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 .
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The Information Disclosure Statement filed on 05/29/2024 complies with 37 CFR 1.97. Therefore, the information referred therein has been considered.
Claim Rejections - 35 USC § 103
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.
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 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Beauregard et al. (US 2015/0327017) in view of Davaine and further in view of Hoffberg et al. (US 2007/0063875).
Regarding claim 1, Beauregard discloses a network state estimation method comprising: a round trip time (RTT) acquisition step of acquiring an RTT of each of a first received packet and a second received packet received via a network ([0039], “Once the system is operating, mobile device 110 will take repeated RTT measurements, or change in RTT (deltaRTT) measurements with the selected APs.”); a difference calculation step of calculating an RTT difference that is a difference between the RTT of the first received packet and the RTT of the second received packet ([0040], “The measurement of a change in RTT from the mobile device to an AP essentially only measures the radius of a circle from each AP to the mobile device. This may also be considered as the change in length of a vector from each AP to the corresponding mobile device.”); a filtering step of performing smoothing on the RTT difference ([0041], “In 204 of FIG. 2A, the data collected as part of the RTT measurements is analyzed. This analysis may include various types of filtering and processing such as particle or Kalman filtering.”). Beauregard does not explicitly disclose the plurality of Kalman filters in series. Davaine teaches by using a plurality of stages of Kalman filters connected in series with each other; a threshold specifying step of specifying a threshold for an output of a final stage of Kalman filter among the plurality of stages of Kalman filters on the basis of an output of a first stage of Kalman filter among the plurality of stages of Kalman filters (Davaine teaches a plurality of stages of Kalman filters connected in series, wherein a first stage Kalman filter outputs state estimates to a second, final stage Kalman filter (Section 4.3.3, page 26).). Beauregard in view of Davaine does not explicitly detail dynamically modifying a threshold of the final stage based on the output of the first stage. Hoffberg teaches and a state estimation step of comparing the output of the final stage of Kalman filter among the plurality of stages of Kalman filters with the specified threshold and estimating a state of the network on the basis of the comparison result ([0023], “The GPS receiver may incorporate a Kalman filter, which is adaptive and therefore automatically modifies its threshold of acceptable data perturbations, depending on the velocity of the vehicle (GPS antenna). This optimizes system response and accuracy of the GPS system. Generally, when the vehicle increases velocity by a specified amount, the GPS Kalman filter will raise its acceptable noise threshold. Similarly, when the vehicle decreases its velocity by a specified amount, the GPS Kalman filter will lower its acceptable noise threshold.”). 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 cascaded Kalman filter of Davaine to include the adaptive thresholding of Hoffberg. A POSITA would have been motivated to use the velocity state estimate outputted by Davaine’s first stage Kalman filter to dynamically specify the threshold for the final stage Kalman filter, in order to automatically adapt to changing vehicle dynamics and prevent the filter from rejecting valid data during high-speed maneuvers.
Regarding claim 2, Beauregard discloses the network state estimation method according to claim 1, wherein in the threshold specifying step, a candidate threshold that is a candidate for the threshold at the time of receiving the second received packet is calculated on the basis of the threshold at the time of receiving the first received packet ([0059], “In 502, a method begins with repeatedly measuring, over a first time period, a round trip time (RTT) between a mobile device and a first network access point to create a first RTT data set. In 504, the method continues with identifying a first threshold associated with the RTT between the mobile device and the first network access point. In such an embodiment, the threshold may be associated with a single set of measurements between a mobile device such as mobile device 110 and a particular AP such as AP 120a. 506 then involves repeatedly measuring, over the first time period, a RTT between the mobile device and a second network access point to create a second RTT data set.”), and an output of the first stage of Kalman filter that has performed smoothing on the RTT difference ([0041], “In 204 of FIG. 2A, the data collected as part of the RTT measurements is analyzed. This analysis may include various types of filtering and processing such as particle or Kalman filtering.”), a maximum value between the calculated candidate threshold and a predetermined lower limit value is calculated, and the calculated maximum value is specified as the threshold at the time of receiving the second received packet ([0059], “508 involves identifying a second threshold associated with the RTT between the mobile device and the second network access point. These measuring and threshold identification steps may be repeated for any number of sets of RTT measurements for different APs. 510 involves analyzing the first RTT data set in real-time or near real-time as the first RTT data set is created to identify changes in the RTT between the mobile device and the first network access point which are above the first threshold.”).
Regarding claim 3, Beauregard discloses the network state estimation method according to claim 2, wherein the lower limit value is set on the basis of a scheduling characteristic of the network ([0047], “FIG. 4 shows one potential implementation of a mobile device 400 that may be similar to mobile device 110 of FIG. 1. Further, mobile device 400 may also implement processing for initiating RTT measurements, deltaRTT measurements, and other measurements that may be used to determine a stopped state according to the embodiments described herein. Additional details of such processes may be initiated and managed by RTT positioning module 421.”).
Regarding claim 4, Beauregard discloses the network state estimation method according to claim 1, wherein in the state estimation step, a state of increase or decrease of the RTT in the network is estimated as a state of the network ([0041], “In 204 of FIG. 2A, the data collected as part of the RTT measurements is analyzed. This analysis may include various types of filtering and processing such as particle or Kalman filtering.”; [0044], “FIG. 3 then shows one potential example of RTT measurement data that may be used in order to make a stopped state determination. FIG. 3 shows an example of RTT measurements taken over time between a single mobile device such as mobile device 110 and a single AP such as AP 120a, with raw RTT data 310 and a filtered movement estimate 320 both shown. Filtered movement estimate 320 may be created in any acceptable filtering manner. This includes any filtering as described above in 224c. In various embodiments, the filtered movement estimate 320 may simply be a filter applied to raw RTT data 310 to remove the signal noise.”).
Regarding claim 5, Beauregard discloses the network state estimation method according to claim 1, further comprising a reception interval calculation step of calculating a reception interval between the first received packet and the second received packet on the basis of a transmission interval of a transmission packet for the first received packet and the second received packet, an RTT of the first received packet, and an RTT of the second received packet ([0059], “512 similarly involves analyzing the second RTT data set in real-time or near real-time as the second RTT data set is created to identify changes in the RTT between the mobile device and the second network access point which are above the second threshold. This analysis of the first and second sets of RTT data may then be used in 514 in determining whether the mobile device is in a stopped state at least in part by determining when the changes in the RTT between the mobile device and the first network access point are not above the first threshold at the same time that the changes in the RTT between the mobile device and the second network access point are not above the second threshold.”).
Regarding claim 6, Beauregard discloses a network state estimation apparatus comprising: at least one memory storing instructions ([0071], “The computing device 700 may further include (and/or be in communication with) one or more non-transitory storage devices 725”), and at least one processor configured to execute the instructions to ([0070], “The computing device 700 is shown comprising hardware elements that can be electrically coupled via a bus 705 (or may otherwise be in communication, as appropriate). The hardware elements may include one or more processors 710, including, without limitation, one or more general-purpose processors and/or one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, and/or the like);”): acquire a round trip time (RTT) of each of a first received packet and a second received packet received via a network ([0039], “Once the system is operating, mobile device 110 will take repeated RTT measurements, or change in RTT (deltaRTT) measurements with the selected APs.”); calculate an RTT difference that is a difference between the RTT of the first received packet and the RTT of the second received packet ([0040], “The measurement of a change in RTT from the mobile device to an AP essentially only measures the radius of a circle from each AP to the mobile device. This may also be considered as the change in length of a vector from each AP to the corresponding mobile device.”); perform smoothing on the RTT difference ([0041], “In 204 of FIG. 2A, the data collected as part of the RTT measurements is analyzed. This analysis may include various types of filtering and processing such as particle or Kalman filtering.”). Beauregard does not explicitly disclose the plurality of Kalman filters in series. Davaine teaches by using a plurality of stages of Kalman filters connected in series with each other; specify a threshold for an output of a final stage of Kalman filter among the plurality of stages of Kalman filters on the basis of an output of a first stage of Kalman filter among the plurality of stages of Kalman filters Davaine teaches a plurality of stages of Kalman filters connected in series, wherein a first stage Kalman filter outputs state estimates to a second, final stage Kalman filter (Section 4.3.3, page 26).). Beauregard in view of Davaine does not explicitly detail dynamically modifying a threshold of the final stage based on the output of the first stage. Hoffberg teaches and compare the output of the final stage of Kalman filter among the plurality of stages of Kalman filters with the specified threshold and to estimate a state of the network on the basis of the comparison result ([0023], “The GPS receiver may incorporate a Kalman filter, which is adaptive and therefore automatically modifies its threshold of acceptable data perturbations, depending on the velocity of the vehicle (GPS antenna). This optimizes system response and accuracy of the GPS system. Generally, when the vehicle increases velocity by a specified amount, the GPS Kalman filter will raise its acceptable noise threshold. Similarly, when the vehicle decreases its velocity by a specified amount, the GPS Kalman filter will lower its acceptable noise threshold.”). 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 cascaded Kalman filter of Davaine to include the adaptive thresholding of Hoffberg. A POSITA would have been motivated to use the velocity state estimate outputted by Davaine’s first stage Kalman filter to dynamically specify the threshold for the final stage Kalman filter, in order to automatically adapt to changing vehicle dynamics and prevent the filter from rejecting valid data during high-speed maneuvers.
Regarding claim 7, Beauregard discloses the network state estimation apparatus according to claim 6, wherein the at least one processor is further configured to execute the instructions to, calculate a candidate threshold that is a candidate for the threshold at the time of receiving the second received packet on the basis of the threshold at the time of receiving the first received packet ([0059], “In 502, a method begins with repeatedly measuring, over a first time period, a round trip time (RTT) between a mobile device and a first network access point to create a first RTT data set. In 504, the method continues with identifying a first threshold associated with the RTT between the mobile device and the first network access point. In such an embodiment, the threshold may be associated with a single set of measurements between a mobile device such as mobile device 110 and a particular AP such as AP 120a. 506 then involves repeatedly measuring, over the first time period, a RTT between the mobile device and a second network access point to create a second RTT data set.”), and an output of the first stage of Kalman filter that has performed smoothing on the RTT difference ([0041], “In 204 of FIG. 2A, the data collected as part of the RTT measurements is analyzed. This analysis may include various types of filtering and processing such as particle or Kalman filtering.”), calculate a maximum value between the calculated candidate threshold and a predetermined lower limit value, and specify the calculated maximum value as the threshold at the time of receiving the second received packet ([0059], “508 involves identifying a second threshold associated with the RTT between the mobile device and the second network access point. These measuring and threshold identification steps may be repeated for any number of sets of RTT measurements for different APs. 510 involves analyzing the first RTT data set in real-time or near real-time as the first RTT data set is created to identify changes in the RTT between the mobile device and the first network access point which are above the first threshold.”).
Regarding claim 8, Beauregard discloses the network state estimation apparatus according to claim 7, wherein the lower limit value is set on the basis of a scheduling characteristic of the network ([0047], “FIG. 4 shows one potential implementation of a mobile device 400 that may be similar to mobile device 110 of FIG. 1. Further, mobile device 400 may also implement processing for initiating RTT measurements, deltaRTT measurements, and other measurements that may be used to determine a stopped state according to the embodiments described herein. Additional details of such processes may be initiated and managed by RTT positioning module 421.”).
Regarding claim 9, Beauregard discloses the network state estimation apparatus according to claim 6, wherein the at least one processor is further configured to execute the instructions to estimate a state of increase or decrease of the RTT in the network as a state of the network ([0041], “In 204 of FIG. 2A, the data collected as part of the RTT measurements is analyzed. This analysis may include various types of filtering and processing such as particle or Kalman filtering.”; [0044], “FIG. 3 then shows one potential example of RTT measurement data that may be used in order to make a stopped state determination. FIG. 3 shows an example of RTT measurements taken over time between a single mobile device such as mobile device 110 and a single AP such as AP 120a, with raw RTT data 310 and a filtered movement estimate 320 both shown. Filtered movement estimate 320 may be created in any acceptable filtering manner. This includes any filtering as described above in 224c. In various embodiments, the filtered movement estimate 320 may simply be a filter applied to raw RTT data 310 to remove the signal noise.”).
Regarding claim 10, Beauregard discloses the network state estimation apparatus according to claim 6, wherein the at least one processor is further configured to execute the instructions to calculate a reception interval between the first received packet and the second received packet on the basis of a transmission interval of a transmission packet for the first received packet and the second received packet, an RTT of the first received packet, and an RTT of the second received packet ([0059], “512 similarly involves analyzing the second RTT data set in real-time or near real-time as the second RTT data set is created to identify changes in the RTT between the mobile device and the second network access point which are above the second threshold. This analysis of the first and second sets of RTT data may then be used in 514 in determining whether the mobile device is in a stopped state at least in part by determining when the changes in the RTT between the mobile device and the first network access point are not above the first threshold at the same time that the changes in the RTT between the mobile device and the second network access point are not above the second threshold.”).
Regarding claim 11, Beauregard discloses a network state estimation system, comprising: at least one memory storing instructions ([0071], “The computing device 700 may further include (and/or be in communication with) one or more non-transitory storage devices 725”), and at least one processor configured to execute the instructions to ([0070], “The computing device 700 is shown comprising hardware elements that can be electrically coupled via a bus 705 (or may otherwise be in communication, as appropriate). The hardware elements may include one or more processors 710, including, without limitation, one or more general-purpose processors and/or one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, and/or the like);”): acquire a round trip time (RTT) of each of a first received packet and a second received packet received via a network ([0039], “Once the system is operating, mobile device 110 will take repeated RTT measurements, or change in RTT (deltaRTT) measurements with the selected APs.”); calculate an RTT difference that is a difference between the RTT of the first received packet and the RTT of the second received packet ([0040], “The measurement of a change in RTT from the mobile device to an AP essentially only measures the radius of a circle from each AP to the mobile device. This may also be considered as the change in length of a vector from each AP to the corresponding mobile device.”); perform smoothing on the RTT difference ([0041], “In 204 of FIG. 2A, the data collected as part of the RTT measurements is analyzed. This analysis may include various types of filtering and processing such as particle or Kalman filtering.”). Beauregard does not explicitly disclose the plurality of Kalman filters in series. Davaine teaches by using a plurality of stages of Kalman filters connected in series with each other; specify a threshold for an output of a final stage of Kalman filter among the plurality of stages of Kalman filters on the basis of an output of a first stage of Kalman filter among the plurality of stages of Kalman filters (Davaine teaches a plurality of stages of Kalman filters connected in series, wherein a first stage Kalman filter outputs state estimates to a second, final stage Kalman filter (Section 4.3.3, page 26).). Beauregard in view of Davaine does not explicitly detail dynamically modifying a threshold of the final stage based on the output of the first stage. Hoffberg teaches and compare the output of the final stage of Kalman filter among the plurality of stages of Kalman filters with the specified threshold and to estimate a state of the network on the basis of the comparison result ([0023], “The GPS receiver may incorporate a Kalman filter, which is adaptive and therefore automatically modifies its threshold of acceptable data perturbations, depending on the velocity of the vehicle (GPS antenna). This optimizes system response and accuracy of the GPS system. Generally, when the vehicle increases velocity by a specified amount, the GPS Kalman filter will raise its acceptable noise threshold. Similarly, when the vehicle decreases its velocity by a specified amount, the GPS Kalman filter will lower its acceptable noise threshold.”). 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 cascaded Kalman filter of Davaine to include the adaptive thresholding of Hoffberg. A POSITA would have been motivated to use the velocity state estimate outputted by Davaine’s first stage Kalman filter to dynamically specify the threshold for the final stage Kalman filter, in order to automatically adapt to changing vehicle dynamics and prevent the filter from rejecting valid data during high-speed maneuvers.
Regarding claim 12, Beauregard discloses the network state estimation system according to claim 11, wherein the at least one processor is further configured to execute the instructions to, calculate a candidate threshold that is a candidate for the threshold at the time of receiving the second received packet on the basis of the threshold at the time of receiving the first received packet ([0059], “In 502, a method begins with repeatedly measuring, over a first time period, a round trip time (RTT) between a mobile device and a first network access point to create a first RTT data set. In 504, the method continues with identifying a first threshold associated with the RTT between the mobile device and the first network access point. In such an embodiment, the threshold may be associated with a single set of measurements between a mobile device such as mobile device 110 and a particular AP such as AP 120a. 506 then involves repeatedly measuring, over the first time period, a RTT between the mobile device and a second network access point to create a second RTT data set.”), and an output of the first stage of Kalman filter that has performed smoothing on the RTT difference ([0041], “In 204 of FIG. 2A, the data collected as part of the RTT measurements is analyzed. This analysis may include various types of filtering and processing such as particle or Kalman filtering.”), calculate a maximum value between the calculated candidate threshold and a predetermined lower limit value, and specify the calculated maximum value as the threshold at the time of receiving the second received packet ([0059], “508 involves identifying a second threshold associated with the RTT between the mobile device and the second network access point. These measuring and threshold identification steps may be repeated for any number of sets of RTT measurements for different APs. 510 involves analyzing the first RTT data set in real-time or near real-time as the first RTT data set is created to identify changes in the RTT between the mobile device and the first network access point which are above the first threshold.”).
Regarding claim 13, Beauregard discloses the network state estimation system according to claim 11, wherein the lower limit value is set on the basis of a scheduling characteristic of the network ([0047], “FIG. 4 shows one potential implementation of a mobile device 400 that may be similar to mobile device 110 of FIG. 1. Further, mobile device 400 may also implement processing for initiating RTT measurements, deltaRTT measurements, and other measurements that may be used to determine a stopped state according to the embodiments described herein. Additional details of such processes may be initiated and managed by RTT positioning module 421.”).
Regarding claim 14, Beauregard discloses the network state estimation system according to claim 14, wherein the at least one processor is further configured to execute the instructions to estimate a state of increase or decrease of the RTT in the network as a state of the network ([0041], “In 204 of FIG. 2A, the data collected as part of the RTT measurements is analyzed. This analysis may include various types of filtering and processing such as particle or Kalman filtering.”; [0044], “FIG. 3 then shows one potential example of RTT measurement data that may be used in order to make a stopped state determination. FIG. 3 shows an example of RTT measurements taken over time between a single mobile device such as mobile device 110 and a single AP such as AP 120a, with raw RTT data 310 and a filtered movement estimate 320 both shown. Filtered movement estimate 320 may be created in any acceptable filtering manner. This includes any filtering as described above in 224c. In various embodiments, the filtered movement estimate 320 may simply be a filter applied to raw RTT data 310 to remove the signal noise.”).
Regarding claim 15, Beauregard discloses the network state estimation system according to claim 11, wherein the at least one processor is further configured to execute the instructions to calculate a reception interval between the first received packet and the second received packet on the basis of a transmission interval of a transmission packet for the first received packet and the second received packet, an RTT of the first received packet, and an RTT of the second received packet ([0059], “512 similarly involves analyzing the second RTT data set in real-time or near real-time as the second RTT data set is created to identify changes in the RTT between the mobile device and the second network access point which are above the second threshold. This analysis of the first and second sets of RTT data may then be used in 514 in determining whether the mobile device is in a stopped state at least in part by determining when the changes in the RTT between the mobile device and the first network access point are not above the first threshold at the same time that the changes in the RTT between the mobile device and the second network access point are not above the second threshold.”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nick A Sundara whose telephone number is (571)272-6749. The examiner can normally be reached M-TH 7:30-5:30 EST.
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/NICK ANON SUNDARA/Examiner, Art Unit 2479 /JAE Y LEE/Supervisory Patent Examiner, Art Unit 2479
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