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 .
Priority
The pending application 18/043,816, filed on 3/2/2023, is a national stage application filed under 35 U.S.C. 371 of PCT/US2021/071774, filed on 10/7/02021, and claims priority from foreign application GR20200100616, filed on 10/12/2020.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 22 JAN 2026 has been entered.
Response to Amendment
Applicant's amendment filed on 22 JAN 2026 has been entered. Claims 1, 3, 8, 12-13, 21, 23, 28 and 32-33 have been amended. Claims 7 and 27 have been cancelled. Claims 1-6, 8-26 and 28-40 are still pending in this application, with claims 1, 12, 21 and 32 being independent.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 12, 21 and 32 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.
Applicant argues that “ Dupray fails to disclose a base station that calculates statistics of one or more time-angle metrics based on a signal received from a UE and that reports those statistics to a network entity, as recited in claims 1 and 21, and fails to disclose a network entity that receives such statistics from a base station, as recited in claims 12 and 32, wherein those statistics comprise parameters of a probability distribution function, as claimed. Moreover, during the Examiner interview summary conducted on December 19, 2025, the Office agreed that this feature was not disclosed in Dupray, Karr, or Marshall.” (Applicant’s remarks p. 10)
Upon further search and consideration, newly cited Uchiyama et al. (EP 3,018,923 A1) is relied upon to teach the argued features. Therefore, applicant’s arguments are moot.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8-26, and 28-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter according to the subject matter eligibility flowchart analysis described below:
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Regarding step 1:
(claim 1) … a method (i.e. process)
(claim 12) … a method (i.e. process)
(claim 21) … a base station (i.e. product)
(claim 32) … a network entity (i.e. product)
Regarding Step 2A, prong 1:
Claims 1, 12, 21 and 32 recite the following elements which, under a broadest reasonable interpretation of the claimed invention constitute either mathematical calculations or mental processes for the articulated reasons given in parenthesis:
(claim 1, lines 3-5) calculating statistics of one or more time-angle metrics based on a signal received from a user equipment (UE), the statistics comprising parameters of a probability distribution function (the BRI of the calculating statistics… step is reasonably considered a mathematical calculation in light of the specification teaching calculating the probability distribution, see ¶ [0104], “At 504, the base station102 calculates statistics of one or more time-angle metrics based on signals from the UE104. In some aspects, calculating statistics of a time-angle metric comprises calculating a probability distribution of the time-angle metric, a mean of the time-angle metric, a standard deviation of the time-angle metric, or combinations thereof.”)
(claim 12, lines 6) calculating, based on the statistics, an estimated position of the UE (the BRI of the calculating… an estimated position of the UE step is reasonably considered a mathematical calculation in light of the specification teaching calculating the probability distribution, see ¶ [0104], “At 504, the base station102 calculates statistics of one or more time-angle metrics based on signals from the UE104. In some aspects, calculating statistics of a time-angle metric comprises calculating a probability distribution of the time-angle metric, a mean of the time-angle metric, a standard deviation of the time-angle metric, or combinations thereof.”)
(claim 21, lines 6-7) calculate statistics of one or more time-angle metrics, based on a signal received from a user equipment (UE), the statistics comprising parameters of a probability distribution function (the BRI of the calculate statistics… step is reasonably considered a mathematical calculation in light of the specification teaching calculating the probability distribution, see ¶ [0104], “At 504, the base station102 calculates statistics of one or more time-angle metrics based on signals from the UE104. In some aspects, calculating statistics of a time-angle metric comprises calculating a probability distribution of the time-angle metric, a mean of the time-angle metric, a standard deviation of the time-angle metric, or combinations thereof.”)
(claim 32, line 9) calculate, based on the statistics, an estimated position of the UE (the BRI of the calculate… an estimated position of the UE step is reasonably considered a mathematical calculation in light of the specification teaching calculating the probability distribution, see ¶ [0104], “At 504, the base station102 calculates statistics of one or more time-angle metrics based on signals from the UE104. In some aspects, calculating statistics of a time-angle metric comprises calculating a probability distribution of the time-angle metric, a mean of the time-angle metric, a standard deviation of the time-angle metric, or combinations thereof.”)
Regarding Step 2A, prong 2:
Claims 1, 12, 21 and 32 do not integrate the claimed abstract idea into a practical application. Claims 1, 12, 21 and 32 recite the following elements beyond the judicial exception, but fail to impose a meaningful limit on the judicial exception for the articulated reasons given in parenthesis:
(claim 1, line 6) … reporting the statistics to a network entity (amounts to mere data gathering, thus failing to impose a meaningful limit on the judicial exception)
(claim 12, lines 3-4) … receiving, from a base station, statistics of one or more time-angle metrics associated with a user equipment (UE), the statistics comprising parameters of a probability distribution function… (amounts to mere data gathering, thus failing to impose a meaningful limit on the judicial exception)
(claim 21, line 4) … at least one processor… (amounts to merely using a computer/generic computer components as a tool to perform an abstract idea, thus failing to impose a meaningful limit on the judicial exception)
(claim 21, line 9) … report the statistics to a network entity (amounts to mere data gathering, thus failing to impose a meaningful limit on the judicial exception)
(claim 32, line 4) … at least one processor… (amounts to merely using a computer/generic computer components as a tool to perform an abstract idea, thus failing to impose a meaningful limit on the judicial exception)
(claim 32, lines 6-8) … receive, via the at least one transceiver, from a base station, statistics of one or more time-angle metrics associated with a user equipment (UE), the statistics comprising parameters of a probability distribution function (amounts to mere data gathering, thus failing to impose a meaningful limit on the judicial exception)
Regarding Step 2B:
Claims 1, 12, 21 and 32 do not recite additional elements, taken individually and in combination, that result in the claims as a whole, amounting to significantly more than the exception for the following reasons given in parenthesis:
(claim 1, line 6) … reporting the statistics to a network entity (amounts to mere data gathering, thus failing to amount to significantly more than judicial exception)
(claim 12, lines 3-4) … receiving, from a base station, statistics of one or more time-angle metrics associated with a user equipment (UE), the statistics comprising parameters of a probability distribution function… (amounts to mere data gathering, thus failing to amount to significantly more than judicial exception)
(claim 21, line 4) … at least one processor… (amounts to merely using a computer/generic computer components as a tool to perform an abstract idea, thus failing to amount to significantly more than judicial exception)
(claim 21, line 9) … report the statistics to a network entity (amounts to mere data gathering, thus failing to amount to significantly more than judicial exception)
(claim 32, line 4) … at least one processor… (amounts to merely using a computer/generic computer components as a tool to perform an abstract idea, thus failing to amount to significantly more than judicial exception)
(claim 32, lines 6-8) … receive, via the at least one transceiver, from a base station, statistics of one or more time-angle metrics associated with a user equipment (UE), the statistics comprising parameters of a probability distribution function (amounts to mere data gathering, thus failing to amount to significantly more than judicial exception)
Dependent claims 2-6, 8-11, 13-20, 22-26, 28-31 and 33-40 include additional steps of reporting and receiving statistics and identifying information, which constitute either mathematical calculations or mental processes similar to the independent claims discussed above. Therefore, all dependent claims are also rejected under 35 U.S.C. 101 in a similar fashion and analysis as shown above for the rejected independent claims.
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.
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) 1-9, 11-15, 17-29 and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dupray (US 8,135,413 B2, previously relied upon by the examiner) in view of Uchiyama et al. (EP 3,018,923 A1, newly cited by the examiner).
Regarding claim 1 (Currently Amended), Dupray discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
A method of wireless communication performed by a base station (Dupray mobile base station 148, Fig. 4), the method comprising:
calculating statistics of one or more time-angle metrics (Dupray “the location models provided may include not only the radius-radius/TOA and TDOA techniques but also adaptive techniques such as artificial neural net techniques… and angle or direction of arrival techniques” – Col. 17, lines 45-50; “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45) based on a signal received from a user equipment (Dupray mobile station MS 140, Fig. 4) and
reporting the statistics to a network entity (Dupray “The MBS 148 also includes a mobile station 140 for data communication with the gateway 142, via a BS 122. In particular, such data communication includes telemetering…MBS 148 estimates of the location of the target MS 140.” – Col. 38, lines 52-57).
Uchiyama et al. discloses:
calculating statistics, the statistics comprising parameters of a probability distribution function (Uchiyama et al. “For example, each eNB, instead of the NE 100, may generate the statistical information.” - ¶ [0305]; “As a first specific method, the statistical information generation unit 133 creates a probability density function of the CQI indicating the probability distribution of the CQI to generate the region statistical information on the CQI from the probability density function.” - ¶ [0043])
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Uchiyama et al. into the invention of Dupray to yield the invention of claim 1. Both Dupray and Uchiyama et al. are considered analogous arts to the claimed invention as they both disclose calculating statistical information for positioning of user equipment in a wireless communication network. Dupray discloses the limitations of claim 1 outlined above. However, Dupray fails to explicitly disclose the statistics comprising parameters of a probability distribution function. This feature is disclosed by Uchiyama et al. where that statistical information includes a probability density function that can be generated by each eNB (Uchiyama et al. ¶ [0043], [0305]). The combination of Dupray and Uchiyama et al. would be obvious with a reasonable expectation of success to reduce the amount of control information transmitted and received by the base station in order to optimize the use of wireless resources in the wireless communication system (Uchiyama et al. ¶ [0006], [0289]).
Regarding claim 2 (Original), Dupray as modified above discloses:
The method of claim 1, wherein the one or more time-angle metrics comprise one or more of an uplink (UL) time of arrival (ToA), a downlink (DL) ToA, an UL time difference of arrival (TDoA), a DL TDoA, a round-trip time (RTT), an angle of arrival (AoA), a zenith of arrival (ZoA), UJL transmit-to-receive time difference, DL transmit-to-receive time difference, or combinations thereof (Dupray “mobile location base station is used for locating a target MS via, for example, time-of-arrival or time difference-of-arrival measurements” – Col. 17, lines 14-18; angle or direction of arrival - Col. 17, line 50).
Regarding claim 3 (Currently Amended), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 1, wherein the parameters of the probability distribution function (Dupray “the invention of “Lo provide further embodiments of wireless location estimators that may be used as First Order Models 1224. In particular, the '642 patent determines a corresponding probability density function (pdf) about each of a plurality of base stations in communication with the target MS 140.” – Col. 76, lines 14-20)
Uchiyama et al. discloses:
the parameters of the probability distribution function comprise a mean (Uchiyama et al. “Furthermore, the statistical information is, for example, an average value in the probability distribution.” - ¶ [0046]), a mean vector, a standard deviation (Uchiyama et al. “The dispersion (that is, reliability) calculated in this manner indicates that, when the value is larger, variations in the region statistical information is small and the reliability of the region statistical information is higher.” - ¶ [0058]), a covariance matrix, a weight vector, a weight matrix, or combinations thereof.
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Uchiyama et al. into the invention of Dupray to yield the invention of claim 3. Both Dupray and Uchiyama et al. are considered analogous arts to the claimed invention as they both disclose calculating statistical information for positioning of user equipment in a wireless communication network. Dupray as modified above discloses the invention of claim 1. However, Dupray fails to explicitly disclose the statistics comprising parameters of a probability distribution function. This feature is disclosed by Uchiyama et al. where parameters of the probability distribution function comprise an average value and a dispersion (Uchiyama et al. ¶ [0046], [0058]). The combination of Dupray and Uchiyama et al. would be obvious with a reasonable expectation of success to reduce the amount of control information transmitted and received by the base station in order to optimize the use of wireless resources in the wireless communication system (Uchiyama et al. ¶ [0006], [0289]).
Regarding claim 4 (Original), Dupray as modified above discloses:
The method of claim 1, wherein reporting the statistics comprises reporting statistics for a time-angle metric relative to a reference value (Dupray “the FOMs have been calibrated to thereby output confidence values (probabilities) related to the likelihood of correspondingly generated hypotheses being correct” – Col. 18, lines 57-60), wherein the reference value comprises a value calculated by the base station (Dupray “an MBS location subsystem 1508 architecture may be provided that has one or more first order models 1224 whose output is supplied to, for example, a blackboard or expert system” – Col. 86, lines 53-56; where the blackboard or expert system is part of the location gateway, Fig. 6(2)), a value reported to the base station, or combinations thereof.
Regarding claim 5 (Original), Dupray as modified above discloses:
The method of claim 1, wherein the reporting the statistics comprises identifying what information was used to calculate the statistics (Dupray location hypothesis data structure includes FOM ID, MS ID, timestamps, and location signature cluster – Table LH-1), the information comprising at least one of an UL channel profile, a DL channel profile, an uplink signal, or a report about a downlink signal (Dupray “For each target MS location estimate generated and utilized by an embodiment of the present disclosure, the location estimate may be provided in a data structure (or object class) denoted as a "location hypothesis" (illustrated in Table LH-1)” – Col. 44, lines 34-38; location hypothesis includes loc_sig_cluster which “provides access to the collection of location signature signal characteristics derived from communications between the target MS 140 and base station(s)… the location data accessed here is provided to the first order models” – Table LH-1; where the loc_sig_cluster is considered the channel profile; “a mobile location base station can periodically be in bi-directional communication with a target MS for determining a signal time-of-arrival (or time-difference-of-arrival) measurement between the mobile location base station and the target MS.” – Col. 17, lines 9-10; the channel profiles are uplink channel profiles and/or downlink profiles).
Regarding claim 6 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 1, (Dupray “the invention of “Lo provide further embodiments of wireless location estimators that may be used as First Order Models 1224. In particular, the '642 patent determines a corresponding probability density function (pdf) about each of a plurality of base stations in communication with the target MS 140.” – Col. 76, lines 14-20), or combinations thereof.
Although Dupray does not explicitly disclose that the mobile base station reports a marginal probability distribution of one time-angle metric, a joint probability distribution of a plurality of time-angle metrics, or combinations thereof, Dupray does disclose that the mobile base station comprises first order models (Dupray “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45), and estimates and reports the location of the target mobile station to the location center (Dupray “The MBS 148 also includes a mobile station 140 for data communication with the gateway 142, via a BS 122. In particular, such data communication includes telemetering…MBS 148 estimates of the location of the target MS 140.” – Col. 38, lines 52-57). The first order models produce the location estimates that are reported to the location center. In particular, the first order model of Lo outputs a probability density function (Dupray Col. 76, lines 14-20). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to include the step of reporting a marginal probability distribution of one time-angle metric, a joint probability distribution of a plurality of time-angle metrics, or combinations thereof into in the method of Dupray to provide measurements about the mobile station to a hypothesis analyzer in the location center in order to resolve conflicts between hypotheses in a current activation for locating the target mobile station (Dupray Col. 78, lines 26-27).
Regarding claim 8 (Currently Amended), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 1, wherein (Dupray “Binned_cells sort the cells of the area of interest by their probabilities into bins where each successive bin includes those cells whose confidence values are within a smaller (non-overlapping) range from that of any preceding bin.” – embodiment of program shown in Col. 61), wherein the PMF is based on the probability distribution function (Dupray “a Gaussian or other probabilistic distribution of probability values” – Col. 60, line 59) quantized into the set of bins (Dupray “The most likelihood estimator 1344 receives a collection of active or relevant location hypotheses from the hypothesis analyzer 1332 and uses these location hypotheses to determine one or more most likely estimates for the target MS 140.” – Col. 60, lines 35-29; the program iterates through the bins to determine the most likely location and the probability as determined by the range of bins – Col. 61)
Although Dupray does not explicitly disclose calculating a probability mass function (PMF) at the mobile base station, and wherein reporting the statistics to the network entity comprises reporting a probability that a time-angle metric is within a bin range, Dupray does disclose the most likelihood estimator (Dupray location estimator 1344, Figs. 6(2), 8(4)) of the location engine calculates the probability mass distribution function and outputs a probability that a time-angle metric is within a bin range. Dupray also discloses that “the architecture of the location engine 139 may also be applied for providing a second embodiment of the mobile base station location subsystem 1508” (Dupray Col. 86, lines 48-50). Therefore, it is obvious that the mobile base station may comprise the location estimator and calculate the probability mass distribution function and outputting a probability that a time-angle metric is within a bin range. Further, the mobile base station transmits additional mobile station location information to the location center. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing modify the method of Dupray to include the step of the mobile base station calculating and reporting a probability that a time-angle metric is within a bin range in order to improve the accuracy and reliability of tracking the mobile station and increase the extensibility and flexibility of the location center (Dupray “It is important to note that the architecture for the location center/gateway 142 and the location engine provided by the present disclosure is designed for extensibility and flexibility so that MS 140 location accuracy and reliability may be enhanced as further location data become available and as enhanced MS location techniques become available.” – Col. 52, lines 57-62).
Regarding claim 9 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 1, comprises reporting the percentile values (Dupray “Binned_cells sort the cells of the area of interest by their probabilities into bins where each successive bin includes those cells whose confidence values are within a smaller (non-overlapping) range from that of any preceding bin. Further, assume there are, e.g., 100 bins BI wherein B1 has cells with confidences within the range [0, 0.1], and BI has cells with confidences within the range [(i - 1) * 0.01, i * 0.01]” – embodiment of program shown in Col. 61; where each bin represents a percentile of the probability).
Although Dupray does not explicitly disclose calculating the statistics comprises calculating percentile values over a pre-determined set of percentiles, and wherein reporting the statistics to the network entity comprises reporting the percentile values, Dupray does disclose percentile values calculated in the most likelihood estimator (location estimator 1344, Figs. 6(2), 8(4)) of the location center. Dupray also discloses that “the architecture of the location engine 139 may also be applied for providing a second embodiment of the mobile base station location subsystem 1508” (Dupray Col. 86, lines 48-50). Therefore, it is obvious that the mobile base station may comprise the location estimator calculate the percentile values over a pre-determined set of percentiles. Further, the mobile base station transmits additional mobile station location information to the location center. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing modify the method of Dupray to include the step of the mobile base station calculating the statistics comprises calculating percentile values over a pre-determined set of percentiles, and wherein reporting the statistics to the network entity comprises reporting the percentile values in order to improve the accuracy and reliability of tracking the mobile station and increase the extensibility and flexibility of the location center (Dupray “It is important to note that the architecture for the location center/gateway 142 and the location engine provided by the present disclosure is designed for extensibility and flexibility so that MS 140 location accuracy and reliability may be enhanced as further location data become available and as enhanced MS location techniques become available.” – Col. 52, lines 57-62).
Regarding claim 11 (Original), Dupray as modified above discloses:
The method of claim 1, wherein reporting the statistics to the network entity comprises reporting the statistics according to a statistics reporting configuration (Dupray data structure diagram, Figs. 9A-9B).
Regarding claim 12 (Currently Amended), Dupray discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
A method of wireless communication performed by a network entity (Dupray location center 142, Fig. 4), the method comprising:
receiving, from a base station (Dupray mobile base station 148, Fig. 4), statistics of one or more time-angle metrics (Dupray “The MBS 148 also includes a mobile station 140 for data communication with the gateway 142, via a BS 122. In particular, such data communication includes telemetering…MBS 148 estimates of the location of the target MS 140.” – Col. 38, lines 52-57) associated with a user equipment (UE) (Dupray mobile station 140, Fig. 4)and
calculating, based on the statistics, an estimated position of the UE (Dupray “The most likelihood estimator 1344 receives a collection of active or relevant location hypotheses from the hypothesis analyzer 1332 and uses these location hypotheses to determine one or more most likely estimates for the target MS 140.” – Col. 60, lines 35-29).
Uchiyama et al. discloses:
the statistics comprising parameters of a probability distribution function (Uchiyama et al. “For example, each eNB, instead of the NE 100, may generate the statistical information.” - ¶ [0305]; “As a first specific method, the statistical information generation unit 133 creates a probability density function of the CQI indicating the probability distribution of the CQI to generate the region statistical information on the CQI from the probability density function.” - ¶ [0043];)
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Uchiyama et al. into the invention of Dupray to yield the invention of claim 12. Both Dupray and Uchiyama et al. are considered analogous arts to the claimed invention as they both disclose calculating statistical information for positioning of user equipment in a wireless communication network. Dupray discloses the limitations of claim 12 outlined above. However, Dupray fails to explicitly disclose the statistics comprising parameters of a probability distribution function. This feature is disclosed by Uchiyama et al. where that statistical information includes a probability density function that can be generated by each eNB (Uchiyama et al. ¶ [0043], [0305]). The combination of Dupray and Uchiyama et al. would be obvious with a reasonable expectation of success to reduce the amount of control information transmitted and received by the base station in order to optimize the use of wireless resources in the wireless communication system (Uchiyama et al. ¶ [0006], [0289]).
Regarding claim 13 (Currently Amended), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 12, wherein the parameters of the probability distribution function (Dupray “Instead, the Parl FOM estimates the target MS's location by minimizing a joint probability of location related errors. In particular, such minimization may use the mean square error, and the location (x,y) at which minimization occurs is taken as the estimate of the target MS 140. In particular, the ambiguity function A(x,y) defines the error involved in a position determination for each point in a geolocation Cartesian coordinate system.” – Col. 84, lines 3-11)
Uchiyama et al. discloses:
the parameters of the probability distribution function comprise a mean (Uchiyama et al. “Furthermore, the statistical information is, for example, an average value in the probability distribution.” - ¶ [0046]), a mean vector, a standard deviation (Uchiyama et al. “The dispersion (that is, reliability) calculated in this manner indicates that, when the value is larger, variations in the region statistical information is small and the reliability of the region statistical information is higher.” - ¶ [0058]), a covariance matrix, a weight vector, a weight matrix, or combinations thereof.
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Uchiyama et al. into the invention of Dupray to yield the invention of claim 13. Both Dupray and Uchiyama et al. are considered analogous arts to the claimed invention as they both disclose calculating statistical information for positioning of user equipment in a wireless communication network. Dupray as modified above discloses the invention of claim 12. However, Dupray fails to explicitly disclose the statistics comprising parameters of a probability distribution function. This feature is disclosed by Uchiyama et al. where parameters of the probability distribution function comprise an average value and a dispersion (Uchiyama et al. ¶ [0046], [0058]). The combination of Dupray and Uchiyama et al. would be obvious with a reasonable expectation of success to reduce the amount of control information transmitted and received by the base station in order to optimize the use of wireless resources in the wireless communication system (Uchiyama et al. ¶ [0006], [0289]).
Regarding claim 14 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 12, wherein the statistics comprise a probability mass function (PMF) over a set of bins (Dupray “Binned_cells sort the cells of the area of interest by their probabilities into bins where each successive bin includes those cells whose confidence values are within a smaller (non-overlapping) range from that of any preceding bin.” – embodiment of program shown in Col. 61) and wherein (Dupray “The most likelihood estimator 1344 receives a collection of active or relevant location hypotheses from the hypothesis analyzer 1332 and uses these location hypotheses to determine one or more most likely estimates for the target MS 140.” – Col. 60, lines 35-29; the program iterates through the bins to determine the most likely location and the probability as determined by the range of bins – Col. 61).
Although Dupray does not explicitly disclose receiving the statistics comprises receiving a probability that a time-angle metric is within a bin range, Dupray does disclose the most likelihood estimator (location estimator 1344, Figs. 6(2), 8(4)) of the location engine calculates the probability mass distribution function and outputs a probability that a time-angle metric is within a bin range. Dupray also discloses that “the architecture of the location engine 139 may also be applied for providing a second embodiment of the mobile base station location subsystem 1508” (Dupray Col. 86, lines 48-50). Therefore, it is obvious that the mobile base station may comprise calculate the probability mass distribution function and outputting a probability that a time-angle metric is within a bin range. Further, the mobile base station transmits additional mobile station location information to the location center. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to modify the method of Dupray include the step of receiving the statistics comprising a probability that a time-angle metric is within a bin range, from the mobile base station comprising the location estimator in order to improve the accuracy and reliability of tracking the mobile station and increase the extensibility and flexibility of the location center (Dupray “It is important to note that the architecture for the location center/gateway 142 and the location engine provided by the present disclosure is designed for extensibility and flexibility so that MS 140 location accuracy and reliability may be enhanced as further location data become available and as enhanced MS location techniques become available.” – Col. 52, lines 57-62).
Regarding claim 15 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 12, wherein (Dupray “Binned_cells sort the cells of the area of interest by their probabilities into bins where each successive bin includes those cells whose confidence values are within a smaller (non-overlapping) range from that of any preceding bin. Further, assume there are, e.g., 100 bins BI wherein B1 has cells with confidences within the range [0, 0.1], and BI has cells with confidences within the range [(i - 1) * 0.01, i * 0.01]” – embodiment of program shown in Col. 61; where each bin represents a percentile of the probability).
Although Dupray does not explicitly disclose receiving the statistics comprises receiving percentile values over a pre-determined set of percentiles, Dupray does disclose percentile values calculated in the most likelihood estimator (location estimator 1344, Figs. 6(2), 8(4)) of the location center. Dupray also discloses that “the architecture of the location engine 139 may also be applied for providing a second embodiment of the mobile base station location subsystem 1508” (Dupray Col. 86, lines 48-50). Therefore, it is obvious that the mobile base station may comprise the location estimator and calculate the percentile values over a pre-determined set of percentiles. Further, the mobile base station transmits additional mobile station location information to the location center. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing modify the method of Dupray to include the step of the network entity receiving the statistics comprises receiving percentile values over a pre-determined set of percentiles in order to improve the accuracy and reliability of tracking the mobile station and increase the extensibility and flexibility of the location center (Dupray “It is important to note that the architecture for the location center/gateway 142 and the location engine provided by the present disclosure is designed for extensibility and flexibility so that MS 140 location accuracy and reliability may be enhanced as further location data become available and as enhanced MS location techniques become available.” – Col. 52, lines 57-62).
Regarding claim 17 (Previously presented), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 12, (Dupray “the invention of Lo provide further embodiments of wireless location estimators that may be used as First Order Models 1224. In particular, the '642 patent determines a corresponding probability density function (pdf) about each of a plurality of base stations in communication with the target MS 140.” – Col. 76, lines 14-20) of one time-angle metric or set of time-angle metrics more frequently than receiving probability distribution of another time-angle metric of set of time-angle metrics (Dupray “As a consequence of the MBS 148 being mobile, there are fundamental differences in the operation of an MBS in comparison to other types of BS's 122 (152). In particular, other types of base stations have fixed locations that are precisely determined and known by the location center, whereas a location of an MBS 148 may be known only approximately and thus may require repeated and frequent re-estimating.” – Col. 79, lines line 10-16; where the location center 142 receives location estimates from the mobile base station 148 more frequently than from the fixed location base stations 122, 152, Figs. 4-5).
Although Dupray does not explicitly disclose that the network entity receiving the statistics comprises receiving probability distribution of one time-angle metric or set of time-angle metrics more frequently that receiving probability distribution of another time-angle metric of set of time-angle metrics, Dupray does disclose that the mobile base station comprises first order models (Dupray “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45), and estimates and reports the location of the target mobile station to the location center (Dupray “The MBS 148 also includes a mobile station 140 for data communication with the gateway 142, via a BS 122. In particular, such data communication includes telemetering…MBS 148 estimates of the location of the target MS 140.” – Col. 38, lines 52-57). The first order models produce the location estimates that are reported to the location center. In particular, the first order model of Lo outputs a joint probability density function for each base station (Dupray Col. 76, lines 14-20). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to include the step of receiving the statistics comprises receiving probability distribution of one time-angle metric or set of time-angle metrics more frequently that receiving probability distribution of another time-angle metric of set of time-angle metrics into the method of Dupray in order to optimize the accuracy of the measurements while making efficient use of network resources.
Regarding claim 18 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 12, (Dupray “Essentially the Parl FOM combines angle of arrival related data and TDOA related data for obtaining an optimized estimate of the target MS 140. However, it appears that independent AOA and TDOA MS locations are not used in determining a resulting target MS location (e.g., without the need for projecting lines at angles of arrival or computing the intersection of hyperbolas defined by pairs of base stations). Instead, the Parl FOM estimates the target MS's location by minimizing a joint probability of location related errors.” – Col. 67, line 64 – Col. 68, line 5).
Although Dupray does not explicitly disclose that the network entity receiving the statistics comprises receiving marginal probability distributions separately from joint probability distributions, receiving the marginal probability distributions together with the joint probability distributions, or combinations thereof, Dupray does disclose that the mobile base station comprises first order models (Dupray “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45), and estimates and reports the location of the target mobile station to the location center (Dupray “The MBS 148 also includes a mobile station 140 for data communication with the gateway 142, via a BS 122. In particular, such data communication includes telemetering…MBS 148 estimates of the location of the target MS 140.” – Col. 38, lines 52-57). The first order models produce the location estimates that are reported to the location center. In particular, the first order model of Parl outputs a joint probability density function based on AOA and TDOA data (Dupray Col. 76, lines 14-20). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to include the step of receiving the statistics comprises receiving marginal probability distributions separately from joint probability distributions, receiving the marginal probability distributions together with the joint probability distributions, or combinations thereof into in the method of Dupray to provide measurements about the mobile station to a hypothesis analyzer in the location center in order to resolve conflicts between hypotheses in a current activation for locating the target mobile station (Dupray Col. 78, lines 26-27).
Regarding claim 19 (Original), Dupray as modified above discloses:
The method of claim 12, wherein receiving the statistics comprises receiving the statistics according to a statistics reporting configuration (Dupray data structure diagram, Figs. 9A-9B).
Regarding claim 20 (Previously presented), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 12, wherein (Dupray “the invention of “Lo provide further embodiments of wireless location estimators that may be used as First Order Models 1224. In particular, the '642 patent determines a corresponding probability density function (pdf) about each of a plurality of base stations in communication with the target MS 140.” – Col. 76, lines 14-20), and calculates the estimated position of the UE based on the plurality of probability distributions (Dupray “Subsequently, a most likely location estimation is determined from a joint probability density function of the individual base station probability density functions.” – Col. 76, lines 25-28).
Although Dupray does not explicitly disclose that the network entity receives a plurality of probability distributions of one or more time-angle metrics associated with the UE from a plurality of base stations, and calculates the estimated position of the UE based on the plurality of probability distributions, Dupray does disclose that the mobile base station comprises first order models (Dupray “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45), and estimates and reports the location of the target mobile station to the location center (Dupray “The MBS 148 also includes a mobile station 140 for data communication with the gateway 142, via a BS 122. In particular, such data communication includes telemetering…MBS 148 estimates of the location of the target MS 140.” – Col. 38, lines 52-57). The first order models produce the location estimates that are reported to the location center. In particular, the first order model of Lo outputs a probability density function for each base station (Dupray Col. 76, lines 14-20). Further, the location center comprises a most likelihood estimator (location estimator 1344, Figs. 6(2), 8(4)) in the location engine that “receives a collection of active or relevant location hypotheses from the hypothesis analyzer 1332 and uses these location hypotheses to determine one or more most likely estimates for the target MS 140.” (Dupray Col. 60, lines 25-29). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to include the step of the network entity receiving a plurality of probability distributions of one or more time-angle metrics associated with the UE from a plurality of base stations, and calculating the estimated position of the UE based on the plurality of probability distributions into in the method of Dupray in order to improve the accuracy and reliability of tracking the mobile station and increase the extensibility and flexibility of the location center (Dupray “It is important to note that the architecture for the location center/gateway 142 and the location engine provided by the present disclosure is designed for extensibility and flexibility so that MS 140 location accuracy and reliability may be enhanced as further location data become available and as enhanced MS location techniques become available.” – Col. 52, lines 57-62).
Regarding claim 21 (Currently Amended), Dupray discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
A base station (Dupray mobile base station 148, Fig. 4), comprising:
a memory (Dupray signal processing database 26, Fig. 14; “Note that the MBS signal processing subsystem 1541, in one embodiment, is similar to the signal processing subsystem 1220 of the location center 142.” – Col. 83, lines 10-12);
at least one transceiver (Dupray transceiver 1512, Fig. 11(2); and
at least one processor (Dupray signal processing subsystem 1541, Fig. 11(1)) communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to:
calculate statistics of one or more time-angle metrics (Dupray “the location models provided may include not only the radius-radius/TOA and TDOA techniques but also adaptive techniques such as artificial neural net techniques…, and angle or direction of arrival techniques” – Col. 17, lines 45-50; “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45) based on a signal received from a user equipment (UE) (Dupray mobile station MS 140, Fig. 4)and
report the statistics to a network entity (Dupray “Such mobile location units may provide greater target MS location accuracy by, for example, homing in on the target MS and by transmitting additional MS location information to the location center 142.” – Col. 78, lines 35-38).
Uchiyama et al. discloses:
the statistics comprising parameters of a probability distribution function (Uchiyama et al. “For example, each eNB, instead of the NE 100, may generate the statistical information.” - ¶ [0305]; “As a first specific method, the statistical information generation unit 133 creates a probability density function of the CQI indicating the probability distribution of the CQI to generate the region statistical information on the CQI from the probability density function.” - ¶ [0043])
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Uchiyama et al. into the invention of Dupray to yield the invention of claim 21. Both Dupray and Uchiyama et al. are considered analogous arts to the claimed invention as they both disclose calculating statistical information for positioning of user equipment in a wireless communication network. Dupray discloses the limitations of claim 21 outlined above. However, Dupray fails to explicitly disclose the statistics comprising parameters of a probability distribution function. This feature is disclosed by Uchiyama et al. where that statistical information includes a probability density function that can be generated by each eNB (Uchiyama et al. ¶ [0043], [0305]). The combination of Dupray and Uchiyama et al. would be obvious with a reasonable expectation of success to reduce the amount of control information transmitted and received by the base station in order to optimize the use of wireless resources in the wireless communication system (Uchiyama et al. ¶ [0006], [0289]).
Regarding claim 22 (Original), the same cited section and rationale as corresponding method claim 2 is applied.
Regarding claim 23 (Currently Amended), the same cited section and rationale as corresponding method claim 3 is applied.
Regarding claim 24 (Original), the same cited section and rationale as corresponding method claim 4 is applied.
Regarding claim 25 (Original), the same cited section and rationale as corresponding method claim 25 is applied.
Regarding claim 26 (Original), the same cited section and rationale as corresponding method claim 6 is applied.
Regarding claim 28 (Original), the same cited section and rationale as corresponding method claim 8 is applied.
Regarding claim 29 (Original), the same cited section and rationale as corresponding method claim 9 is applied.
Regarding claim 31 (Original), the same cited section and rationale as corresponding method claim 11 is applied.
Claim(s) 10, 16 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dupray (US 8,135,413 B2, previously relied upon by the examiner) in view of Uchiyama et al. (EP 3,018,923 A1, newly cited by the examiner) as applied to claim 1 above, and further in view of Karr et al. (US 7,764,231 B1, previously relied upon by the examiner).
Regarding claim 10 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 1
Karr et al. discloses:
wherein calculating the statistics comprises using a neural network to calculate the statistics, and wherein reporting the statistics to the network entity comprises reporting weights of the neural network (Karr et al. "It is as an aspect of the present invention to use an adaptive neural network architecture which has the ability to explore the parameter or matrix weight space corresponding to a ANN for determining new configurations of weights that reduce an objective or error function indicating the error in the output of the ANN over some aggregate set of input data ensembles." - Col. 67, lines 11-17).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Karr et al. into the invention of Dupray as modified above to yield the invention of claim 10. Dupray, Uchiyama et al. and Karr et al. are considered analogous arts to the claimed invention as they disclose estimating the location of mobile devices in a wireless communication network. Dupray as modified above discloses the invention of claim 1. However, Dupray fails to explicitly disclose calculating the statistics comprises using a neural network to calculate the statistics, and wherein reporting the statistics to the network entity comprises reporting weights of the neural network. This feature is disclosed by Karr et al. where “an adaptive neural network architecture which has the ability to explore the parameter or matrix weight space corresponding to a ANN for determining new configurations of weights that reduce an objective or error function indicating the error in the output of the ANN over some aggregate set of input data ensembles” (Karr et al. Col. 67, lines 11-17). The combination of Dupray, Uchiyama et al. and Karr et al. would be obvious with a reasonable expectation of success to reduce the amount of control information transmitted and received by the base station in order to optimize the use of wireless resources in the wireless communication system (Uchiyama et al. ¶ [0006], [0289]) and to adjust the matrix of weights for the ANNs so that very good, near optimal ANN configurations may be found efficiently (Karr et al. Col. 72, lines 8-10).
Regarding claim 16 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The method of claim 12,
Karr et al. discloses:
wherein receiving the statistics comprises receiving weights of a neural network used to calculate the statistics (Karr et al. "It is as an aspect of the present invention to use an adaptive neural network architecture which has the ability to explore the parameter or matrix weight space corresponding to a ANN for determining new configurations of weights that reduce an objective or error function indicating the error in the output of the ANN over some aggregate set of input data ensembles." - Col. 67, lines 11-17).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Karr et al. into the invention of Dupray to yield the invention of claim 16. Dupray, Uchiyama et al. and Karr et al. are considered analogous arts to the claimed invention as they disclose estimating the location of mobile devices in a wireless communication network. Dupray as modified above discloses the invention of claim 12. However, Dupray fails to explicitly disclose wherein receiving the statistics comprises receiving weights of a neural network used to calculate the statistics. This feature is disclosed by Karr et al. where “an adaptive neural network architecture which has the ability to explore the parameter or matrix weight space corresponding to a ANN for determining new configurations of weights that reduce an objective or error function indicating the error in the output of the ANN over some aggregate set of input data ensembles” (Karr et al. Col. 67, lines 11-17). The combination of Dupray, Uchiyama et al. and Karr et al. would be obvious with a reasonable expectation of success to reduce the amount of control information transmitted and received by the base station in order to optimize the use of wireless resources in the wireless communication system (Uchiyama et al. ¶ [0006], [0289]) and to adjust the matrix of weights for the ANNs so that very good, near optimal ANN configurations may be found efficiently (Karr et al. Col. 72, lines 8-10).
Regarding claim 30 (Original), the same cited section and rationale as corresponding method claim 10 is applied.
Claim(s) 32-35 and 37-40 remain rejected under 35 U.S.C. 103 as being unpatentable over Dupray (US 8,135,413 B2, previously relied upon by the examiner) in view of Marshall et al. (US 2014/0221005 A1, previously relied upon by the examiner) and Uchiyama et al. (EP 3,018,923 A1, newly cited by the examiner).
Regarding claim 32 (Currently Amended), Dupray discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
A network entity (Dupray location center 142, Fig. 4), comprising:
a memory (Dupray location information databases 1232, Fig. 4);
at least one processor (Dupray location center 142 performs MS location processing, Figs. 4-5; Col. 50, lines 60-61) communicatively coupled to the memory and (Dupray the location center 142 obviously comprises at least one processor connected to the location information data base 1232 and the location center 142 is communicatively coupled to the mobile switch center MSC 112, Figs. 4-5), the at least one processor configured to:
receive, (Dupray mobile base station 148, Fig. 4), statistics of one or more time- angle metrics (Dupray “the location models provided may include not only the radius-radius/TOA and TDOA techniques but also adaptive techniques such as artificial neural net techniques…, and angle or direction of arrival techniques” – Col. 17, lines 45-50; “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45) associated with a user equipment (UE) (Dupray mobile station MS 140, Fig. 4), and
calculate, based on the statistics, an estimated position of the UE (Dupray “the location models provided may include not only the radius-radius/TOA and TDOA techniques but also adaptive techniques such as artificial neural net techniques…, and angle or direction of arrival techniques” – Col. 17, lines 45-50; “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45).
Marshall et al. discloses:
at least one transceiver (Marshall et al. " Communications interface 1290 may include a variety of wired and wireless connections that support wired transmission and/or reception and, if desired, may additionally or alternatively support transmission and reception of one or more signals over one or more types of wireless communication networks" - ¶ [0169])
Uchiyama et al. discloses:
the statistics comprising parameters of a probability distribution function (Uchiyama et al. “For example, each eNB, instead of the NE 100, may generate the statistical information.” - ¶ [0305]; “As a first specific method, the statistical information generation unit 133 creates a probability density function of the CQI indicating the probability distribution of the CQI to generate the region statistical information on the CQI from the probability density function.” - ¶ [0043])
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Marshall et al. and Uchiyama et al. into the invention of Dupray to yield the invention of claim 32. Dupray, Marshall et al. and Uchiyama et al. are considered analogous arts to the claimed invention as they disclose determining the location of mobile devices based on measurements of times of signal arrivals in wireless communication networks. Dupray discloses a network entity comprising a memory, at least one processor, and receives and calculates an estimated position of the UE. However, Dupray fails to explicitly disclose the location center comprises a transceiver and the statistics comprise parameters of a probability distribution function. These features are disclosed by Marshall et al. where the server 150 comprises a communications interface 1290 that may include a variety of wired and wireless connections (Marshall et al. ¶ [0169]), and Uchiyama et al. where the statistical information includes a probability density function that can be generated by each eNB (Uchiyama et al. ¶ [0043], [0305]). The combination of Dupray, Marshall et al. and Uchiyama et al. would be obvious with a reasonable expectation of success to provide the network entity with wireless connectivity in order to increase the adaptability and flexibility of the network entity in the wireless network and reduce the amount of control information transmitted and received by the base station in order to optimize the use of wireless resources in the wireless communication system (Uchiyama et al. ¶ [0006], [0289]).
Regarding claim 33 (Currently Amended), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The network entity of claim 32, wherein the parameters of the probability distribution function (Dupray “Instead, the Parl FOM estimates the target MS's location by minimizing a joint probability of location related errors. In particular, such minimization may use the mean square error, and the location (x,y) at which minimization occurs is taken as the estimate of the target MS 140. In particular, the ambiguity function A(x,y) defines the error involved in a position determination for each point in a geolocation Cartesian coordinate system.” – Col. 84, lines 3-11)
Uchiyama et al. discloses:
the parameters of the probability distribution function comprise a mean (Uchiyama et al. “Furthermore, the statistical information is, for example, an average value in the probability distribution.” - ¶ [0046]), a mean vector, a standard deviation (Uchiyama et al. “The dispersion (that is, reliability) calculated in this manner indicates that, when the value is larger, variations in the region statistical information is small and the reliability of the region statistical information is higher.” - ¶ [0058]), a covariance matrix, a weight vector, a weight matrix, or combinations thereof.
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Uchiyama et al. into the invention of Dupray to yield the invention of claim 33. Dupray, Marshall et al. and Uchiyama et al. are considered analogous arts to the claimed invention as they disclose determining the location of mobile devices based on measurements of times of signal arrivals in wireless communication networks. Dupray as modified above discloses the invention of claim 32. However, Dupray fails to explicitly disclose the statistics comprising parameters of a probability distribution function. This feature is disclosed by Uchiyama et al. where parameters of the probability distribution function comprise an average value and a dispersion (Uchiyama et al. ¶ [0046], [0058]). The combination of Dupray, Marshall et al. and Uchiyama et al. would be obvious with a reasonable expectation of success to provide the network entity with wireless connectivity in order to increase the adaptability and flexibility of the network entity in the wireless network and reduce the amount of control information transmitted and received by the base station in order to optimize the use of wireless resources in the wireless communication system (Uchiyama et al. ¶ [0006], [0289]).
Regarding claim 34 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The network entity of claim 32, wherein the statistics comprise a probability mass function (PMF) over a set of bins (Dupray “Binned_cells sort the cells of the area of interest by their probabilities into bins where each successive bin includes those cells whose confidence values are within a smaller (non-overlapping) range from that of any preceding bin.” – embodiment of program shown in Col. 61) and wherein, to receive the statistics, the at least one processor is configured to receive a probability that a time-angle metric is within a bin range (Dupray “The most likelihood estimator 1344 receives a collection of active or relevant location hypotheses from the hypothesis analyzer 1332 and uses these location hypotheses to determine one or more most likely estimates for the target MS 140.” – Col. 60, lines 35-29; the program iterates through the bins to determine the most likely location estimate and the probability as determined by the range of bins the program iterated through – Col. 61).
Although Dupray does not explicitly disclose receiving the statistics comprises receiving a probability that a time-angle metric is within a bin range, Dupray does disclose the most likelihood estimator (location estimator 1344, Figs. 6(2), 8(4)) of the location engine calculates the probability mass distribution function and outputs a probability that a time-angle metric is within a bin range. Dupray also discloses that “the architecture of the location engine 139 may also be applied for providing a second embodiment of the mobile base station location subsystem 1508” (Dupray Col. 86, lines 48-50). Therefore, it is obvious that the mobile base station may comprise the location estimator and calculate the probability mass distribution function and outputting a probability that a time-angle metric is within a bin range. Further, the mobile base station transmits additional mobile station location information to the location center. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to modify the method of Dupray include the step of receiving the statistics comprising a probability that a time-angle metric is within a bin range, from the mobile base station comprising the location estimator in order to improve the accuracy and reliability of tracking the mobile station and increase the extensibility and flexibility of the location center (Dupray “It is important to note that the architecture for the location center/gateway 142 and the location engine provided by the present disclosure is designed for extensibility and flexibility so that MS 140 location accuracy and reliability may be enhanced as further location data become available and as enhanced MS location techniques become available.” – Col. 52, lines 57-62).
Regarding claim 35 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The network entity of claim 32, (Dupray “Binned_cells sort the cells of the area of interest by their probabilities into bins where each successive bin includes those cells whose confidence values are within a smaller (non-overlapping) range from that of any preceding bin. Further, assume there are, e.g., 100 bins BI wherein B1 has cells with confidences within the range [0, 0.1], and BI has cells with confidences within the range [(i - 1) * 0.01, i * 0.01]” – embodiment of program shown in Col. 61; where each bin represents a percentile of the probability).
Although Dupray does not explicitly disclose receiving the statistics comprises receiving percentile values over a pre-determined set of percentiles, Dupray does disclose percentile values calculated in the most likelihood estimator (location estimator 1344, Figs. 6(2), 8(4)) of the location center. Dupray also discloses that “the architecture of the location engine 139 may also be applied for providing a second embodiment of the mobile base station location subsystem 1508” (Dupray Col. 86, lines 48-50). Therefore, it is obvious that the mobile base station may comprise the location estimator and calculate the percentile values over a pre-determined set of percentiles. Further, the mobile base station transmits additional mobile station location information to the location center. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing modify the method of Dupray to include the step of the network entity receiving the statistics comprises receiving percentile values over a pre-determined set of percentiles in order to improve the accuracy and reliability of tracking the mobile station and increase the extensibility and flexibility of the location center (Dupray “It is important to note that the architecture for the location center/gateway 142 and the location engine provided by the present disclosure is designed for extensibility and flexibility so that MS 140 location accuracy and reliability may be enhanced as further location data become available and as enhanced MS location techniques become available.” – Col. 52, lines 57-62).
Regarding claim 37 (Previously presented), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The network entity of claim 32, (Dupray “the invention of Lo provide further embodiments of wireless location estimators that may be used as First Order Models 1224. In particular, the '642 patent determines a corresponding probability density function (pdf) about each of a plurality of base stations in communication with the target MS 140.” – Col. 76, lines 14-20) of one time-angle metric or set of time-angle metrics more frequently than receiving probability distribution of another time-angle metric of set of time-angle metrics (Dupray “As a consequence of the MBS 148 being mobile, there are fundamental differences in the operation of an MBS in comparison to other types of BS's 122 (152). In particular, other types of base stations have fixed locations that are precisely determined and known by the location center, whereas a location of an MBS 148 may be known only approximately and thus may require repeated and frequent re-estimating.” – Col. 79, lines line 10-16; where the location center 142 receives location estimates from the mobile base station 148 more frequently than from the fixed location base stations 122, 152, Figs. 4-5).
Although Dupray does not explicitly disclose that the network entity receiving the statistics comprises receiving probability distribution of one time-angle metric or set of time-angle metrics more frequently that receiving probability distribution of another time-angle metric of set of time-angle metrics, Dupray does disclose that the mobile base station comprises first order models (Dupray “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45), and estimates and reports the location of the target mobile station to the location center (Dupray “The MBS 148 also includes a mobile station 140 for data communication with the gateway 142, via a BS 122. In particular, such data communication includes telemetering…MBS 148 estimates of the location of the target MS 140.” – Col. 38, lines 52-57). The first order models produce the location estimates that are reported to the location center. In particular, the first order model of Lo outputs a joint probability density function for each base station (Dupray Col. 76, lines 14-20). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to include the step of receiving the statistics comprises receiving probability distribution of one time-angle metric or set of time-angle metrics more frequently that receiving probability distribution of another time-angle metric of set of time-angle metrics into the method of Dupray in order to optimize the accuracy of the measurements while making efficient use of network resources.
Regarding claim 38 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The network entity of claim 32, (Dupray “Essentially the Parl FOM combines angle of arrival related data and TDOA related data for obtaining an optimized estimate of the target MS 140. However, it appears that independent AOA and TDOA MS locations are not used in determining a resulting target MS location (e.g., without the need for projecting lines at angles of arrival or computing the intersection of hyperbolas defined by pairs of base stations). Instead, the Parl FOM estimates the target MS's location by minimizing a joint probability of location related errors.” – Col. 67, line 64 – Col. 68, line 5).
Although Dupray does not explicitly disclose that the network entity receiving the statistics comprises receiving marginal probability distributions separately from joint probability distributions, receiving the marginal probability distributions together with the joint probability distributions, or combinations thereof, Dupray does disclose that the mobile base station comprises first order models (Dupray “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45), and estimates and reports the location of the target mobile station to the location center (Dupray “The MBS 148 also includes a mobile station 140 for data communication with the gateway 142, via a BS 122. In particular, such data communication includes telemetering…MBS 148 estimates of the location of the target MS 140.” – Col. 38, lines 52-57). The first order models produce the location estimates that are reported to the location center. In particular, the first order model of Parl outputs a joint probability density function based on AOA and TDOA data (Dupray Col. 76, lines 14-20). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to include the step of receiving the statistics comprises receiving marginal probability distributions separately from joint probability distributions, receiving the marginal probability distributions together with the joint probability distributions, or combinations thereof into in the method of Dupray to provide measurements about the mobile station to a hypothesis analyzer in the location center in order to resolve conflicts between hypotheses in a current activation for locating the target mobile station (Dupray Col. 78, lines 26-27).
Regarding claim 39 (Original), Dupray as modified above discloses:
The network entity of claim 32, wherein, to receive the statistics, the at least one processor is configured to receive the statistics according to a statistics reporting configuration (Dupray data structure diagram, Figs. 9A-9B).
Regarding claim 40 (Previously presented), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The network entity of claim 32, wherein the at least one processor is further configured to receive a plurality of probability distributions of the one or more time-angle metrics associated with the UE from a plurality of base stations (Dupray “the invention of “Lo provide further embodiments of wireless location estimators that may be used as First Order Models 1224. In particular, the '642 patent determines a corresponding probability density function (pdf) about each of a plurality of base stations in communication with the target MS 140.” – Col. 76, lines 14-20) and to calculate the estimated position of the UE based on the plurality of probability distributions (Dupray “Subsequently, a most likely location estimation is determined from a joint probability density function of the individual base station probability density functions.” – Col. 76, lines 25-28).
Although Dupray does not explicitly disclose that the network entity receives a plurality of probability distributions of one or more time-angle metrics associated with the UE from a plurality of base stations, and calculates the estimated position of the UE based on the plurality of probability distributions, Dupray does disclose that the mobile base station comprises first order models (Dupray “integration of new FOMs, wherein such integration maybe at a central site or at a mobile unit” – Col. 5, lines 44-45), and estimates and reports the location of the target mobile station to the location center (Dupray “The MBS 148 also includes a mobile station 140 for data communication with the gateway 142, via a BS 122. In particular, such data communication includes telemetering…MBS 148 estimates of the location of the target MS 140.” – Col. 38, lines 52-57). The first order models produce the location estimates that are reported to the location center. In particular, the first order model of Lo outputs a probability density function for each base station (Dupray Col. 76, lines 14-20). Further, the location center comprises a most likelihood estimator (location estimator 1344, Figs. 6(2), 8(4)) in the location engine that “receives a collection of active or relevant location hypotheses from the hypothesis analyzer 1332 and uses these location hypotheses to determine one or more most likely estimates for the target MS 140.” (Dupray Col. 60, lines 25-29). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to include the step of the network entity receiving a plurality of probability distributions of one or more time-angle metrics associated with the UE from a plurality of base stations, and calculating the estimated position of the UE based on the plurality of probability distributions into in the method of Dupray in order to improve the accuracy and reliability of tracking the mobile station and increase the extensibility and flexibility of the location center (Dupray “It is important to note that the architecture for the location center/gateway 142 and the location engine provided by the present disclosure is designed for extensibility and flexibility so that MS 140 location accuracy and reliability may be enhanced as further location data become available and as enhanced MS location techniques become available.” – Col. 52, lines 57-62).
Claim(s) 36 remains rejected under 35 U.S.C. 103 as being unpatentable over Dupray (US 8,135,413 B2, previously relied upon by the examiner) in view of Marshall et al. (US 2014/0221005 A1, previously relied upon by the examiner) and Uchiyama et al. (EP 3,018,923 A1, newly cited by the examiner), as applied to claim 32 above, and further in view of Karr et al. (US 7,764,231 B1, previously relied upon by the examiner).
Regarding claim 36 (Original), Dupray as modified above discloses:
[Note: what is not explicitly taught by Dupray has been struck-through]
The network entity of claim 32
Karr et al. discloses:
wherein, to receive the statistics, the at least one processor is configured to receive weights of a neural network used to calculate the statistics (Karr et al. "It is as an aspect of the present invention to use an adaptive neural network architecture which has the ability to explore the parameter or matrix weight space corresponding to a ANN for determining new configurations of weights that reduce an objective or error function indicating the error in the output of the ANN over some aggregate set of input data ensembles." - Col. 67, lines 11-17).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Karr et al. into the invention of Dupray as modified above to yield the invention of claim 36. Dupray, Marshall et al., Uchiyama et al. and Karr et al. are considered analogous arts to the claimed invention as they disclose estimating the location of mobile devices in a wireless communication network. Dupray as modified above discloses the invention of claim 32. However, Dupray fails to explicitly disclose wherein receiving the statistics comprises receiving weights of a neural network used to calculate the statistics. This feature is disclosed by Karr et al. where “an adaptive neural network architecture which has the ability to explore the parameter or matrix weight space corresponding to a ANN for determining new configurations of weights that reduce an objective or error function indicating the error in the output of the ANN over some aggregate set of input data ensembles” (Karr et al. Col. 67, lines 11-17). The combination of Dupray, Marshall et al., Uchiyama et al. and Karr et al. would be obvious with a reasonable expectation of success to provide the network entity with wireless connectivity in order to increase the adaptability and flexibility of the network entity in the wireless network, reduce the amount of control information transmitted and received by the base station in order to optimize the use of wireless resources in the wireless communication system (Uchiyama et al. ¶ [0006], [0289]), and adjust the matrix of weights for the ANNs so that very good, near optimal ANN configurations may be found efficiently (Karr et al. Col. 72, lines 8-10).
Conclusion
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NAOMI M. WOLFORD
Examiner
Art Unit 3648
/N.M.W./ Examiner, Art Unit 3648
4 MAR 2026
/RESHA DESAI/ Supervisory Patent Examiner, Art Unit 3648