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
Amendment filed on 06/23/2026 is acknowledged. As a result, claims 8-9 are cancelled. Claims 1-7 and 10-20 are pending for examination.
Response to Arguments
Applicant’s arguments with respect to claims 1, 18 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Regarding claims 1, 18 and 20, Wigren sufficiently disclosed in [0014], [0015] and [0053] the limitation that was added in the amendment (see detailed 103 rejection below).
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, 5-7, 10-20 are rejected under 35 U.S.C 102(a)(1) as being anticipated
by Ganu et al (US 20220390614 A, hereinafter Ganu) in view of WIGREN (SE510436C2, hereinafter Wigren)
Regarding Claim 1, Ganu discloses A network device, comprising: a processor; at least one network interface controller configured to provide access to a network; a memory communicatively coupled to the processor, wherein the memory comprises a geolocation logic
(Ganu [0019] FIG. 1 is a block diagram of an example computing
device 100 for automatic location of APs. Computing device 100
includes at least one processing resource 110 and at least one
machine-readable storage medium 120 comprising (e.g., encoded
with) at least GNSS signal measurement receiving instructions
122. Fig 2 Network interface controller (212, 214 ...) )
configured to: receive geo-positioning data associated with a plurality of access points (APs)
(Ganu [0017, line 3] receive, by a computing device from each AP
in a subset of a plurality of APs, a GNSS signal measurement Fig
3 Receiving a GNSS signal measurement from each AP in a subset
of a plurality of Aps)
the geo-positioning data comprising two or more of:
1) one or more global navigation satellite system (GNSS) measurements
(Ganu [0018, line 3] a computing device may receive GNSS signal
measurements from a subset of APs (GNSS-capable APs), and based
on each received GNSS signal measurement Fig 2 and 3 shows one
or more GNSS measurements)
2) one or more wireless local area network (WLAN) signal measurements
(Ganu [0020, line 5] computing device 100 may comprise a gateway
router, a wireless local area network (WLAN) controller)
3) one or more air pressure measurements
(Ganu [0036] the subsequent local measurement may comprise a
barometric pressure measurement of the AP. The pressure
measurement for each AP may be performed by a calibrated
pressure sensor (i.e., where the elevation of the AP is
predetermined), or an uncalibrated pressure sensor.)
(Note: "air pressure measurements" in the claim is referred to here as "barometric pressure measurement")
or 4) preexisting knowledge;
(Note: Examiner is required to find two or more out of four limitations in the reference. Examiner has elaborated three out of four limitations, and that is considered to be sufficient.)
determine a set of geo-positions of a set of APs in the plurality of APs based on the geo- positioning data, each AP in the set of APs corresponding to one geo- position in the set of geo-positions.
(Ganu [0017, line 3] receive, by a computing device from each AP in a subset of a plurality of APs, a GNSS signal measurement, and based on each received GNSS signal measurement, constrain, by the computing device, a map of relative AP locations (i.e., AP map) by at least one translational degree of freedom or one
rotational degree of freedom.
[0031] In the example of FIG. 3, at block 315, AP location
resolving instructions 126, when executed by processing resource
110, resolve locations of the plurality of APs in the map of
relative AP locations.)
(Note: "resolving instructions" in the reference is "determining a set of")
But Ganu does not teach
calculate a probability for each geo-position hypothesis in a plurality of geo- position hypotheses, wherein calculating the probabilities of geo-position hypotheses comprises applying a Bayes' theorem to one or more collected sensor measurements.
However, in a similar endeavor, Wigren teaches
calculate a probability for each geo-position hypothesis in a plurality of geo- position hypotheses, wherein calculating the probabilities of geo-position hypotheses comprises applying a Bayes' theorem to one or more collected sensor measurements.
(Wigren [0053] All the parameters can therefore be calculated with knowledge of the probability of correct detection for the sensor.
[0014] A further object of the present invention is to provide a method for multi-target tracking with multi-sensors that includes target type information.
[0015] The object of the present invention is achieved by a process having the features presented in the claims.
The process according to the invention uses time-recursive Bayesian methods to calculate probabilities for different target types, using different types of discrete information, such as ESM data, IRST information and direct observations.)
Therefore, it would have been obvious to one of the ordinary skill in the art
before the effective filing date of the examined application to have modified Ganu by incorporating Wigren providing Bayes’ theorem to be is used to combine the available data into probabilities for each location hypothesis.
Motivation of doing so would have enabled the device to selects the most probable AP location.
Regarding Claim 5 and as applied to claim 1, Ganu discloses,
wherein the one or more WLAN signal measurements include one or more received signal strength indicator (RSSI) measurements.
(Ganu [0020, line 5] For instance, computing device 100 may
comprise a gateway router, a wireless local area network (WLAN)
controller.) (Ganu [0042] In the example of FIG. 2, network 205
may comprise one or more wireless local area networks
(WLANs))
[0034, line 11] computing device 100 may utilize an RSSI
measurement)
Regarding Claim 6 and as applied to claim 1, Ganu discloses,
wherein at least one WLAN signal measurement in the oneor more WLAN signal measurements correlates with a distance between a pair of APs in the plurality of APs.
(Ganu [0020] In the example of FIG. 1, computing device 100 may
include a device to communicate with a plurality of APs in a
network to perform automatic location of such APs.)
Regarding Claim 7 and as applied to claim 1, Ganu discloses,
wherein at least one air pressure measurement in the one or more air pressure measurements correlates with an elevation of an AP in the plurality of APs.
(Ganu [0036] In the example of FIG. 3, at block 310, AP map
constraining instructions 124 may comprise instructions to
receive a subsequent local measurement (e.g., second local
measurement) for each of at least one AP in the subset of APs.
For instance, the subsequent local measurement may comprise a
barometric pressure measurement of the AP. The pressure
measurement for each AP may be performed by a calibrated
pressure sensor (i.e., where the elevation of the AP is
predetermined)
Regarding Claim 10 and as applied to claim 1, Ganu discloses,
wherein at least one AP in the plurality of APs includes a GNSS receiver.
(Ganu [0016] In a network that includes access points equipped
with Global Navigation Satellite (GNSS) receivers, such GNSS-
capable access points may determine their own location from GNSS
data and may be established as anchor points.)
[0043] In the example of FIG. 2, each first network device
212 includes a GNSS receiver 220 (i.e., each first network
device 212 is a GNSS-capable network device))
Regarding Claim 11 and as applied to claim 1, Ganu discloses,
wherein at least one AP in the plurality of APs includes an air pressure sensor.
(Ganu [0036, line 6] The pressure measurement for each AP may be
performed by a calibrated pressure sensor.)
Regarding Claim 12 and as applied to claim 1, Ganu discloses,
wherein the plurality of APs are located indoors.
(Ganu [0027, line 25] may reveal information about where the AP
is located within a structure (e.g., building) whose boundaries
and materials are known.
[0037] In the example of FIG. 3, at block 310, AP map constraining instructions 124 may comprise instructions to determine building floor level information for the plurality of APs.)
Regarding Claim 13 and as applied to claim 12, Ganu discloses,
wherein the plurality of APs is located on more than one floor in a building.
(Ganu [0037, line 3] The building floor level information for
the plurality of APs may include a floor level for one or more
of the plurality of APs, which may be used to infer that one or
more of the plurality of APs are co-planar with each other.)
Regarding Claim 14 and as applied to claim 1, Ganu discloses,
wherein to determine the set of geo-positions of the set of APs, the geolocation logic is further configured to:
a) generate, for each AP in theset of APs, a plurality of geo-position hypotheses;
(Ganu [0029, line 7] The signal measurement criteria may
correspond to a threshold minimum value, a threshold maximum
value, an acceptable range of values, etc., for one or more
metrics or parameters of one or more received GNSS signals.)
(Note: hypothesis here means different threshold values of
estimate.)
b) calculate, for each AP in the set of APs, a probability for each geo-position hypothesis in the plurality of geo-position hypotheses;
(Ganu [0032, line 17] the location accuracy threshold may be
based on a probability of the accuracy of one or more location
estimates of the plurality of APs.)
and c) select, for each AP in the set of APs, a geo-position hypothesis associated with a highest probability as a determined geo-position of the AP.
(Ganu [0029] In addition, at block 310, AP map constraining
instructions 124 may comprise instructions to determine, for
each AP in the subset of APs, whether the GNSS signal
measurement received from the AP satisfies a signal measurement
criteria. As used herein, a "signal measurement criteria" refers
to one or more threshold metrics or parameters of one or more
received GNSS signals. The signal measurement criteria may
correspond to a threshold minimum value, a threshold maximum
value, an acceptable range of values, etc., for one or more
metrics or parameters of one or more received GNSS signals.)
(Note: "a threshold maximum value" is the "highest probablity" of the claim limitation.)
Regarding Claim 15 and as applied to claim 1, Ganu discloses,
wherein the geolocation logic is further configured to transmit an indication of a probability of the at least one geo-position.
(Ganu [0032, line 7] the location accuracy threshold may be
based on a probability of the accuracy of one or more location
estimates of the plurality of APs.)
Regarding Claim 16 and as applied to claim 1, Ganu discloses,
wherein the
geolocation logic is further configured to: receive an updated geo-positioning data
point associated with the plurality of Aps
(Ganu [0027] In the example of FIG. 3, at block 305, GNSS signal
measurement receiving instructions 122, when executed by
processing resource 110, receive, from each AP in a subset of a
plurality of APs, a GNSS signal measurement.
[0027, line 5] The GNSS signal measurement received by computing device 100 from an AP may indicate one or more metrics or parameters of GNSS signal (s) received by the AP.)
determine an updated set of geo- positions of the set of APs based on the updated geo- positioning data point.
(Ganu [0027, line 18] a GNSS signal measurement received by computing device 100 from an AP may indicate a dilution of precision (DOP) or satellite geometry of signals used in estimating a position for the AP. Based on the DOP or satellite geometry of signals used in estimating the position for the AP, computing device 100 may determine a level of error in the estimated position of the AP.)
Regarding Claim 17 and as applied to claim 1, Ganu discloses,
wherein the geolocation logic is further con figured to transmit an indication of at least one geo- position in the set of geo-positions.
(Ganu [0027, line 5] The GNSS signal measurement received by
computing device 100 from an AP may indicate one or more metrics or parameters of GNSS signal (s) received by the AP. For instance, the GNSS signal measurement may indicate a GNSS type.
[0027, line 28] It will be understood that the received GNSS
signal measurement may include other suitable type (s) of metrics or parameters of GNSS signal (s) received by the AP.)
Regarding Claim 18, Ganu discloses A network device, comprising: a processor;
at least one network interface controller configured to provide access to a network;
and a memory communicatively coupled to the processor, wherein the memory
comprises a geolocation logic that is
(Ganu [0019] FIG. 1 is a block diagram of an example computing
device 100 for automatic location of APs. Computing device 100
includes at least one processing resource 110 and at least one
machine-readable storage medium 120 comprising (e.g., encoded
with) at least GNSS signal measurement receiving instructions
122. Fig 2 Network interface controller (212, 214 ...))
configured to: receive geo-positioning data associated with a plurality of access
points (APs)
(Ganu [0017, line 3] receive, by a computing device from each AP
in a subset of a plurality of APs, a GNSS signal measurement Fig
3 Receiving a GNSS signal measurement from each AP in a subset
of a plurality of Aps)
the geo-positioning data comprising two or more of:
1) one or more global
navigation satellite system measurements
(Ganu [0018, line 3] a computing device may receive GNSS signal
measurements from a subset of APs (GNSS-capable APs), and based
on each received GNSS signal measurement Fig 2 and 3 shows one
or more GNSS measurements)
2) one or more wireless local area network signal measurements
(Ganu [0020, line 5] computing device 100 may comprise a gateway
router, a wireless local area network (WLAN) controller)
3) one or more air pressure measurements
(Ganu [0036] the subsequent local measurement may comprise a
barometric pressure measurement of the AP. The pressure
measurement for each AP may be performed by a calibrated
pressure sensor (i.e., where the elevation of the AP is
predetermined), or an uncalibrated pressure sensor.)
(Note: "air pressure measurements" in the claim is referred to here as "barometric pressure measurement")
or 4) preexisting knowledge;
(Note: Examiner is required to find two or more out of four limitations in the reference. Examiner has elaborated three out of four limitations, and that is considered to be sufficient.)
determine a set of geo-positions of a set of APs in the plurality of APs based on the
geo-positioning data, each AP in the set of APs corresponding to one geo-position
in the set of geo-positions;
(Ganu [0017, lime 3] receive, by a computing device from each AP
in a subset of a plurality of APs, a GNSS signal measurement,
and based on each received GNSS signal measurement, constrain,
by the computing device, a map of relative AP locations (i.e.,
AP map) by at least one translational degree of freedom or one
rotational degree of freedom.)
[0031] In the example of FIG. 3, at block 315, AP location
resolving instructions 126, when executed by processing resource
110, resolve locations of the plurality of APs in the map of
relative AP locations.)
(Note: "resolving instructions" in the reference is "determining a
set of")
receive an updated geo-positioning data point associated with the plurality of APs;
(Ganu [0027] In the example of FIG. 3, at block 305, GNSS signal
measurement receiving instructions 122, when executed by
processing resource 110, receive, from each AP in a subset of a
plurality of APs, a GNSS signal measurement.
[0027, line 2] The GNSS signal measurement received by computing device 100 from an AP may indicate one or more metrics or parameters of GNSS signal (s) received by the AP.)
[32, line 8] Based on the local measurement, AP location
resolving instructions 126 may comprise instructions to
determine location estimates (e.g., first location estimates)
for the plurality of APs.)
determine an updated set of geo-positions of the set of APs based on the updated geo-positioning data point.
(Ganu [0027, line 18] a GNSS signal measurement received by
computing device 100 from an AP may indicate a dilution of
precision (DOP) or satellite geometry of signals used in
estimating a position for the AP. Based on the DOP or satellite
geometry of signals used in estimating the position for the AP,
computing device 100 may determine a level of error in the
estimated position of the AP.)
But Ganu does not teach
calculate a probability for each geo-position hypothesis in a plurality of geo- position hypotheses, wherein calculating the probabilities of geo-position hypotheses comprises applying a Bayes' theorem to one or more collected sensor measurements.
However, in a similar endeavor, Wigren teaches
calculate a probability for each geo-position hypothesis in a plurality of geo- position hypotheses, wherein calculating the probabilities of geo-position hypotheses comprises applying a Bayes' theorem to one or more collected sensor measurements.
(Wigren [0053] All the parameters can therefore be calculated with knowledge of the probability of correct detection for the sensor.
[0014] A further object of the present invention is to provide a method for multi-target tracking with multi-sensors that includes target type information.
[0015] The object of the present invention is achieved by a process having the features presented in the claims.
The process according to the invention uses time-recursive Bayesian methods to calculate probabilities for different target types, using different types of discrete information, such as ESM data, IRST information and direct observations.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Ganu by incorporating Wigren providing Bayes’ theorem to be is used to combine the available data into probabilities for each location hypothesis.
Motivation of doing so would have enabled the device to selects the most probable AP location.
Regarding Claim 19, Ganu discloses network device of claim 18, wherein the geolocation logic is further configured to transmit an indication of at least one geo- position in the set of geo-positions.
(Ganu [0027, line 5] The GNSS signal measurement received by
computing device 100 from an AP may indicate one or more metrics
or parameters of GNSS signal (s) received by the AP. For
instance, the GNSS signal measurement may indicate a GNSS type.
[0027, line 28] It will be understood that the received GNSS signal measurement may include other suitable type (s) of metrics or parameters of GNSS signal (s) received by the AP.)
Regarding Claim 20, Ganu discloses A method for geolocating access points (APs), comprising: receiving geo-positioning data associated with a plurality of APs, the geo-positioning data comprising two or more of:
1) one or more global navigation satellite system measurements,
(Ganu [0017, line 3] receive, by a computing device from each AP
in a subset of a plurality of APs, a GNSS signal measurement Fig
3 Receiving a GNSS signal measurement from each AP in a subset
of a plurality of Aps)
2) one or more wireless local area network signal measurements,
(Ganu [0020] In the example of FIG. 1, computing device 100 may
include a device to communicate with a plurality of APs in a
network to perform automatic location of such APs. For instance,
computing device 100 may comprise a gateway router, a wireless
local area network (WLAN) controller)
3) one or more air pressure measurements, or
(Ganu [0036] the subsequent local measurement may comprise a
barometric pressure measurement of the AP. The pressure
measurement for each AP may be performed by a calibrated
pressure sensor (i.e., where the elevation of the AP is
predetermined), or an uncalibrated pressure sensor.)
(Note: "air pressure measurements" in the claim is referred to here as "barometric pressure measurement")
4) preexisting knowledge;
(Note: Examiner is required to find two or more out of four limitations in the reference. Examiner has elaborated three out of four limitations, and that is considered to be sufficient.)
determining a set of geo-positions of a set of APs in the plurality of APs based on the geo- positioning data, each AP in the set of APs corresponding to one geo- position in the set of geo-positions.
(Ganu [0017, line 3] receive, by a computing device from each AP
in a subset of a plurality of APs, a GNSS signal measurement,
and based on each received GNSS signal measurement, constrain,
by the computing device, a map of relative AP locations (i.e.,
AP map) by at least one translational degree of freedom or one
rotational degree of freedom.)
[0031] In the example of FIG. 3, at block 315, AP location
resolving instructions 126, when executed by processing resource
110, resolve locations of the plurality of APs in the map of
relative AP locations.)
(Note: "resolving instructions" in the reference is "determining a set of")
But Ganu does not teach
calculate a probability for each geo-position hypothesis in a plurality of geo- position hypotheses, wherein calculating the probabilities of geo-position hypotheses comprises applying a Bayes' theorem to one or more collected sensor measurements.
However, in a similar endeavor, Wigren teaches
calculate a probability for each geo-position hypothesis in a plurality of geo- position hypotheses, wherein calculating the probabilities of geo-position hypotheses comprises applying a Bayes' theorem to one or more collected sensor measurements.
(Wigren [0053] All the parameters can therefore be calculated with knowledge of the probability of correct detection for the sensor.
[0014] A further object of the present invention is to provide a method for multi-target tracking with multi-sensors that includes target type information.
[0015] The object of the present invention is achieved by a process having the features presented in the claims.
The process according to the invention uses time-recursive Bayesian methods to calculate probabilities for different target types, using different types of discrete information, such as ESM data, IRST information and direct observations.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Ganu by incorporating Wigren providing Bayes’ theorem to be is used to combine the available data into probabilities for each location hypothesis.
Motivation of doing so would have enabled the device to selects the most probable AP location.
Claim 2 is rejected under 35 U.S.C 102(a)(1) as being anticipated by Ganu et al (US20220390614 A, hereinafter Ganu) in view of WIGREN (SE510436C2, hereinafter Wigren) and in further view of Edge (US 20120306693 A1, hereinafter Edge).
Regarding Claim 2. Ganu and Wigren disclose the invention in Claim 1
But do not teach
wherein the one or more GNSS measurements include one or more pseudo range measurements.
However, Edge teaches
(Edge [0008] The method of obtaining location measurements
includes one or more of obtaining GNSS pseudo range measurements
for one or more satellite vehicles by one or more static devices
in the plurality.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Ganu and Wigren and by incorporating Edge providing resources to have pseudo range measurements.
Motivation of doing so would have enabled the device to have more accurate location measurement for APs.
Claim 3 is rejected under 35 U.S.C 102(a)(1) as being anticipated by Ganu et al (US 20220390614 A, hereinafter Ganu) in view of WIGREN (SE510436C2, hereinafter Wigren) and in further view of Wilson et al (US 20180199304 A1, hereinafter Wilson)
Regarding Claim 3, Ganu and Wigren disclose the invention in Claim 1
But do not teach
wherein the one or more WLAN signal measurements include one or more
time of arrival (ToA) measurements.
However, Wilson et al teaches
(Wilson [0006, line 4] the information indicating the
distance between the mobile device and the base station includes
at least one of a time of flight (TOF), a time of arrival (TOA),
or a roundtrip time (RTT) of the signal.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Ganu and Wigren and by incorporating Wilson to have ToA measurement method in WLAN signal measurements.
Motivation of doing so would have enabled the device to have more accurate location measurement for APs.
Claim 4 is rejected under 35 U.S.C 102(a)(1) as being anticipated by Ganu et al (US 20220390614 A, hereinafter Ganu) in view of WIGREN (SE510436C2, hereinafter Wigren) and in further view of Ayyalasomayajula et al (US 20220196787 A1, hereinafter Ayyalasomayajula)
Regarding Claim 4. Ganu and Wigren disclose the invention in Claim 1
But do not teach
wherein the one or more WLAN signal measurements include one or more
channel state information (CSI) measurements.
However, Ayyalasomayajula teaches
(Ayyalasomayajula [0053, line 3] the WiFi device on mapping
device 108 may collect a channel state information (CSI) for
WiFi packets heard from all the access points in the
environment.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Ganu and Wigren and by incorporating Ayyalasomayajula to have CSI measurement method in WLAN signal measurements.
Motivation of doing so would have enabled the device to have more accurate location measurement for APs.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RANA HASSAN MAHMUD whose telephone number is (571)272-8939. The examiner can normally be reached Mon-Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kathy Wang-Hurst can be reached at 5712705371. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RANA H MAHMUD/Examiner, Art Unit 2644
/KATHY W WANG-HURST/Supervisory Patent Examiner, Art Unit 2644
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