DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant's arguments and remarks filed on 04/02/2026 have been fully considered.
Applicant's amendments overcome the previous U.S.C. 101 rejection.
Claims 1–8, 10–14, 16, 19, 21–23, and 25–29 have been amended.
Claims 9, 20, 24, and 30 are canceled.
Claims 31–32 are newly added. No new matter was introduced.
Claims 1–8, 10–19, 21–23, 25–29, and 31–32 are pending.
Response to Arguments
Applicant's arguments filed 04/02/2026 have been fully considered but they are not persuasive.
The rejection is maintained under 35 U.S.C. § 103 over Sundararajan (US 2022/0046577 A1) as the primary reference in view of Tullberg (US 2022/0051139 A1) as a secondary reference. Tullberg is not withdrawn; it remains relied upon, but its role has been narrowed. Tullberg is no longer relied upon for the “receive a measurement configuration” limitation, for “select a reference point positioning signal … based on the measurement configuration,” or for “reference the measured set of positioning signals based on the selected reference point.” Those limitations are now shown to be disclosed by the primary reference, Sundararajan. Tullberg is retained solely for the reference-relative data-representation and machine-learning-training limitations - namely, the use of reference-relative data as the input to the positioning model (claim 1), “select a referencing method from a plurality of referencing methods” (claim 3), training the positioning model on the referenced measurements (claims 5 and 23), and “reference each measured positioning signal … as a value from the selected reference point positioning signal” (claims 31 and 32).
Applicant’s argument that Tullberg’s “compressed data” is not a “measurement configuration” is persuasive as to the prior mapping and has been adopted. The Examiner agrees that Tullberg’s “compressed data … a compressed representation of data samples” (Tullberg [0153]) is not the claimed measurement configuration, and Tullberg is no longer equated with that limitation. The measurement configuration is now shown to be disclosed by Sundararajan: the UE “receives a PRS configuration index … in the OTDOA assistance data” (Sundararajan [0162]) and “obtains at least one neural network function” from a network entity (Sundararajan [0213]). Because Tullberg’s compressed data is no longer mapped to the measurement configuration, this argument is moot as to the maintained rejection.
Applicant’s argument that Tullberg’s cluster centroid is not “selected based on the measurement configuration” is likewise persuasive as to the prior mapping and has been adopted. Tullberg’s per-sample centroid determination is no longer relied upon as the claimed reference-point selection. The selection of the reference point positioning signal “based on the measurement configuration” is now shown to be disclosed by Sundararajan: the OTDOA assistance data (the measurement configuration) “includes assistance data for a reference cell” (Sundararajan [0162]), and the UE “determine[s] the timing of the PRS occasions of the reference and neighbor cells for OTDOA positioning” (Sundararajan [0164]). The reference cell so designated is the selected reference point positioning signal and constitutes a timing reference point. This argument is therefore moot as to the maintained rejection.
Applicant’s argument that the Office failed to allege that Sundararajan discloses “receive a measurement configuration” and “reference the measured set of positioning signals based on the selected reference point” is no longer applicable, as Sundararajan is now expressly relied upon for both limitations. As to referencing the measured set “based on the selected reference point,” Sundararajan discloses that the UE measures “RSTDs between reference RF signals received from pairs of network nodes” (Sundararajan [0191]) and determines position “using the OTDOAs and/or RSTDs” (Sundararajan [0194]); the reference signal time difference expresses each measured neighbor-cell signal as a time value relative to the selected reference cell.
The newly added limitation “wherein the reference point positioning signal comprises a timing reference point, a power reference point, or a phase reference point” does not distinguish over the art. This is an alternative (“or“) limitation; under MPEP § 2111.04 the prior art need teach only one alternative. Sundararajan teaches the timing reference point alternative via the OTDOA reference cell and RSTD timing (Sundararajan [0162], [0164], [0191]). Applicant’s Remarks do not separately address this clause, and the power-reference-point and phase-reference-point alternatives need not be reached.
Applicant’s arguments do not reach Tullberg’s retained teachings or Sundararajan’s reference-cell teaching. The arguments are directed exclusively to Tullberg’s former role as supplying the measurement configuration and the reference-point selection. They do not address the teachings for which Tullberg is now relied upon - that, “For each new data sample, the closest cluster centroid is determined” and the distance thereto is computed (Tullberg [0171]), and that the model is trained “using the received compressed data as input to the machine learning model” (Tullberg [0157]) - nor do they address Sundararajan’s disclosure of a reference cell and RSTD-based referencing. Those teachings therefore stand unrebutted.
Applicant’s argument that claims 19 and 29 are allowable for reasons similar to claim 1, and that the dependent claims are allowable by virtue of their dependence, is not persuasive, because claim 1 is not allowable for the reasons set forth in the rejection. Independent claims 19 and 29 are rejected over Sundararajan in view of Tullberg as set forth above. Applicant has not separately traversed the rejection of any dependent claim on the merits; the dependent claims accordingly stand or fall with their respective independent claims.
For at least the foregoing reasons, the claims remain unpatentable, the rejection is maintained as modified over Sundararajan in view of Tullberg, and this action is made final.
Claim Objections
Claim 7 objected to because of the following informalities:
Claim 7 states “from the measured set of positioning signal based” which mixes singular and plural terminology. Correction to “from the measured set of positioning signals based”.
Appropriate correction is required.
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.
Claims 9, 20, 24, and 30 are canceled and will not be addressed.
Claims 1-8, 10-19, 21-23, 25-29, and 31-32 are rejected under 35 U.S.C. 103 as being unpatentable over Sundararajan et al. (US 2022/0046577 A1) in view of Tullberg et al. (US 2022/0051139 A1).
Regarding Claims 1 and 29, the claims are independent claims directed to an apparatus and a method, respectively. The body of Claim 29 recites substantively identical functional elements as Claim 1, differing only in the preamble. Claims 1 and 29 are therefore grouped and the full analysis is presented for Claim 1.
Sundararajan et al. (‘577) teaches: An apparatus for wireless communication at a wireless positioning device, comprising: ([0212]: “the process 900 may be performed by a UE”; FIG. 3A).
Sundararajan et al. (‘577) teaches: at least one memory; and at least one processor coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor, is configured to: ([0254]: a machine-learning module “implemented by a processing system, such as processors 332, 384, or 394”; FIG. 3A depicting memory coupled to the processor).
Sundararajan et al. (‘577) teaches: receive a measurement configuration ([0162]: “when a UE receives a PRS configuration index … in the OTDOA assistance data”; [0213]: the UE “obtains at least one neural network function” received from a network entity). The OTDOA assistance data and neural-network function received by the UE constitute the measurement configuration.
Sundararajan et al. (‘577) teaches: receive a set of positioning signals ([0214]: the data is obtained “by performing a set of positioning measurements on a reference signal for positioning (e.g., PRS)”; [0189]: base stations “broadcast … Positioning Reference Signals (PRS)”).
Sundararajan et al. (‘577) teaches: measure the set of positioning signals ([0205]: the UE “is configured to measure and report certain pre-defined metrics such as … (TOA) … (RSTD)”; [0011]: the data comprises “raw samples of a reference signal for positioning”).
Sundararajan et al. (‘577) teaches: select a reference point positioning signal from the measured set of positioning signals based on the measurement configuration, wherein the reference point positioning signal comprises a timing reference point, a power reference point, or a phase reference point ([0162]: the OTDOA assistance data “includes assistance data for a reference cell”; [0164]: the UE “determine the timing of the PRS occasions of the reference and neighbor cells for OTDOA positioning”). This element recites an “or” statement listing three alternative reference points; per MPEP § 2111.04 the art need teach only one alternative. The reference cell’s positioning reference signal—designated by the received OTDOA assistance data (the measurement configuration) and used as the basis for the time-difference measurements—is selected from the measured set and constitutes the timing reference point. The “power reference point” and “phase reference point” alternatives are not separately addressed, as the timing alternative is taught.
Sundararajan et al. (‘577) teaches: reference the measured set of positioning signals based on the selected reference point ([0191]: the UE measures “RSTDs between reference RF signals received from pairs of network nodes”; [0194]: the UE measures the reference RF signals to “determine the position … using the OTDOAs and/or RSTDs”). The reference signal time difference (RSTD) expresses each measured neighbor-cell signal as a time value relative to the selected reference cell, thereby referencing the measured set to the selected timing reference point.
Sundararajan et al. (‘577) teaches: output the referenced, measured set of positioning signals to a positioning model ([0215]: the UE “processes the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function”; [0223]: the features comprise “a compressed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning”). The neural-network function is the positioning model, and the reference-relative measurement features are output to it.
Sundararajan et al. (‘577) does not explicitly state that the data referenced to the selected reference point is the input used by the positioning model, but Tullberg et al. (‘139) teaches this linkage ([0171]: “For each new data sample, the closest cluster centroid is determined” and the distance to that centroid is determined; [0157]: “train the machine learning model using the received compressed data as input to the machine learning model”; [0180]: “the ML model is trained on cluster centroids”). Tullberg represents each measured sample relative to a selected reference point (the closest cluster centroid) and uses that reference-relative representation as the input to the machine-learning model.
It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to combine the positioning-measurement neural-network processing of Sundararajan et al. (‘577) with the reference-relative data representation and machine-learning training of Tullberg et al. (‘139). One would have been motivated to do so because both references address the same problem of reducing the volume of wireless-device measurement data conveyed to a network node for machine-learning purposes—Sundararajan to reduce positioning-measurement signaling overhead ([0211]) and Tullberg to avoid transmitting the full set of training data ([0011])—and Tullberg teaches that representing each measured sample relative to a reference point and using that representation as the model input accomplishes this reduction while preserving model performance ([0171], [0180]). Applying Tullberg’s reference-relative representation and training to Sundararajan’s positioning measurements referenced to the selected timing reference point predictably yields the claimed output of the referenced measured set to a positioning model. There is a reasonable expectation of success because both references operate on wireless-device measurement data processed by machine learning at or for a network node, such that applying a known data-referencing-and-training technique to analogous positioning data is the predictable use of a known technique to achieve a predictable result. This rationale rests on the references’ own stated goals as noted above.
Claim 29 is rejected for the same reasons as Claim 1, as its method body is substantively identical to Claim 1.
Regarding Claim 2, Sundararajan et al. (‘577) teaches the apparatus of Claim 1, and further teaches: transmit the referenced measured set of positioning signals to a wireless device for the positioning model ([0216]: the UE “reports the processed set of positioning measurement features to a network component”; [0026]: “report the processed set of positioning measurement features to a network component”; [0035]: “transmit, via the at least one transceiver, to a user equipment (UE), at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE…and receive, via the at least one transceiver, from the UE, a set of positioning measurement features that is processed based on the at least one neural network function”).
Regarding Claim 3, Sundararajan et al. (‘577) teaches the apparatus of Claim 2 and teaches transmitting a referencing indicator with the reported features ([0216]).
Sundararajan does not explicitly teach, but Tullberg et al. (‘139) teaches: select a referencing method from a plurality of referencing methods based on the measured set of positioning signals ([0171]: “Other clustering techniques may be used as well, for example, the Expectation Maximization (EM) algorithm” and “For each new data sample, the closest cluster centroid is determined”; [0172]:“In some embodiments, a Principal Component Analysis (PCA) or similar analysis per cluster is performed”). Tullberg selects, from a plurality of referencing/clustering methods, the method by which each measured sample is referenced to a reference point, based on the data samples themselves.
It would have been obvious to a PHOSITA before the effective filing date to incorporate Tullberg’s selection among a plurality of reference-relative (clustering) methods into Sundararajan’s positioning system, for the same overhead-reduction reasons set forth for Claim 1; selecting the referencing method best suited to the measured data is the predictable application of Tullberg’s express teaching, with a reasonable expectation of success because both references process wireless-device measurement data for machine learning.
Sundararajan et al. (‘577) does not explicitly teach, but Tullberg et al. (‘139) teaches: wherein the measurement configuration comprises an indicator of the plurality of referencing methods ([0153]: “The network node 110 is configured to receive compressed data from the wireless device 120”; [0162]: the UE “receives a PRS configuration index I.sub.PRS in the OTDOA assistance data”; [0012]: “a plurality of neural network functions”) Sundararajan et al. (‘577)’s measurement configuration carries an indicator/index designating which of a plurality of processing methods the device applies.
Sundararajan et al. (‘577) teaches: wherein, to output the referenced measured set of positioning signals to the positioning model, the at least one processor, individually or in any combination, is configured to: transmit a referencing indicator associated with the selected referencing method ([0034]: “the processing processes the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and the reporting reports the probability distribution or metrics based on the probability distribution”; [0216]: “UE 302 (e.g., transmitter 314, transmitter 324, etc.) reports the processed set of positioning measurement features to a network component”).
It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to combine the positioning-measurement processing of Sundararajan et al. (‘577) with the data-driven selection among a plurality of reference-relative methods of Tullberg et al. (‘139). One would have been motivated to do so because both references address the same problem of reduction the volume of wireless device measurement data conveyed to a network node for machine learning purposes – Sundararajan et al. (‘577) to reduce positioning measurement signaling overhead ([0211]) and Tullberg et al. (‘139) to avoid transmitting the full set of training data ([0011]). Selecting, based on the measured data, among Tullberg’s plurality of referencing methods ([0171]) and indicating that plurality through the measurement configuration that Sundararajan’s device already receives ([0162]) is the predictable use of known technique to achieve a predictable result, with a reasonable expectation of success because both references process wireless device measurement data for machine learning at or for a network node.
Regarding Claim 4, Sundararajan et al. (‘577) teaches the apparatus of Claim 3. Claim 4 recites a list joined by “; or any combination thereof”; per MPEP § 2111.04 only one alternative need be taught. Sundararajan et al. (‘577) teaches: wherein the referencing indicator comprises at least one of: a referencing method identifier (ID) associated with the selected referencing method ([0034]: “the reporting reports the probability distribution or metrics based on the probability distribution”; [0014]: “the processing processes the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and the reporting reports the probability distribution or metrics based on the probability distribution”);
Sundararajan et al. (‘577) does not explicitly teach, but Tullberg et al. (‘139) teaches: a condition range ID associated with the selected referencing method; an adaptation decision ID associated with the selected referencing method ([0171]: “the distance to the cluster centroid is determined and compared to a threshold”; [0134]: “the wireless device 120 may be configured to store, in a memory 307, the cluster centroid, the cluster counter and the number of outlier collected data samples associated with the cluster as the compressed data”);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include identifiers for condition ranges and adaptation decisions as taught by Tullberg et al. (‘139) in the system of Sundararajan et al. (‘577). One would have been motivated to do so in order to communicate which processing method was used based on the data characteristics, enabling the receiving device to properly interpret the processed data ([0171], [0134] of Tullberg). A person of ordinary skill would have had a reasonable expectation of success because including identifiers to indicate processing methods is conventional practice in data communication systems.
Regarding Claim 5, Sundararajan et al. (‘577) teaches the apparatus of Claim 1. Claim 5 recites two alternatives joined by “or”; only one need be taught. Sundararajan teaches the second alternative: calculate a positioning output using the positioning model based on the referenced measured set of positioning signals ([0020]: “determining a positioning estimate for the UE”). The first alternative—train the positioning model and a set of labels—is additionally taught by Tullberg ([0157]) together with Sundararajan’s supervised-learning labels ([0254]: “associate this training input data with an output data set”), but need not be reached.
Regarding Claim 6, Sundararajan et al. (‘577) teaches the apparatus of Claim 5, and further teaches: the measurement configuration comprises a referencing indicator ([0162]: “PRS configuration index … in the OTDOA assistance data”)
Sundararajan et al. (‘577) teaches: select the positioning model from a plurality of positioning models based on the referencing indicator ([0012]: “a plurality of neural network functions” selected per the configured measurement type).
Regarding Claim 7, Sundararajan et al. (‘577) teaches the apparatus of Claim 5. Claim 7 recites “at least one of (a) … or (b) …”; only one alternative need be taught. Sundararajan teaches alternative (b), an adaptation decision from a plurality of adaptation decisions for the reference point positioning signal ([0204]: the first-arriving cluster “is presumed to be the LOS data stream”; [0211]: “timing and magnitude of a … number of peaks in the channel estimate”), and references the measured set based on the selected reference point as set forth for Claim 1. Alternative (a) need not be addressed.
Sundararajan et al. (‘577) additionally teaches: select the reference point positioning signal from the measured set of positioning signal(s) based on at least one of the selected condition range or the selected adaptation decision ([204]) and reference the measured set of positioning signals based on the selected reference point positioning signal ([0191]: RSTD relative to the reference cell).
Regarding Claim 8, Sundararajan et al. (‘577) teaches the apparatus of Claim 5, including a transceiver ([0218]; FIG. 3A), and further teaches: transmit, via the transceiver, the calculated positioning output ([0218], Fig. 3A, [0216]: reporting the processed features to a network component).
Claim 9 is canceled.
Regarding Claim 10, Sundararajan et al. (‘577) teaches the apparatus of Claim 1. Claim 10 recites three alternatives joined by “or,” each conditioned on the reference point “satisfying” a criterion. Per MPEP § 2111.04, these are contingent (“in response to … satisfying”) limitations that need not necessarily occur, and only one “or” alternative need be taught. Sundararajan teaches the first alternative, select the reference point positioning signal from the measured set of positioning signals in response to the reference point positioning signal satisfying a condition range ([0205]: configured to measure metrics such as RSRP and SINR; [0030]: “(SINR)”, RSRP). The remaining contingent alternatives need not be addressed.
Regarding Claim 11, Sundararajan et al. (‘577) teaches the apparatus of Claim 10. Claim 11 recites a list joined by “; or any combination thereof”; only one alternative need be taught. Sundararajan teaches a third range of signal-to-interference plus noise ratios (SINRs) / a fourth range of reference signal received powers (RSRPs) ([0030]: “signal-to-interference-plus-noise ratio (SINR)” and “received power (RSRP)”; [0162]: the measurement configuration / OTDOA assistance data carries a “PRIS configuration index” and like selection indices). The remaining alternatives need not be addressed.
Regarding Claim 12, Sundararajan et al. (‘577) teaches the apparatus of Claim 10. Claim 12 recites a list joined by “; or any combination thereof”; only one alternative need be taught. Sundararajan teaches an earliest arrival, wherein the measurement configuration comprises a sixth indicator to select the earliest arrival ([0204]: the first cluster “arrives first … presumed to be the LOS data stream”; [0162]: the measurement configuration carries the corresponding selection index). The remaining alternatives need not be addressed.
Regarding Claim 13, Sundararajan et al. (‘577) teaches the apparatus of Claim 10. Claim 13 recites a contingent (“in response to the reference point positioning signal satisfying the condition range”) limitation; per MPEP § 2111.04 the conditional step need not occur. To the extent given weight, Sundararajan teaches select the adaptation decision from a plurality of adaptation decisions in response to the reference point positioning signal satisfying the condition range, wherein the measurement configuration comprises an indicator of the plurality of adaptation decisions ([0204-0205]: selecting a peak / earliest-arrival path where a measured condition such as SINR/RSRP is met; [0162]: the measurement configuration carries the corresponding selection indices).
Regarding Claim 14, Sundararajan et al. (‘577) teaches the apparatus of Claim 1, and further teaches: transmit an indicator of supported referencing attributes before the reception of the measurement configuration ([0209]: “UE-specific parameters (e.g., … device type … chipset type, etc.)” that the network uses to generate/filter the configuration). The UE’s indication of its supported attributes precedes and informs the network-generated measurement configuration ([0210]).
Regarding Claim 15, Sundararajan et al. (‘577) teaches the apparatus of Claim 14. Claim 15 recites “at least one of”; only one alternative need be taught. Sundararajan teaches a timing reference type ([0209]: timing-related parameters such as “clock drift, antenna-to-baseband delay or hardware group delay”; [0205]: RSTD/TOA timing metrics). The remaining alternatives need not be addressed.
Regarding Claim 16, Sundararajan et al. (‘577) teaches the apparatus of Claim 1, and further teaches: the measurement configuration comprises a plurality of sets of referencing attributes ([0162], [0191]: assistance data comprising configurations for a reference cell and a plurality of neighbor cells), select a set of referencing attributes from the plurality of sets of referencing attributes ([0164]), and select the reference point positioning signal from the measured set of positioning signals based on the selected set of referencing attributes ([0162], [0191]: assistance data comprising configurations for a reference cell and a plurality of neighbor cells, from which the reference cell is selected per [0164]).
Regarding Claim 17, Sundararajan et al. (‘577) teaches the apparatus of Claim 1. Claim 17 recites “or”; only one alternative need be taught.
Sundararajan et al. (‘577) teaches: wherein the set of positioning signals comprises a set of positioning reference signals (PRSs) or a set of sounding reference signals (SRSs) ([0017]: “the positioning measurement data comprises a set of positioning measurements on a reference signal for positioning”; [0027]: “the obtaining obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning”).
Note: The claim uses the “or” statement, meaning prior art only needs to teach one of the listed alternatives.
Regarding Claim 18, Sundararajan et al. (‘577) teaches the apparatus of Claim 1. Claim 18 recites “at least one of”; only one alternative need be taught.
Sundararajan et al. (‘577) teaches: wherein the wireless positioning device comprises at least one of a user equipment (UE) ([0066]: “executed by a user equipment (UE), cause the UE to: obtain at least one neural network function”; [0026]: “In an aspect, a method of operating a UE includes: obtaining at least one neural network function”);
Sundararajan et al. (‘577) teaches: a base station ([0035]: “In an aspect, a base station (BS) includes a memory; at least one transceiver; and at least one processor”);
Sundararajan et al. (‘577) teaches: or a transmission reception point (TRP) ([0150]: “such as the processing systems 332, 384, 394, the transceivers 310, 320, 350, and 360”).
Note: The claim uses the “at least one of” statement, meaning prior art only needs to teach one of the listed alternatives.
Regarding Claim 19, Claim 19 is an independent claim directed to an apparatus at a network entity.
Sundararajan et al. (‘577) teaches: An apparatus for wireless communication at a network entity, comprising: at least one memory; and at least one processor coupled to the at least one memory … ([0217]: process performed by a BS; [0254]: processors; [0130]: LMF).
Sundararajan et al. (‘577) teaches: configure a measurement configuration for a set of positioning signals and a set of positioning models at a wireless positioning device, wherein the measurement configuration comprises a referencing indicator associated with a reference point positioning signal, wherein the reference point positioning signal comprises a timing reference point, a power reference point, or a phase reference point ([0191]: the location server “send[s] assistance data to the UE … includ[ing] … configuration information for reference RF signals”; [0162]: assistance data “includes assistance data for a reference cell”). This is an “or” statement; the timing reference point alternative is taught (the reference-cell designation associated with the RSTD timing reference). The remaining alternatives need not be addressed.
Sundararajan et al. (‘577) teaches: transmit the measurement configuration ([0218]: the BS “transmits, to a UE, at least one neural network function”; [0191]: assistance data sent to the UE).
Sundararajan et al. (‘577) teaches: receive a set of positioning signal measurements reference based on the reference point positioning signal associated with the referencing indicator ([0022]: “receiving, from the UE, a … set of … positioning measurement features”; [0015]: “receiving, from the UE, a set of positioning measurement features”; [0191]: the UE reports RSTDs referenced to the reference cell).
Claim 20 is canceled.
Regarding Claim 21, Sundararajan et al. (‘577) teaches the apparatus of Claim 19, including a transceiver ([0218]; FIG. 3B), and further teaches: receive, via the transceiver, the referencing indicator associated with the reference point positioning signal ([0022]; “receiving, from the UE, a … set … positioning measurement features”); and calculate a positioning output using a positioning model based on the received set of positioning signal measurements and the referencing indicator, wherein the set of positioning models comprises the positioning model ([0020]: “determining a positioning estimate for the UE based on the received set” the BS receives the features; [0012]: the plurality of neural network functions includes the positioning model).
Regarding Claim 22, Sundararajan et al. (‘577) teaches the apparatus of Claim 21. Claim 22 recites “at least one of”; only one alternative need be taught. Sundararajan teaches a positioning model ID associated with the reference point positioning signal ([0012]: identified neural network functions). The remaining alternatives need not be addressed.
Regarding Claim 23, Sundararajan et al. (‘577) teaches the apparatus of Claim 19 and teaches receiving the referencing indicator, receive referencing indicator associated with the reference point positioning signal ([0015], [0022]: “receiving, from the UE, a … set of … positioning measurement features”).
Sundararajan et al. (‘477) does not explicitly teach, but Tullberg et al. (‘139) teaches: train a positioning model based on the received set of positioning signal measurements and the referencing indicator ([0157]: “train the machine learning model using the received compressed data as input to the machine learning model”; [0180]: “the ML model is trained on cluster centroids”). Sundararajan et al. (‘577)’s network entity calculates a positioning estimate from the received measurements while Tullberg’s network node trains the machine-learning model using the received reference-relative measurement data.
It would have been obvious to a PHOSITA before the effective filing date of the claimed invention to train Sundararajan’s positioning model at the network entity on the received positioning signal measurements as taught by Tullberg et al. (‘139). One would have been motivated to do so to build the positioning model from the wireless device’s reported measurements while minimizing the data that must be transmitted (Sundararajan et al. (‘477) [0211]; Tullberg et al. (‘139) [0011]), with a reasonable expectation of success because both references train machine-learning models at a network node on wireless-device measurement data.
Claim 24 is canceled.
Regarding Claim 25, Sundararajan et al. (‘577) teaches the apparatus of Claim 19. Claim 25 recites “at least one of”; only one alternative need be taught. Sundararajan teaches a positioning model ID ([0012]). The remaining alternatives need not be addressed.
Regarding Claim 26, Sundararajan et al. (‘577) teaches the apparatus of Claim 19. Claim 26 recites a list joined by “; or any combination thereof”; only one alternative need be taught. Sundararajan teaches a positioning model ID ([0012]). The remaining alternatives need not be addressed.
Regarding Claim 27, Sundararajan et al. (‘577) teaches the apparatus of Claim 19. Claim 27 recites a list joined by “; or any combination thereof”; only one alternative need be taught. Sundararajan teaches a third indicator to select a third range of signal-to-interference plus noise ratios (SINRs) / a fourth indicator to select a fourth range of reference signal received powers (RSRPs) ([0030]; [0162], [0205]). The remaining indicators need not be addressed.
Regarding Claim 28, Sundararajan et al. (‘577) teaches the apparatus of Claim 19, and further teaches: receive an indicator of supported referencing attributes, wherein, to configure the measurement configuration for the set of positioning signals and the set of positioning models at the wireless positioning device, the at least one processor is configured to: select a set of referencing attributes from the supported referencing attributes ([0209]: UE-specific parameters used to configure processing). The recited supported attributes are an “at least one of” list; Sundararajan teaches a timing reference type ([0205]). The remaining alternatives need not be addressed. Further taught: configure the referencing indicator based on the selected set of referencing attributes ([0162], [0191], [0210]).
Regarding Claims 31 and 32, Claims 31 and 32 depend from Claims 1 and 19, respectively, and recite the same additional limitation. They are grouped and the analysis for Claim 31 is presented; Claim 32 is rejected for the same reasons.
Sundararajan et al. (‘577) teaches the apparatus of Claim 1, including referencing the measured set via RSTD relative to the selected reference cell ([0191], [0194]). Sundararajan does not explicitly recite, but Tullberg et al. (‘139) teaches: reference each measured positioning signal of the measured set of positioning signals as a value from the selected reference point positioning signal ([0171]: “For each new data sample, the closest cluster centroid is determined” and “the distance to the cluster centroid is determined”). Tullberg expresses each measured sample as a value (distance) from the selected reference point (centroid).
It would have been obvious to a PHOSITA before the effective filing date to represent each of Sundararajan’s measured positioning signals as a value from the selected reference point as taught by Tullberg, for the same overhead-reduction reasons set forth for Claim 1, with a reasonable expectation of success because expressing each measurement relative to a reference point is the predictable application of Tullberg’s express technique to Sundararajan’s reference-cell-referenced measurements.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REMASH R GUYAH whose telephone number is (571)270-0115. The examiner can normally be reached M-F 7:30-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha H Desai can be reached at (571) 270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/REMASH R GUYAH/Examiner, Art Unit 3648
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3648
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