DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
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.
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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8 JUN 2026 has been considered by the examiner.
Response to Amendment
Applicant's amendment filed on 8 JUN 2026 has been entered. Claims 3, 5, 17, 23, and 37-38 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 filed 8 JUN 2026 have been fully considered but they are not persuasive.
Regarding the Examiner’s rejection of claims 1-6, 8-26 and 28-40 under 35 U.S.C. 101 as being directed to non-statutory subject matter, the applicant argues that “Extracting and processing “time-angle metric based on a signal received from a UE” relies on the processing of physical RF signals, which is a concrete technological signal-processing step utilized in wireless networks, not a disembodied mathematical concept.” (applicant’s remarks, p. 9)
Applicant argues that the claims are directed to an improvement in telecommunications technology (applicant’s remarks, p. 9). The specification discloses “To overcome the technical disadvantages of conventional systems and methods described above, mechanisms by which the bandwidth used by a user equipment (UE) for positioning reference signal (PRS) can be dynamically adjusted, e.g., response to environmental conditions, are presented.” (applicant’s specification ¶ [0020]) However, the claim does not recite the feature of dynamically adjusting the PRS in response to receiving the reported statistics. The claim does not achieve the desired outcome of adjusting the PRS. Thus, the claim does not reflect the disclosed improvement and does not integrate the abstract idea into a practical application. Therefore, applicant’s argument on this issue is not persuasive.
Applicant’s arguments, see p. 10-11, filed 8 JUN 2026, with respect to the rejection(s) of claim(s) 1-6, 8-26 and 28-40 under 35 U.S.C. 103 as being unpatentable over Dupray in view of Uchiyama have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Sillasto et al. (US 2005/0255854 A1).
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. machine)
(claim 32) … a network entity (i.e. machine)
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 station 102 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 1) … wireless communication performed by a base station (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 1, line 6) … reporting the statistics to a network entity (this step is generally being applied on a computer since the step is performed by a generic base station/processor which is a general purpose computer, thus failing to impose a meaningful limit on the judicial exception)
(claim 12, line 1) … wireless communication performed by a network entity (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 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… (this step is generally being applied on a computer since the step is performed by a generic network entity/processor which is a general purpose computer, thus failing to impose a meaningful limit on the judicial exception)
(claim 21, lines 1-5) … a base station comprising: a memory; at least one transceiver; and 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 (this step is generally being applied on a computer since the step is performed by a generic base station/processor which is a general purpose computer, thus failing to impose a meaningful limit on the judicial exception)
(claim 32, lines 1-5) … a network entity, comprising: a memory, at least one transceiver; and 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 (this step is generally being applied on a computer since the step is performed by a generic base station/processor which is a general purpose computer, 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 1) … wireless communication performed by a base station (merely using a computer/generic computer components as a tool to perform an abstract idea, thus failing amount to significantly more than the abstract idea)
(claim 1, line 6) … reporting the statistics to a network entity (this step is generally being applied on a computer since the step is performed by a generic base station/processor which is a general purpose computer, thus failing to impose a meaningful limit on the judicial exception, thus failing to amount to significantly more than the judicial exception)
(claim 12, line 1) … wireless communication performed by a network entity (merely using a computer/generic computer components as a tool to perform an abstract idea, thus failing to amount to significantly more than 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… (this step is generally being applied on a computer since the step is performed by a generic network entity/processor which is a general purpose computer, thus failing to amount to significantly more than the judicial exception)
(claim 21, lines 1-5) … a base station comprising: a memory; at least one transceiver; and 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 the judicial exception)
(claim 21, line 9) … report the statistics to a network entity (this step is generally being applied on a computer since the step is performed by a generic base station/processor which is a general purpose computer, thus failing to amount to significantly more than judicial exception)
(claim 32, lines 1-5) … a network entity, comprising: a memory, at least one transceiver; and 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 (this step is generally being applied on a computer since the step is performed by a generic base station/processor which is a general purpose computer, 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
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) 1-3, 6, 12-13, 18, 20-23, 26, 32-33, 38 and 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sillasto et al. (US 2005/0255854 A1, newly cited by the examiner) in view of Uchiyama et al. (EP 3,018,923 A1, previously relied upon by the examiner).
Regarding claim 1 (Previously Presented), Sillasto et al. discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
A method of wireless communication (Sillasto et al. Node B, Fig. 2), the method comprising:
calculating statistics of one or more time-angle metrics (Sillasto et al. “a distance estimate calculation obtained from a set of measurement pairs of the round trip time (RTT) and Rx-Tx time difference (TD)” - ¶ [0040]) based on a signal received from a user equipment (UE) (Sillasto et al. UE, Figs. 2-3), the statistics comprising parameters of a probability distribution function (Sillasto et al. “By premeasuring data of this kind, a probability density function of the double range measurement area can be determined…” - ¶ [0042]; “Thus, the CI+RTT location algorithm provides as outputs: location calculation results being a set of parameters identifying the coordinates of a location estimate and the parameters of a certain confidence region (a geographical region where the exact UE location is estimated to be with a certain probability)” - ¶ [0083]-[0084]); and
Uchiyama et al. discloses:
A method of wireless communication performed by a base station (Uchiyama et al. eNB 200, Fig. 8), the method comprising:
calculating statistics based on a signal received from a user equipment (Uchiyama et al. UE 300, Fig. 9), the statistics comprising a probability density function (Uchiyama et al. “the statistical information generation unit 133 creates a probability density function of the CQI…” - ¶ [0043]; “each eNB, instead of the NE 100, may generate the statistical information.” - ¶ [0305])
reporting the statistics to a network entity (Uchiyama et al. “the eNB of a macro cell may generate both the macro cell statistical information and the small cell statistical information, and may provide the small cell statistical information for the eNB of the small cell.” - ¶ [0305]; “the NE may be implemented in the eNB representing the plurality of eNBs. In this case, the representative eNB may function as the NE for other eNBs while function as the NE for its own apparatus.” - ¶ [0308]).
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 Sillasto et al. to yield the invention of claim 1 above. Both Sillasto et al. and Uchiyama et al. are considered analogous arts to the claimed invention as they both disclose methods of positioning a user equipment in a wireless communication network and generating probability density functions based on signals received from a UE. Sillasto et al. discloses the limitations of claim 1 outlined above. However, Sillasto et al. fails to explicitly disclose the base station performs the calculation of the statistics and reports the statistics to a network entity. This feature is disclosed by Uchiyama et al. where “each eNB, instead of the NE 100, may generate the statistical information.” (Uchiyama et al. ¶ [0305]) and the eNB can generate and report statistical information to other eNBs, which can also function as network entities (Uchiyama et al. ¶ [0305], [0308]). The combination of Sillasto et al. 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), Sillasto et al. 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) (Sillasto et al. “a distance estimate calculation obtained from a set of measurement pairs of the round trip time (RTT) and Rx-Tx time difference (TD)” - ¶ [0040]), an angle of arrival (AoA), a zenith of arrival (ZoA), UL transmit-to-receive time difference, DL transmit-to-receive time difference, or combinations thereof.
Regarding claim 3 (Currently Amended), Sillasto et al. as modified above discloses:
The method of claim 1, wherein the parameters of the probability distribution function comprise a mean, a mean vector, a standard deviation, a covariance matrix, a weight, a weight vector, a weight matrix, or combinations thereof (Sillasto et al. “The weighted mass centre method uses a weighted average of the site’s coordinates.” - ¶ [0050]).
Regarding claim 6 (Original), Sillasto et al. as modified above discloses:
The method of claim 1, wherein reporting the statistics comprises reporting a marginal probability distribution of one time-angle metric, a joint probability distribution (Sillasto et al. “The PDFs of distance conditioned by the observation and of orientation conditioned by the observations are then combined with each other (block 11) to determine joint PDF of UE distance and orientation within the cell…” - ¶ [0081]) of a plurality of time-angle metrics, or combinations thereof.
Regarding claim 12 (Previously Presented), Sillasto et al. discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
A method of wireless communication performed by
(Sillasto et al. Node B, Fig. 2), statistics of one or more time-angle metrics (Sillasto et al. “a distance estimate calculation obtained from a set of measurement pairs of the round trip time (RTT) and Rx-Tx time difference (TD)” - ¶ [0040]) associated with a user equipment (UE) (Sillasto et al. UE, Figs. 2-3), the statistics comprising parameters of a probability distribution function (Sillasto et al. “By premeasuring data of this kind, a probability density function of the double range measurement area can be determined…” - ¶ [0042]); and
calculating, based on the statistics, an estimated position of the UE (Sillasto et al. “Thus, the CI+RTT location algorithm provides as outputs: location calculation results being a set of parameters identifying the coordinates of a location estimate and the parameters of a certain confidence region (a geographical region where the exact UE location is estimated to be with a certain probability)” - ¶ [0083]-[0084]).
Uchiyama et al. discloses:
A method of wireless communication performed by a network entity (Uchiyama et al. “the eNB of a macro cell may generate both the macro cell statistical information and the small cell statistical information, and may provide the small cell statistical information for the eNB of the small cell.” - ¶ [0305]; “the representative eNB may function as the NE for other eNBs, while functioning as the NE for its own apparatus.” - ¶ [0308]), the method comprising:
receiving, from a base station (Uchiyama et al. eNB 200, Fig. 8; “each eNB, instead of the NE 100, may generate the statistical information.” - ¶ [0305]), statistics associated with a user equipment (UE) (Uchiyama et al. UE 300, Figs. 1, 9), the statistics comprising parameters of a probability distribution function (Uchiyama et al. “the statistical information generation unit 133 creates a probability density function of the CQI…” - ¶ [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 Sillasto et al. to yield the invention of claim 12 above. Both Sillasto et al. and Uchiyama et al. are considered analogous arts to the claimed invention as they both disclose methods of positioning a user equipment in a wireless communication network and generating probability density functions based on signals received from a UE. Sillasto et al. discloses the limitations of claim 12 outlined above. However, Sillasto et al. fails to explicitly disclose the network entity receives the statistics from the base station. This feature is disclosed by Uchiyama et al. where “each eNB, instead of the NE 100, may generate the statistical information.” (Uchiyama et al. ¶ [0305]) and the eNB can generate and report statistical information to other eNBs, which can also function as network entities (Uchiyama et al. ¶ [0305], [0308]). The combination of Sillasto et al. 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 (Previously Presented), Sillasto et al. as modified above discloses:
The method of claim 12, wherein the parameters of the probability distribution function comprise a mean, a mean vector, a standard deviation, a covariance matrix, a weight, a weight vector, a weight matrix, or combinations thereof (Sillasto et al. “The weighted mass centre method uses a weighted average of the site’s coordinates.” - ¶ [0050]).
Regarding claim 18 (Original), Sillasto et al. as modified above discloses:
The method of claim 12, wherein receiving the statistics comprises receiving marginal probability distributions separately from joint probability distributions (Sillasto et al. “The PDFs of distance conditioned by the observation and of orientation conditioned by the observations are then combined with each other (block 11) to determine joint PDF of UE distance and orientation within the cell…” - ¶ [0081]; “Once the joint PDFs of distance and orientation within all the cells providing active radio links are available (blocks 4’) they are combined (block 12’) to determine the final joint PDF of distance and orientation…” - ¶ [0082]), receiving the marginal probability distributions together with the joint probability distributions, or combinations thereof.
Regarding claim 20 (Previously Presented), Sillasto et al. as modified above discloses:
The method of claim 12, wherein the network entity receives a plurality of probability distributions of the one or more time-angle metrics associated with the UE from a plurality of base stations (Sillasto et al. “The PDFs of distance conditioned by the observation and of orientation conditioned by the observations are then combined with each other (block 11) to determine joint PDF of UE distance and orientation within the cell…” - ¶ [0081]; “Once the joint PDFs of distance and orientation within all the cells providing active radio links are available (blocks 4’) they are combined (block 12’) to determine the final joint PDF of distance and orientation…” - ¶ [0082]), and calculates the estimated position of the UE based on the plurality of probability distributions (Sillasto et al. “The CI+RTT location method involves the two main steps of 1. Estimating the location of the UE in terms of x-y coordinates and 2. Calculating a confidence region for this location estimate.” - ¶ [0086]-[0088]).
Regarding claim 21 (Previously Presented), Sillasto et al. discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
A base station (Sillasto et al. Node B, Fig. 2), comprising:
calculate statistics of one or more time-angle metrics (Sillasto et al. “a distance estimate calculation obtained from a set of measurement pairs of the round trip time (RTT) and Rx-Tx time difference (TD)” - ¶ [0040]) based on a signal received from a user equipment (UE) (Sillasto et al. UE, Figs. 2-3), the statistics comprising parameters of a probability distribution function (Sillasto et al. “By premeasuring data of this kind, a probability density function of the double range measurement area can be determined…” - ¶ [0042]; “Thus, the CI+RTT location algorithm provides as outputs: location calculation results being a set of parameters identifying the coordinates of a location estimate and the parameters of a certain confidence region (a geographical region where the exact UE location is estimated to be with a certain probability)” - ¶ [0083]-[0084]); and
Uchiyama et al. discloses:
A base station (Uchiyama et al. eNB 200, Figs. 1, 8), comprising:
a memory (Uchiyama et al. storage unit 240, Fig. 8);
at least one transceiver (Uchiyama et al. radio communication unit 220, Fig. 8; “The wireless communication unit 220 performs wireless communication with the UE 300 located within a macro cell 20.” - ¶ [0070]); and
at least one processor (Uchiyama et al. processing unit 250, Fig. 8) communicatively coupled to the memory and the at least one transceiver (Uchiyama et al. processing unit 250 is coupled to radio communication unit 220 and storage unit 240, Fig. 8), the at least one processor configured to:
calculate statistics (Uchiyama et al. “each eNB, instead of the NE 100, may generate the statistical information.” - ¶ [0305]) based on a signal received from a user equipment (UE) (Uchiyama et al. UE 300, Figs. 1, 9), the statistics comprising parameters of a probability distribution function; and
report the statistics to a network entity (Uchiyama et al. “the eNB of a macro cell may generate both the macro cell statistical information and the small cell statistical information, and may provide the small cell statistical information for the eNB of the small cell.” - ¶ [0305]; “the NE may be implemented in the eNB representing the plurality of eNBs. In this case, the representative eNB may function as the NE for other eNBs while function as the NE for its own apparatus.” - ¶ [0308]).
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 Sillasto et al. to yield the invention of claim 21 above. Both Sillasto et al. and Uchiyama et al. are considered analogous arts to the claimed invention as they both disclose methods of positioning a user equipment in a wireless communication network and generating probability density functions based on signals received from a UE. Sillasto et al. discloses the limitations of claim 21 outlined above. However, Sillasto et al. fails to explicitly disclose the base station performs the calculation of the statistics and reports the statistics to a network entity. This feature is disclosed by Uchiyama et al. where “each eNB, instead of the NE 100, may generate the statistical information.” (Uchiyama et al. ¶ [0305]) and the eNB can generate and report statistical information to other eNBs, which can also function as network entities (Uchiyama et al. ¶ [0305], [0308]). The combination of Sillasto et al. 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 26 (Original), the same cited section and rationale as corresponding method claim 6 is applied.
Regarding claim 32 (Previously Presented), Sillasto et al. discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
receive, via the at least one transceiver, from a base station (Sillasto et al. Node B, Fig. 2), statistics of one or more time-angle metrics (Sillasto et al. “a distance estimate calculation obtained from a set of measurement pairs of the round trip time (RTT) and Rx-Tx time difference (TD)” - ¶ [0040]) associated with a user equipment (UE) (Sillasto et al. UE, Figs. 2-3), the statistics comprising parameters of a probability distribution function (Sillasto et al. “By premeasuring data of this kind, a probability density function of the double range measurement area can be determined…” - ¶ [0042]; “Thus, the CI+RTT location algorithm provides as outputs: location calculation results being a set of parameters identifying the coordinates of a location estimate and the parameters of a certain confidence region (a geographical region where the exact UE location is estimated to be with a certain probability)” - ¶ [0083]-[0084]); and
calculate, based on the statistics, an estimated position of the UE (Sillasto et al. “Thus, the CI+RTT location algorithm provides as outputs: location calculation results being a set of parameters identifying the coordinates of a location estimate and the parameters of a certain confidence region (a geographical region where the exact UE location is estimated to be with a certain probability)” - ¶ [0083]-[0084]).
Uchiyama et al. discloses:
A network entity (Uchiyama et al. network entity 100, Figs. 1-2), comprising:
a memory (Uchiyama et al. storage unit 120, Fig. 2);
at least one transceiver (Uchiyama et al. communication unit 110, Fig. 2); and
at least one processor (Uchiyama et al. processing unit 130, Fig. 2) communicatively coupled to the memory and the at least one transceiver (Uchiyama et al. processing unit 130 is coupled to communication unit 110 and storage unit 120, Fig. 2), the at least one processor configured to:
receive, via the at least one transceiver, from a base station (Uchiyama et al. eNB 200, Fig. 8), statistics (Uchiyama et al. “each eNB, instead of the NE 100, may generate the statistical information.” - ¶ [0305]) associated with a user equipment (UE) (Uchiyama et al. UE 300, Fig. 9), the statistics comprising parameters of a probability distribution function (Uchiyama et al. “the statistical information generation unit 133 creates a probability density function of the CQI…” - ¶ [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 Sillasto et al. to yield the invention of claim 32 above. Both Sillasto et al. and Uchiyama et al. are considered analogous arts to the claimed invention as they both disclose methods of positioning a user equipment in a wireless communication network and generating probability density functions based on signals received from a UE. Sillasto et al. discloses the limitations of claim 32 outlined above. However, Sillasto et al. fails to explicitly disclose the network entity receives the statistics from the base station. This feature is disclosed by Uchiyama et al. where “each eNB, instead of the NE 100, may generate the statistical information.” (Uchiyama et al. ¶ [0305]) and the eNB can generate and report statistical information to other eNBs, which can also function as network entities (Uchiyama et al. ¶ [0305], [0308]). The combination of Sillasto et al. 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 33 (Previously Presented),
The network entity of claim 32, wherein the parameters of the probability distribution function comprise a mean, a mean vector, a standard deviation, a covariance matrix, a weight, a weight vector, a weight matrix, or combinations thereof (Sillasto et al. “The weighted mass centre method uses a weighted average of the site’s coordinates.” - ¶ [0050]).
Regarding claim 38 (Currently Amended), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The network entity of claim 32, wherein, to receive the statistics, the at least one processor is configured to receive marginal probability distributions separately from joint probability distributions (Sillasto et al. “The PDFs of distance conditioned by the observation and of orientation conditioned by the observations are then combined with each other (block 11) to determine joint PDF of UE distance and orientation within the cell…” - ¶ [0081]; “Once the joint PDFs of distance and orientation within all the cells providing active radio links are available (blocks 4’) they are combined (block 12’) to determine the final joint PDF of distance and orientation…” - ¶ [0082]), receive the marginal probability distributions together with the joint probability distributions, or combinations thereof.
Regarding claim 40 (Previously Presented), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. 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 (Sillasto et al. “The PDFs of distance conditioned by the observation and of orientation conditioned by the observations are then combined with each other (block 11) to determine joint PDF of UE distance and orientation within the cell…” - ¶ [0081]; “Once the joint PDFs of distance and orientation within all the cells providing active radio links are available (blocks 4’) they are combined (block 12’) to determine the final joint PDF of distance and orientation…” - ¶ [0082]) and to calculate the estimated position of the UE based on the plurality of probability distributions (Sillasto et al. “The CI+RTT location method involves the two main steps of 1. Estimating the location of the UE in terms of x-y coordinates and 2. Calculating a confidence region for this location estimate.” - ¶ [0086]-[0088]).
Claim(s) 4 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sillasto et al. (US 2005/0255854 A1, newly cited by the examiner) in view of Uchiyama et al. (EP 3,018,923 A1, previously relied upon by the examiner) as applied to claim 1 above, and further in view of Bevan et al. (US 2004/0127230 A1, newly cited by the examiner).
Regarding claim 4 (Original), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The method of claim 1
Bevan et al. discloses:
wherein reporting the statistics comprises reporting statistics for a time-angle metric (Bevan et al. “Therefore, a Probability Density Function (PDF) of AoAs of the scattered signals from the user equipment (6) as received by the base station (2) can be obtained as a PDF p(θ) of the angle distribution of the scatters (4).” - ¶ [0048]) relative to a reference value,
wherein the reference value comprises a value calculated by the base station, a value reported to the base station, or combinations thereof (Bevan et al. “A data store stores pre-calculated expected ratios corresponding to AoAs and the calculated ratio is compared to the pre-calculated expected ratios to extract an estimate of the AoA of the signal from the user equipment.” - ¶ [0007]).
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 Bevan et al. into the invention of Sillasto et al. to yield the invention of claim 4. Sillasto et al., Uchiyama et al. and Bevan et al. are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network and generating probability density functions based on signals received from a UE. Sillasto et al. as modified above discloses the invention of claim 1. However, Sillasto et al. fails to explicitly disclose wherein reporting the statistics comprises reporting statistics for a time-angle metric relative to a reference value, wherein the reference value comprises a value calculated by the base station, a value reported to the base station, or combinations thereof. This feature is disclosed by Bevan et al. where “each eNB, instead of the NE 100, may generate the statistical information.” (Uchiyama et al. ¶ [0305]) and the eNB can generate and report statistical information to other eNBs, which can also function as network entities (Uchiyama et al. ¶ [0305], [0308]). The combination of Sillasto et al., Uchiyama et al. and Bevan 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 provide highly accurate AoA estimates without modifying user equipments (Bevan et al. ¶ [0015]).
Regarding claim 24 (Original), the same cited section and rationale as corresponding method claim 4 is applied.
Claim(s) 5 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sillasto et al. (US 2005/0255854 A1, newly cited by the examiner) in view of Uchiyama et al. (EP 3,018,923 A1, previously relied upon by the examiner) as applied to claim 1 above, and further in view of Ferrari et al. (US 2020/0267681 A1, cited by applicant in IDS filed 2 MAR 2023).
Regarding claim 5 (Currently Amended), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The method of claim 1
Ferrari et al. discloses:
wherein the reporting the statistics comprises identifying what information was used to calculate the statistics, the information comprising at least one of an upink (UL) channel profile, a downlink (DL) channel profile, an uplink signal, or a report about a downlink signal (Ferrari et al. “Channel measurements may include one or more of: channel frequency response (CFR), channel impulse response (CIR), power delay profile (PDP), etc… Channel measurements may further include one or more of: signal strengths (e.g. received signal strength indication (RSSI)) for signals received from cellular transceivers and/or local transceivers and/or may obtain a signal to noise ratio (SNR), a reference signal received power (RSRP), a reference signal received quality (RSRQ), a time of arrival (TOA), or around trip signal propagation (RTT) between UE 105 and WAP (e.g. cellular transceiver such as a gNB 110 or a local transceiver such as a WiFi access point (AP). A UE 105 may transfer these measurements to a location server, such as an LMF 120, to determine a location for UE 105…” - ¶ [0047]).
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 Ferrari et al. into the invention of Sillasto et al. to yield the invention of claim 5. Sillasto et al., Uchiyama et al. and Ferrari et al. are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 1. However, Sillasto et al. fails to explicitly disclose wherein reporting the statistics comprises reporting statistics for a time-angle metric relative to a reference value, wherein the reference value comprises a value calculated by the base station, a value reported to the base station, or combinations thereof. This feature is disclosed by Ferrari et al. where the channel measurements include a channel impulse response (CIR) and power delay profile (PDP). (Ferrari et al. ¶ [0047]). The combination of Sillasto et al., Uchiyama et al. and Ferrari 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 improve signal acquisition and measurement accuracy by a UE (Ferrari et al. ¶ [0046]).
Regarding claim 25 (Currently Amended), the same cited section and rationale as corresponding method claim 5 is applied.
Claim(s) 8-9, 11, 14-15, 17, 19, 28-29, 31, 34-35, 37 and 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sillasto et al. (US 2005/0255854 A1, newly cited by the examiner) in view of Uchiyama et al. (EP 3,018,923 A1, previously relied upon by the examiner) as applied to claim 1 above, and further in view of Dupray (US 8,135,413 B2, previously relied upon by the examiner).
Regarding claim 8 (Previously Presented), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The method of claim 1
Dupray discloses:
wherein calculating the statistics comprises calculating 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 bins.” – 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 the location hypotheses to determine on or more most likely estimates for the target MS 140.” – Col. 60, lines 35-39), and
wherein reporting the statistics to the network entity comprises reporting a probability that a time-angle metric is within a bin range (Dupray “some number of the first order models may reside in remote locations and communicate their generated hypotheses via the Internet.” – Col. 20, lines 12-15; the program iterates through the bins to determine the most likely location and the probability as determined by the range of bins – Col. 61).
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 8. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 1. However, Sillasto et al. fails to explicitly disclose calculating a probability mass function (PMF) at the base station, and wherein the reporting the statistics to the network entity comprises reporting a probability that a time-angle metric is withing a bin range. This feature is disclosed by Dupray where 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. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44).
Regarding claim 9 (Original), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The method of claim 1
Dupray discloses:
wherein calculating the statistics comprises calculating percentile values over a pre- determined set of percentiles (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), and
wherein reporting the statistics to the network entity comprises reporting the percentile values (Dupray “some number of the first order models may reside in remote locations and communicate their generated hypotheses via the Internet.” – Col. 20, lines 12-15; the program iterates through the bins to determine the most likely location and the probability as determined by the range of bins, where each bin represents a percentile of the probability – Col. 61).
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 9. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 1. However, Sillasto et al. fails to 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. This feature is disclosed by Dupray where percentile values calculated in the most likelihood estimator (Dupray location estimator 1344, Figs. 6(2), 8(4)) of the location center. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44).
Regarding claim 11 (Original), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The method of claim 1
Dupray discloses:
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).
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 11. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 1. However, Sillasto et al. fails to explicitly disclose wherein reporting the statistics to the network entity comprises reporting the statistics according to a statistics reporting configuration. This feature is disclosed by Dupray where the location hypotheses are reported using the configuration shown in the data structure diagram of Figs. 9A-9B. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44).
Regarding claim 14 (Original), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The method of claim 12
Dupray discloses:
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 bins.” – embodiment of program shown in Col. 61) and
wherein receiving the statistics comprises receiving a probability that a time-angle metric is within a bin range (Dupray “some number of the first order models may reside in remote locations and communicate their generated hypotheses via the Internet.” – Col. 20, lines 12-15; the program iterates through the bins to determine the most likely location and the probability as determined by the range of bins – Col. 61).
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 14. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 12. However, Sillasto et al. fails to explicitly disclose calculating a probability mass function (PMF) at the mobile base station, and wherein the reporting the statistics to the network entity comprises reporting a probability that a time-angle metric is withing a bin range. This feature is disclosed by Dupray where 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. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44).
Regarding claim 15 (Original), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The method of claim 12
Dupray discloses:
wherein receiving the statistics comprises receiving percentile values over a pre-determined set of percentiles (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; “some number of the first order models may reside in remote locations and communicate their generated hypotheses via the Internet.” – Col. 20, lines 12-15).
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 15. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 12. However, Sillasto et al. fails to explicitly disclose wherein receiving the statistics comprises receiving percentile values over a pre-determined set of percentiles. This feature is disclosed by Dupray where percentile values calculated in the most likelihood estimator (Dupray location estimator 1344, Figs. 6(2), 8(4)) of the location center. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44).
Regarding claim 17 (Currently Amended), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The method of claim 12
Dupray discloses:
wherein receiving the statistics comprises receiving a probability distribution (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 a probability distribution of another time-angle metric or 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.
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 17. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 12. However, Sillasto et al. fails to explicitly disclose wherein receiving the statistics comprises receiving a probability distribution of one time-angle metric or set of time-angle metrics more frequently than receiving a probability distribution of another time-angle metric or set of time-angle metrics. This feature is disclosed by Dupray where location estimates are calculated and reported, and it would be obvious to one of ordinary skill to receive some location estimates more frequently as discussed above. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44) while making efficient use of network resources.
Regarding claim 19 (Original), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The method of claim 12
Dupray discloses:
wherein receiving the statistics comprises receiving the statistics according to a statistics reporting configuration (Dupray data structure diagram, Figs. 9A-9B).
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 19. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 12. However, Sillasto et al. fails to explicitly disclose wherein receiving the statistics comprises receiving the statistics according to a statistics reporting configuration. This feature is disclosed by Dupray where the location hypotheses are reported using the configuration shown in the data structure diagram of Figs. 9A-9B. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44).
Regarding claim 28 (Previously Presented), 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.
Regarding claim 34 (Original), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The network entity of claim 32
Dupray discloses:
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 bins.” – 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 “some number of the first order models may reside in remote locations and communicate their generated hypotheses via the Internet.” – Col. 20, lines 12-15; the program iterates through the bins to determine the most likely location and the probability as determined by the range of bins – Col. 61).
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 34. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 32. However, Sillasto et al. fails to explicitly disclose wherein the statistics comprise a probability mass function (PMF) over a set of bins 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. This feature is disclosed by Dupray where 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. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44).
Regarding claim 35 (Original), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The network entity of claim 32
Dupray discloses:
wherein, to receive the statistics, the at least one processor is configured to receive percentile values over a pre-determined set of percentiles (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; some number of the first order models may reside in remote locations and communicate their generated hypotheses via the Internet.” – Col. 20, lines 12-15).
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 35. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 32. However, Sillasto et al. fails to 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. This feature is disclosed by Dupray where percentile values calculated in the most likelihood estimator (Dupray location estimator 1344, Figs. 6(2), 8(4)) of the location center. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44).
Regarding claim 37 (Currently Amended), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The network entity of claim 32
Dupray discloses:
wherein, to receive the statistics, the at least one processor is configured to receive a probability distribution (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 a probability distribution of another time-angle metric or 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.
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 37. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 32. However, Sillasto et al. fails to explicitly disclose wherein, to receive the statistics, the at least one processor is configured to receive a probability distribution of one time-angle metric or set of time-angle metrics more frequently than receiving a probability distribution of another time-angle metric or set of time-angle metrics. This feature is disclosed by Dupray where location estimates are calculated and reported, and it would be obvious to one of ordinary skill to receive some location estimates more frequently as discussed above. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44) while making efficient use of network resources.
Regarding claim 39 (Original), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. has been struck-through]
The network entity of claim 32
Dupray discloses:
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).
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 Dupray into the invention of Sillasto et al. to yield the invention of claim 39. Sillasto et al., Uchiyama et al. and Dupray are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 32. However, Sillasto et al. fails to explicitly disclose wherein, to receive the statistics, the at least one processor is configured to receive the statistics according to a statistics reporting configuration. This feature is disclosed by Dupray where the location hypotheses are reported using the configuration shown in the data structure diagram of Figs. 9A-9B. The combination of Sillasto et al., Uchiyama et al. and Dupray 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 provide “a number of distinctly different location estimators which provide a greater degree of accuracy and/or reliability…” (Dupray Col. 17, lines 41-44).
Claim(s) 10, 16, 30 and 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sillasto et al. (US 2005/0255854 A1, newly cited by the examiner) in view of Uchiyama et al. (EP 3,018,923 A1, previously relied upon 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), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. 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 Sillasto et al. to yield the invention of claim 10. Sillasto et al., Uchiyama et al. and Karr et al. are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 1. However, Sillasto et al. 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 Sillasto et al., 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 provide 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), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. 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 Sillasto et al. to yield the invention of claim 16. Sillasto et al., Uchiyama et al. and Karr et al. are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 12. However, Sillasto et al. 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 Sillasto et al., 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 provide 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.
Regarding claim 36 (Original), Sillasto et al. as modified above discloses:
[Note: what is not explicitly taught by Sillasto et al. 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 Sillasto et al. to yield the invention of claim 36. Sillasto et al., Uchiyama et al. and Karr et al. are considered analogous arts to the claimed invention as they disclose methods of positioning a user equipment in a wireless communication network. Sillasto et al. as modified above discloses the invention of claim 32. However, Sillasto et al. 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 Sillasto et al., 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 provide 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).
Conclusion
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NAOMI M. WOLFORD
Examiner
Art Unit 3648
/N.M.W./Examiner, Art Unit 3648
26 AUG 2026
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3648
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