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 Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 3, 4, 5, 6, 8, 11, 12, 13, 14, 15, 17, 18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by AMINI et al. (US 20240205873).
Regarding claims 1, 11, 17, AMINI et al. (US 20240205873) teaches a method comprising:
configuring, by a network device (fig. 4), a first access point (AP) and a second AP to communicate over a first frequency (fig. 4, par. 33, 47, 48, 50, 55, This allows the AP 105 to be informed of other devices and the transmission characteristics of the other devices to allow the AP 105 to configure its transmission parameters, such as a channel (e.g., an assigned block of frequencies for communication), transmission power, scheduling, and other information…the various measurements illustrated in FIG. 4 may also be done in various line-of-sight (LOS) frequency bands and non-line-of-sight (NLOS) frequency bands);
receiving, by the network device, a first set of Fine Timing Measurement (FTM) metrics between the first AP and the second AP over the first frequency from one or both of the first AP or the second AP (par. 47, 48, 49, 50, the various measurements illustrated in FIG. 4 may also be done in various line-of-sight (LOS) frequency bands and non-line-of-sight (NLOS) frequency bands…the weight estimator 500 (Location Server) receives a plurality of measurements including at least one of a GNSS measurement 502, a UWB measurement 504, and an FTM measurement 506.);
reconfiguring, by the network device, the first AP and the second AP to communicate over a second frequency different from the first frequency (par. 33, 47, 48, 55, 56, This allows the AP 105 to be informed of other devices and the transmission characteristics of the other devices to allow the AP 105 to configure its transmission parameters, such as a channel (e.g., an assigned block of frequencies for communication)… the various measurements illustrated in FIG. 4 may also be done in various line-of-sight (LOS) frequency bands and non-line-of-sight (NLOS) frequency bands);
receiving, by the network device, a second set of FTM metrics between the first AP and the second AP over the second frequency from one or both of the first AP or the second AP (par. 47, 48, 49, 50, the various measurements illustrated in FIG. 4 may also be done in various line-of-sight (LOS) frequency bands and non-line-of-sight (NLOS) frequency bands…the weight estimator 500 (Location Server) receives a plurality of measurements including at least one of a GNSS measurement 502, a UWB measurement 504, and an FTM measurement 506.); and
determining, by the network device, whether the first AP and the second AP are in Line-of-Sight (LoS) based on the first set of FTM metrics and the second set of FTM metrics (par. 50, 55, 61, 62, 64, The metrics estimation engine 520 may also use inter-protocol validation, which is described below with respect to the recalculation decision engine 560, to determine LOS or NLOS).
Regarding claim 2, AMINI teaches the method of claim 1, wherein the configuring comprises tuning the first AP and the second AP to communicate with each other on a first Wireless Fidelity (Wi-Fi) channel (par. 3, Wi-FI).
Regarding claim 3, AMINI teaches the method of claim 1, wherein the reconfiguring comprises tuning the first AP and the second AP to communicate with each other on a second Wi-Fi channel (par. 3, Wi-Fi).
Regarding claims 4, 13, AMINI teaches the method of claim 1, further comprising: instructing, by the network device, the first AP to initiate a first FTM sequence with the second AP over the first frequency after configuring the first AP and the second AP to communicate over the first frequency (par. 52, 63, The location server 610 responds with a measurement request 614 that requests the AP 602 to provide multiple measurement requests based on the capabilities of the AP 602); and
instructing, by the network device, the first AP to initiate a second FTM sequence with the second AP over the second frequency after reconfiguring the first AP and the second AP to communicate over the second frequency (par. 52, 63, The location server 610 responds with a measurement request 614 that requests the AP 602 to provide multiple measurement requests based on the capabilities of the AP 602), wherein each of the first FTM sequence and the second FTM sequence comprises one or more FTM exchanges (par. 63, The AP 602 may also transmit a reference signal 620 (e.g., an FTM measurement signal) to a coordinating AP 604, which is also received by another coordinating AP 606. The coordinating AP 604 responds to the reference signal with a reference signal reply 622 (e.g., an FTM measurement reply)).
Regarding claims 5, 14, AMINI teaches the method of claim 4, wherein: the first set of FTM metrics comprises one or more first FTM distances between the first AP and the second AP, one or more first signal strength values between the first AP and the second AP, or both determined during the first FTM sequence (par. 40, 50, 52, 58, the relative positioning measurement at block 315 can include multiple measurements using different measurement techniques, such as RTT, signal strength (e.g., RSSI), carrier phase, and so forth…the FTM measurement 506 include information related to distance…a multidimensional array of times, distances, and other information from the UWB measurement 504 and/or the FTM measurement 506); and
the second set of FTM metrics comprises one or more of second FTM distances between the first AP and the second AP or one or more second signal strength values between the first AP and the second AP, or both determined during the second FTM sequence (par. 40, 50, 52, 58, the relative positioning measurement at block 315 can include multiple measurements using different measurement techniques, such as RTT, signal strength (e.g., RSSI), carrier phase, and so forth…the FTM measurement 506 include information related to distance…a multidimensional array of times, distances, and other information from the UWB measurement 504 and/or the FTM measurement 506).
Regarding claim 6, AMINI teaches the method of claim 5, further comprising calculating, by the network device, one or more of: a first standard deviation of the one or more first FTM distances and the one or more second FTM distances; and a first mean of the one or more first FTM distances and the one or more second FTM distances (par. 60, 61, standard deviation of multiple measurements).
Regarding claim 8, AMINI teaches the method of claim 6, further comprising calculating one or more of: a second standard deviation of the one or more first signal strength values and the one or more first signal strength values; and a second mean of the one or more first signal strength values and the one or more first signal strength values (par. 40, 60, 61, multiple measurements using different measurement techniques, such as RTT, signal strength (e.g., RSSI), carrier phase, and so forth …the standard deviation of multiple measurements).
Regarding claim 12, AMINI teaches the network device of claim 11, wherein: to configure the first AP and the second AP, the processing resource is configured to execute one or more of the instructions to tune the first AP and the second AP to communicate with each other on a first Wireless Fidelity (Wi-Fi) channel (par. 3, Wi-FI); and to reconfigure the first AP and the second AP, the processing resource is configured to execute one or more of the instructions to tune the first AP and the second AP to communicate with each other on a second Wi-Fi channel (par. 3, Wi-FI).
Regarding claim 15, AMINI teaches the network device of claim 14, wherein the processing resource is configured to execute one or more of the instructions to calculate one or more of: a first standard deviation of the one or more first FTM distances and the one or more second FTM distances; a first mean of the one or more first FTM distances and the one or more second FTM distances; a second standard deviation of the one or more first signal strength values and the one or more first signal strength values; and a second mean of the one or more first signal strength values and the one or more first signal strength values (par. 60, 61, standard deviation of multiple measurements).
Regarding claim 18, AMINI teaches the non-transitory machine-readable medium of claim 17, wherein: the first set of FTM metrics comprises one or more first FTM distances between the first AP and the second AP, one or more first signal strength values between the first AP and the second AP, or both determined during a first FTM sequence (par. 40, 50, 52, 58, the relative positioning measurement at block 315 can include multiple measurements using different measurement techniques, such as RTT, signal strength (e.g., RSSI), carrier phase, and so forth…the FTM measurement 506 include information related to distance…a multidimensional array of times, distances, and other information from the UWB measurement 504 and/or the FTM measurement 506); and the second set of FTM metrics comprises one or more of second FTM distances between the first AP and the second AP or one or more second signal strength values between the first AP and the second AP, or both determined during a second FTM sequence (par. 40, 50, 52, 58, the relative positioning measurement at block 315 can include multiple measurements using different measurement techniques, such as RTT, signal strength (e.g., RSSI), carrier phase, and so forth…the FTM measurement 506 include information related to distance…a multidimensional array of times, distances, and other information from the UWB measurement 504 and/or the FTM measurement 506).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 7, 9, 16, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over AMINI et al. (US 20240205873) in view of THOMAS et al. (US 20230204705).
Regarding claim 7, AMINI teaches the method of claim 6, further comprising classifying a pair of the first AP and the second AP into one of two clusters (par. 52, a machine learning (ML) model of the inter-AP location engine 515 may be trained to classify and then select locations of each AP).
However, AMINI does not teach classifying based on one or more of the first standard deviation or the first mean.
But, THOMAS et al. (US 20230204705) in a similar or same field of endeavor teaches classifying a pair of the first AP and the second AP into one of two clusters based on one or more of the first standard deviation or the first mean (par. 92, LOS and/or NLOS classification may be based upon a threshold function (β) which may include a combination of features such as signal standard deviation and mean, signal kurtosis, skewness, Rician K-factor, and/or goodness of fit.).
Thus, it would have been obvious to the person of ordinary skill in the art before the effectively filing date of the claimed invention to implement the system or method as taught by THOMAS in the system of AMINI to classify the AP.
The motivation would have been to recover positioning performance or increase accuracy of a UE estimate based on corresponding positioning measurements.
Regarding claim 9, AMINI teaches the method of claim 8, further comprising classifying a pair of the first AP and the second AP into two clusters (par. 52, a machine learning (ML) model of the inter-AP location engine 515 may be trained to classify and then select locations of each AP).
However, AMINI does not teach classifying based on one or more of the first standard deviation, the first mean, the second standard deviation, or the second mean using a clustering technique.
But, THOMAS et al. (US 20230204705) in a similar or same field of endeavor teaches classifying a pair of the first AP and the second AP into two clusters based on one or more of the first standard deviation, the first mean, the second standard deviation, or the second mean using a clustering technique (par. 92, LOS and/or NLOS classification may be based upon a threshold function (β) which may include a combination of features such as signal standard deviation and mean, signal kurtosis, skewness, Rician K-factor, and/or goodness of fit.).
Thus, it would have been obvious to the person of ordinary skill in the art before the effectively filing date of the claimed invention to implement the system or method as taught by THOMAS in the system of AMINI to classify the AP.
The motivation would have been to recover positioning performance or increase accuracy of a UE estimate based on corresponding positioning measurements.
Regarding claim 16, AMINI teaches the network device of claim 15, wherein the processing resource is configured to execute one or more of the instructions to classify a pair of the first AP and the second AP into two clusters (par. 52, a machine learning (ML) model of the inter-AP location engine 515 may be trained to classify and then select locations of each AP)
However, AMINI does not teach classify based on one or more of the first standard deviation, the first mean, the second standard deviation, or the second mean.
But, THOMAS et al. (US 20230204705) in a similar or same field of endeavor teaches classify a pair of the first AP and the second AP into two clusters based on one or more of the first standard deviation, the first mean, the second standard deviation, or the second mean (par. 92, LOS and/or NLOS classification may be based upon a threshold function (β) which may include a combination of features such as signal standard deviation and mean, signal kurtosis, skewness, Rician K-factor, and/or goodness of fit.).
Thus, it would have been obvious to the person of ordinary skill in the art before the effectively filing date of the claimed invention to implement the system or method as taught by THOMAS in the system of AMINI to classify the AP.
The motivation would have been to recover positioning performance or increase accuracy of a UE estimate based on corresponding positioning measurements.
Regarding claim 19, AMINI teaches the non-transitory machine-readable medium of claim 18, wherein the instructions further comprise: instructions to calculate a first standard deviation of the one or more first FTM distances and the one or more second FTM distances (par. 60, 61, standard deviation of multiple measurements); instructions to calculate a second standard deviation of the one or more first signal strength values and the one or more first signal strength values (par. 60, 61, standard deviation of multiple measurements);
However, AMINI does not teach instructions to calculate a first mean of the one or more first FTM distances and the one or more second FTM distances; and instructions to calculate a second mean of the one or more first signal strength values and the one or more first signal strength values.
But, THOMAS et al. (US 20230204705) in a similar or same field of endeavor teaches instructions to calculate a first mean of the one or more first FTM distances and the one or more second FTM distances (par. 92, 119, LOS and/or NLOS classification may be based upon a threshold function (β) which may include a combination of features such as signal standard deviation and mean, signal kurtosis, skewness, Rician K-factor, and/or goodness of fit.); and instructions to calculate a second mean of the one or more first signal strength values and the one or more first signal strength values (par. 92, 119, LOS and/or NLOS classification may be based upon a threshold function (β) which may include a combination of features such as signal standard deviation and mean, signal kurtosis, skewness, Rician K-factor, and/or goodness of fit.).
Thus, it would have been obvious to the person of ordinary skill in the art before the effectively filing date of the claimed invention to implement the system or method as taught by THOMAS in the system of AMINI to classify the AP.
The motivation would have been to recover positioning performance or increase accuracy of a UE estimate based on corresponding positioning measurements.
Regarding claim 20, AMINI teaches the non-transitory machine-readable medium of claim 19, wherein the instructions further comprise instructions to classify a pair of the first AP and the second AP into two clusters (par. 52, a machine learning (ML) model of the inter-AP location engine 515 may be trained to classify and then select locations of each AP).
However, AMINI does not teach classify based on one or more of the first standard deviation, the first mean, the second standard deviation, or the second mean.
But, THOMAS et al. (US 20230204705) in a similar or same field of endeavor teaches classify a pair of the first AP and the second AP into two clusters based on one or more of the first standard deviation, the first mean, the second standard deviation, or the second mean (par. 92, LOS and/or NLOS classification may be based upon a threshold function (β) which may include a combination of features such as signal standard deviation and mean, signal kurtosis, skewness, Rician K-factor, and/or goodness of fit.).
Thus, it would have been obvious to the person of ordinary skill in the art before the effectively filing date of the claimed invention to implement the system or method as taught by THOMAS in the system of AMINI to classify the AP.
The motivation would have been to recover positioning performance or increase accuracy of a UE estimate based on corresponding positioning measurements.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over AMINI et al. (US 20240205873) and THOMAS et al. (US 20230204705) as applied to claim 9 above, and further in view of MORRISON et al. (US 20230384412).
Regarding claim 10, THOMAS teaches the method of claim 9, wherein the clustering technique comprises Centroid-based clustering, Density-based clustering, Distribution-based Clustering, or combinations thereof (par. 92, mean or centroid based clustering, classification based on threshold or density).
However, AMINI and THOMAS do not explicitly teach Distribution-based Clustering;
But, MORRISON et al. (US 20230384412) in a similar or same field of endeavor teaches wherein the clustering technique comprises Centroid-based clustering, Density-based clustering, Distribution-based Clustering, or combinations thereof (par. 114, the actual position of the GNSS device 1402 based on the clustering/distribution patterns).
Thus, it would have been obvious to the person of ordinary skill in the art before the effectively filing date of the claimed invention to implement the system or method as taught by MORRISON in the system of AMINI and THOMAS to classify the AP.
The motivation would have been to improve the performance and accuracy of based positioning.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
MANOLAKOS et al. (US 20190380054) teaches the UE 504 receives an NLOS data stream 523 of RF signals transmitted on beam 513 and an LOS data stream 524 of RF signals transmitted on beam 514 (par. 63).
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/THINH D TRAN/for /Thinh Tran/, Patent Examiner of Art Unit 2466 07/11/2026
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