupled with the controller and configured to receive the plurality of acoustic multipath signals from the receiver and process the plurality of acoustic multipath signals to detect presence of the subject in the vehicle cabin; Para. [0196] teaches “As shown in FIG. 3, the Bot 300 includes a housing 340 containing a processor 302, a memory 304, a transceiver 310 comprising a transmitter 312 and receiver 314, a synchronization controller 306, a power module 308, an optional carrier configurator 320 and a wireless signal generator 322.” Para. [0192] teaches “Bot(s) and Origin(s) may also be utilized in a wireless monitoring system for monitoring an object or detecting an event related to a vehicle. The wireless monitoring system may be referred to as a car presence detection (CPD) system or vehicle wireless monitoring system.”)
and wherein the presence of the subject is detected by: extracting a plurality of channel impulse response (CIR) data from the plurality of received acoustic multipath signals; aggregating the plurality of extracted CIR data to estimate acoustic channel state information (CSI); (Para. [0104] teaches “The channel information (CI) may be associated with/may comprise wireless channel measurements, received signal strength indicator (RSSI), channel state information (CSI), channel impulse response (CIR), channel frequency response (CFR), characteristics of frequency components (e.g. subcarriers) in a bandwidth, channel characteristics, channel filter response, timestamp, auxiliary information, data, meta data, user data, account data, access data, security data, session data, status data, supervisory data, household data, identity (ID), identifier, device data, network data, neighborhood data, environment data, real-time data, sensor data, stored data, encrypted data, compressed data, protected data, and/or another channel information.” Para. [0107] teaches “The CI may comprise data and/or at least one matrices related to channel state information (CSI). The at least one matrices may be used for channel equalization, and/or beam forming, etc. The channel may be associated with a venue. The attenuation may be due to signal propagation in the venue, signal propagating/reflection/refraction/diffraction through/at/around air (e.g. air of venue), refraction medium/reflection surface such as wall, doors, furniture, obstacles and/or barriers, etc. The attenuation may be due to reflection at surfaces and obstacles (e.g. reflection surface, obstacle) such as floor, ceiling, furniture, fixtures, objects, people, pets, etc. Each CI may be associated with a timestamp. Each CI may comprise N1 components (e.g. N1 frequency domain components in CFR, N1 time domain components in CIR, or N1 decomposition components).” Para. [0178] teaches “The CIR may have many taps (e.g. N1 components/taps)”)
obtaining an autocorrelation function (ACF) of the acoustic CSI based on a statistical acoustic sensing (SAS) model; (Para. [0109] teaches “The component-wise characteristics may be a scalar (e.g. energy) or a function with a domain and a range (e.g. an autocorrelation function, transform, inverse transform). The characteristics/STI of the motion of the object may be monitored based on the component-wise characteristics”)
and performing one or more physiological activity monitoring on basis of the ACF to detect presence of the subject in the vehicle cabin. (Para. [0193] teaches “In another embodiment, the system can identify a subtle motion and a breathing of a baby or child 203 at a back seat 212 in the car 201.”)
Mai does not explicitly teach,
one or more transmitters, each configured to transmit an acoustic signal to the subject in the vehicle cabin; (However, they do teach transmitters to transmit wireless signals where the wireless signal can be an acoustic signal. (Para(s). [0193, 0200, and 0275]) They just don’t explicitly state that an acoustic signal is directed to a subject in the vehicle cabin.)
Zamora Esquivel teaches,
one or more transmitters, each configured to transmit an acoustic signal to the subject in the vehicle cabin; (Para. [0047] teaches “These communication interfaces may facilitate driving the vehicle speakers with audio data (e.g., predetermined sounds) and receiving the acoustic data signals from vehicle speakers when configured to function as a microphone. Continuing this example, the object 302 is positioned within the cabin of the vehicle 300 as shown in FIG. 3, which may be a person or another object. The geometric relationship between the speakers 301 causes the sounds emitted from some of the speakers 301 to change the expected acoustic signature of some of the speakers 301 (which is a result of those speakers acting as a microphone to detect the emitted sounds) more than others.” (i.e. speakers are viewed as transmitters of the acoustic signal)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mai with one or more transmitters, each configured to transmit an acoustic signal to the subject in the vehicle cabin such as that of Zamora Esquivel.
One of ordinary skill would have been motivated to modify Mai, because in paragraph [0193] of Mai it states that the system can identify a subtle motion and a breathing of a baby or child 203 at a back seat 212 in the car 201 and in order to do that the signal must at least be partially directed to the baby in the vehicle cabin. Furthermore, Zamora Esquivel teaches in para(s). [0002 and 0003] that using the speakers in the vehicle as the transmitters and receivers would save costs as no additional sensors would need to be added to the vehicle.
Claims 2, 3, 5, 12, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mai (US 20200406860 A1) and Zamora Esquivel (US 20200226856 A1) as applied to claims 1 and 11 above, and further in view of Wu (“GaitWay: Monitoring and Recognizing Gait Speed Through the Walls," in IEEE Transactions on Mobile Computing;” 2021; as seen in the IDS Dated 6/10/2024) .
With respect to claims 2 and 12,
Mai does not explicitly teach,
The system of claim 1, wherein the acoustic CSI is given by:
H
f
,
t
=
∑
i
∈
R
D
H
i
f
,
t
+
∑
j
∈
R
s
H
i
f
,
t
+
N
(
f
,
t
)
where H(f, t) denotes the acoustic CSI,
H
i
(
f
,
t
) denotes a component contributed by an ith scatterer, N(f, t) is a noise term with variance
σ
N
2
, and
R
s
and
R
D
denote a set of static and dynamic scatterers, respectively.
Wu teaches,
H
f
,
t
=
∑
i
∈
R
D
H
i
f
,
t
+
∑
j
∈
R
s
H
i
f
,
t
+
N
(
f
,
t
)
where H(f, t) denotes the acoustic CSI,
H
i
(
f
,
t
) denotes a component contributed by an ith scatterer, N(f, t) is a noise term with variance
σ
N
2
, and
R
s
and
R
D
denote a set of static and dynamic scatterers, respectively. (Section 3.2 teaches “The radio signals are scattered by numerous scatterers, such as walls, ceilings, floors, furniture, human bodies, etc. Due to the superposition principle of EM waves, the CSI H(t,f) can be decomposed as equation 3.”(i.e. Epsilon is the noise term and is analogous to N. Also wave analysis for an acoustic signal would be the same.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Mai and Zamora Esquivel with wherein the acoustic CSI is given by:
H
f
,
t
=
∑
i
∈
R
D
H
i
f
,
t
+
∑
j
∈
R
s
H
i
f
,
t
+
N
(
f
,
t
)
where H(f, t) denotes the acoustic CSI,
H
i
(
f
,
t
) denotes a component contributed by an ith scatterer, N(f, t) is a noise term with variance
σ
N
2
, and
R
s
and
R
D
denote a set of static and dynamic scatterers, respectively such as that of Wu.
One of ordinary skill would have been motivated to modify the combination of Mai and Zamora Esquivel, because Mai teaches wireless monitoring based off of a multipath signal that would be scattered off of a subject and other surfaces of the vehicle and Wu teaches a known way of modeling dynamic scattering which does not rely on the assumption of only a single dominate reflection path from the human body making it more accurate for scattering rich indoor environments such as a vehicle.
With respect to claims 3 and 13,
Mai does not explicitly teach,
The system of claim 2 or the method of claim 12, wherein the autocorrelation function (ACF) is given by:
ρ
f
,
τ
=
∑
i
∈
R
D
2
π
σ
i
2
f
+
σ
N
2
f
δ
τ
∑
i
∈
R
D
2
π
σ
i
2
f
+
σ
N
2
f
J
0
k
v
τ
,
f
o
r
τ
≠
0
where ρ(f, τ) denotes the ACF of H(f, t) with time lag τ, δ(⋅) is the Dirac's delta function;
J
0
(
x
)
=
1
2
π
∫
0
2
π
exp
-
j
x
c
o
s
θ
d
θ
is the
0
t
h
-order Bessel function of the first kind, v is the moving speed of the subject, and k is the wavenumber.
Wu teaches,
wherein the autocorrelation function (ACF) is given by:
ρ
f
,
τ
=
∑
i
∈
R
D
2
π
σ
i
2
f
+
σ
N
2
f
δ
τ
∑
i
∈
R
D
2
π
σ
i
2
f
+
σ
N
2
f
J
0
k
v
τ
,
f
o
r
τ
≠
0
where ρ(f, τ) denotes the ACF of H(f, t) with time lag τ, δ(⋅) is the Dirac's delta function;
J
0
(
x
)
=
1
2
π
∫
0
2
π
exp
-
j
x
c
o
s
θ
d
θ
is the
0
t
h
-order Bessel function of the first kind, v is the moving speed of the subject, and k is the wavenumber. (Second column of page 9 shows derivation of equation 8 which is analogous to the claimed equation.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Mai and Zamora Esquivel wherein the autocorrelation function (ACF) is given by:
ρ
f
,
τ
=
∑
i
∈
R
D
2
π
σ
i
2
f
+
σ
N
2
f
δ
τ
∑
i
∈
R
D
2
π
σ
i
2
f
+
σ
N
2
f
J
0
k
v
τ
,
f
o
r
τ
≠
0
where ρ(f, τ) denotes the ACF of H(f, t) with time lag τ, δ(⋅) is the Dirac's delta function;
J
0
(
x
)
=
1
2
π
∫
0
2
π
exp
-
j
x
c
o
s
θ
d
θ
is the
0
t
h
-order Bessel function of the first kind, v is the moving speed of the subject, and k is the wavenumber such as that of Wu.
One of ordinary skill would have been motivated to modify the combination of Mai and Zamora Esquivel, because Mai teaches wireless monitoring based off of a multipath signal that would be scattered off of a subject and other surfaces of the vehicle and Wu teaches a known way of modeling dynamic scattering which does not rely on the assumption of only a single dominate reflection path from the human body making it more accurate for scattering rich indoor environments such as a vehicle.
With respect to claims 5 and 15,
Mai does not explicitly teach,
The system of claim 3 or the method of claim 13, wherein the channel gain is associated with the ACF by:
g
(
f
)
=
ρ
~
(
f
,
τ
)
=
ρ
(
f
,
τ
)
+
n
(
f
,
τ
)
,
where g(f) denotes the channel gain;
ρ
~
(
f
,
τ
)
is the sampled ACF calculated from a time series of CSI measurements with the noise term n(f, τ); and the channel gain is approximated as:
g
f
=
ρ
~
(
f
,
τ
=
1
F
s
)
, where
F
s
is the CSI sampling rate.
Wu teaches,
ρ
~
(
f
,
τ
)
=
ρ
(
f
,
τ
)
+
n
(
f
,
τ
)
; ;
ρ
~
(
f
,
τ
)
is the sampled ACF calculated from a time series of CSI measurements with the noise term n(f, τ); (Section 3.2 teaches “In practice, the sample ACF is used instead, which is an estimate of the ACF, and we use n(τ,f) to stand for the estimation noise of the ACF, i.e., equation 9” (i.e. this can be viewed as equal to the channel gain))
and the channel gain is approximated as:
g
f
=
ρ
~
(
f
,
τ
=
1
F
s
)
, where
F
s
is the CSI sampling rate. (Section 3.2 further teaches “Therefore, when the channel sampling rate Fs is sufficiently high, α(f) can be estimated as the quantity ρ^H(τ=1/Fs,f), the first tap of the ACF, and w⋆(f) is taken as equation 11.” )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Mai, Zamora Esquivel, and Wu wherein the channel gain is associated with the ACF by: g(f)=ρ ̃(f,τ)=ρ(f,τ)+n(f,τ), where g(f) denotes the channel gain; ρ ̃(f,τ) is the sampled ACF calculated from a time series of CSI measurements with the noise term n(f, τ); and the channel gain is approximated as: g(f)=ρ ̃(f,τ=1/(F_s)), where F_s is the CSI sampling rate such as that of Wu.
One of ordinary skill would have been motivated to modify the combination of Mai, Zamora Esquivel, and Wu, because section 3.2 of Wu teaches that “MRC is a classical diversity combining method in telecommunications that optimizes SNR by combining signals received on multiple antennas. MRC is applicable here by treating subcarriers as the receiving diversity, which has been utilized to facilitate breathing estimation from WiFi and MRC boosts the sensing coverage.” Therefore, it would be obvious to combine because the method is well known and boosts the sensing coverage of the system.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Mai (US 20200406860 A1), Zamora Esquivel (US 20200226856 A1), and Wu (“GaitWay: Monitoring and Recognizing Gait Speed Through the Walls," in IEEE Transactions on Mobile Computing;” 2021; as seen in the IDS Dated 6/10/2024) as applied to claims 3 and 13 above, and further in view of Zhang (“SMARS: Sleep Monitoring via Ambient Radio Signals;”2021 as seen in the IDS Dated 6/10/2024).
With respect to claims 4 and 14,
Mai further teaches,
The system of claim 3 or the method of claim 13, wherein the one or more physiological activity monitoring includes motion detection; (Para. [0193] teaches “In another embodiment, the system can identify a subtle motion and a breathing of a baby or child 203 at a back seat 212 in the car 201.”)
Mai does not explicitly teach,
and the motion detection is performed by: calculating a channel gain of the acoustic CSI from the ACF; comparing the channel gain against a threshold; determining that motion is detected if the channel gain is equal or greater than the threshold. (Section 4.13 teaches “In other words, a breathing signal is detected only if the motion statistic, peak prominence, peak width and peak amplitude are all larger than their respective preset thresholds and the motion interference ratio is smaller than its preset threshold.” Section 4.13 teaches “channel gain k(f). That is, k(f) can be estimated as equation 15 which is the same as the motion statistic.”)
Zhang teaches,
and the motion detection is performed by: calculating a channel gain of the acoustic CSI from the ACF; comparing the channel gain against a threshold; determining that motion is detected if the channel gain is equal or greater than the threshold. (Section 4.13 teaches “In other words, a breathing signal is detected only if the motion statistic, peak prominence, peak width and peak amplitude are all larger than their respective preset thresholds and the motion interference ratio is smaller than its preset threshold.” Section 4.13 teaches “channel gain k(f). That is, k(f) can be estimated as equation 15 which is the same as the motion statistic.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Mai, Zamora Esquivel, and Wu where the motion detection is performed by: calculating a channel gain of the acoustic CSI from the ACF; comparing the channel gain against a threshold; determining that motion is detected if the channel gain is equal or greater than the threshold such as that of Zhang.
One of ordinary skill would have been motivated to modify the combination of Mai, Zamora Esquivel, and Wu, because using the method of would allow the system to have a better SNR and more reliably detect motion as seen in section 4.1.4 of Zhang.
Claims 6, 7, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Mai (US 20200406860 A1) and Zamora Esquivel (US 20200226856 A1) as applied to claims 1 and 11 above, and further in view of Zhang (“SMARS: Sleep Monitoring via Ambient Radio Signals;”2021 as seen in the IDS Dated 6/10/2024).
With respect to claims 6 and 16,
Mai further teaches,
The system of claim 1 or the method of claim 11, wherein the one or more physiological activity detection includes breathing tracking; (Para. [0193] teaches “In another embodiment, the system can identify a subtle motion and a breathing of a baby or child 203 at a back seat 212 in the car 201.”)
Mai does not explicitly teach,
and the breathing tracking is performed by: searching peaks in the autocorrelation function (ACF) over time corresponding to a cycle time of breathing; and determining that breathing is detected and tracked if the peaks are found.
Zhang teaches,
and the breathing tracking is performed by: searching peaks in the autocorrelation function (ACF) over time corresponding to a cycle time of breathing; and determining that breathing is detected and tracked if the peaks are found. (Section 4.1.3 teaches “As shown in Fig. 5, when there is a breathing signal, the ACF will exhibit a definite peak at a certain delay (although the peak value may differ over different subcarriers), contributed by the periodic breathing motions. On the contrary, no prominent peaks can be observed on any subcarrier when there is no breathing (i.e., no periodic motions). In principle, as shown in Fig. 6, a time delay slightly longer than one breathing cycle (e.g., 5 to 7 seconds) is sufficient to pick up the first breathing rate and later on instantaneous estimates can be produced every one second.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Mai and Zamora Esquivel where breathing tracking is performed by: searching peaks in the autocorrelation function (ACF) over time corresponding to a cycle time of breathing; and determining that breathing is detected and tracked if the peaks are found such as that of Zhang.
One of ordinary skill would have been motivated to modify the combination of Mai and Zamora Esquivel, because Para(s). [0143-0146] of Mai discuss identifying peaks and para. [0193] details that the system can be used to determine if a subject is breathing. Also, as seen in section 4.1.3 of Zhang if the found peaks fulfil certain requirements, then the detected motion is likely to be breathing. Therefore, one would be motivated to combine the prior art in order to differentiate breathing from other detected motions.
With respect to claims 7 and 17,
Mai does not explicitly teach,
The system of claim 6 or the method of claim 16, wherein an optimized ACF is obtained by combining one or more autocorrelation function (ACF) corresponding to one or more subcarriers through a maximal ratio combining (MRC) algorithm; and the one or more physiological activity detection includes breathing tracking and the breathing tracking is performed by: searching peaks in the optimized ACF over time corresponding to a cycle time of breathing; and determining that breathing is detected and tracked if the peaks are found.
Zhang teaches,
wherein an optimized ACF is obtained by combining one or more autocorrelation function (ACF) corresponding to one or more subcarriers through a maximal ratio combining (MRC) algorithm; (Section 4.1.4 teaches “To boost the breathing SNR, SMARS devises a novel scheme to combine the breathing signals measured on multiple subcarriers in an optimal manner. Our design is based on Maximal Ratio Combining (MRC), a general diversity fusion strategy with successful applications in wireless communications, that maximizes SNR by combining multiple received signals”)
and the one or more physiological activity detection includes breathing tracking and the breathing tracking is performed by: searching peaks in the optimized ACF over time corresponding to a cycle time of breathing; and determining that breathing is detected and tracked if the peaks are found. (Section 4.1.3 teaches “As shown in Fig. 5, when there is a breathing signal, the ACF will exhibit a definite peak at a certain delay (although the peak value may differ over different subcarriers), contributed by the periodic breathing motions. On the contrary, no prominent peaks can be observed on any subcarrier when there is no breathing (i.e., no periodic motions). In principle, as shown in Fig. 6, a time delay slightly longer than one breathing cycle (e.g., 5 to 7 seconds) is sufficient to pick up the first breathing rate and later on instantaneous estimates can be produced every one second.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Mai and Zamora Esquivel wherein an optimized ACF is obtained by combining one or more autocorrelation function (ACF) corresponding to one or more subcarriers through a maximal ratio combining (MRC) algorithm; and the one or more physiological activity detection includes breathing tracking and the breathing tracking is performed by: searching peaks in the optimized ACF over time corresponding to a cycle time of breathing; and determining that breathing is detected and tracked if the peaks are found such as that of Zhang.
One of ordinary skill would have been motivated to modify the combination of Mai and Zamora Esquivel, because using the method of would allow the system to have better SNR and more reliably detect motion as seen in Section 4.1.4 of Zhang.
Claims 8, 9, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mai (US 20200406860 A1) and Zamora Esquivel (US 20200226856 A1) as applied to claims 1 and 11 above, and further in view of Tsai (“Kasami Code-Shift-Keying Modulation for Ultra-Wideband Communication Systems;” 2007).
With respect to claims 8 and 18,
Mai does not explicitly teach,
The system of claim 1 or the method of claim 11, wherein the one or more driving signals are modulated with a pseudo-noise sequence.
Tsai teaches,
wherein the one or more driving signals are modulated with a pseudo-noise sequence. (Abstract teaches “multiple pulses corresponding to a certain pseudo-noise (PN) code are transmitted to represent a symbol. In addition, the concept of M-ary code shift keying (M-CSK) was introduced into DSSS systems to achieve higher rates. In this work, we propose an M-CSK modulation technique based on the large set of Kasami sequences since it possesses good code properties, including a large code set size and low cross correlations.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Mai and Zamora Esquivel wherein the one or more driving signals are modulated with a pseudo-noise sequence such as that of Tsai.
One of ordinary skill would have been motivated to modify the combination of Mai and Zamora Esquivel, because modulating a signal with a pseudo noise sequence increases bandwidth efficiency and reduces interference of the signal as seen in the Abstract and Section 1 of Tsai.
With respect to claims 9 and 19,
Mai does not explicitly teach,
The system of claim 1 or the method of claim 11, wherein the pseudo-noise sequence is a Kasami sequence.
Tsai teaches,
wherein the pseudo-noise sequence is a Kasami sequence. (Abstract teaches “multiple pulses corresponding to a certain pseudo-noise (PN) code are transmitted to represent a symbol. In addition, the concept of M-ary code shift keying (M-CSK) was introduced into DSSS systems to achieve higher rates. In this work, we propose an M-CSK modulation technique based on the large set of Kasami sequences since it possesses good code properties, including a large code set size and low cross correlations.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Mai and Zamora Esquivel wherein the pseudo-noise sequence is a Kasami sequence such as that of Tsai.
One of ordinary skill would have been motivated to modify the combination of Mai and Zamora Esquivel, because modulating a signal with a Kasami sequence increases bandwidth efficiency and reduces interference of the signal as seen in the Abstract and Section 1 of Tsai.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mai (US 20200406860 A1) and Zamora Esquivel (US 20200226856 A1) as applied to claims 1 and 11 above, and further in view of Yang (US 9020144 B1).
With respect to claims 10 and 20,
Mai does not explicitly teach,
The system of claim 1 or the method of claim 11, further comprising a first high-pass filter applied on the transmitted acoustic signal and a second high-pass filter applied on the received acoustic signal.
Yang teaches,
The system of claim 1, further comprising a first high-pass filter applied on the transmitted acoustic signal and a second high-pass filter applied on the received acoustic signal. (Col. 3 Ln(s). [38-40] teach “In the embodiment of FIG. 1, the receive path 110 includes a high-pass filter (HPF) 112 that performs high-pass filtering with respect to the output audio signal Rx.” Col. 5 Ln(s). [6-11] teach “The transmit path 108 includes a high-pass filter (HPF) 120 that is configured to filter out any DC component of the input audio signal Tx as well to filter out as some low frequency noise.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Mai and Zamora Esquivel further comprising a first high-pass filter applied on the transmitted acoustic signal and a second high-pass filter applied on the received acoustic signal such as that of Tsai.
One of ordinary skill would have been motivated to modify the combination of Mai and Zamora Esquivel, because high pass filters would reduce unwanted low frequency noise in the signals.
Conclusion
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/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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