Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
All claims have priority date of filing 08/24/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/17/2025 is being considered by the examiner.
Election/Restrictions
Applicant's election with traverse of Species VI of Fig. 7B corresponding to claims 1-28 and 30 in the reply filed on 1/16/2026 is acknowledged. The traversal is on the ground(s) that “the inventions are not independent” since the bodies of the independent claims recite the identical operations of receiving, performing, and transmitting. This is not found persuasive because the restriction is not based on distinct inventions but instead on distinct species. See restriction issued 11/28/2025. The independent claims 1, 11, 19 and 27 are generic and applicant is entitled to rejoinder upon allowance of a generic claim as explained in the requirement issued 11/28/2025.
The requirement is still deemed proper and is therefore made FINAL.
Claim 29 is withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a noneelected species, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 1/16/2026.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-8, 10-11, 18-19, 27-28 and 30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20250142291 A1 Thomas; Robin Rajan et al.
Consider Claims 1, 11, 19 and 27:
A user equipment (UE) configured for wireless communication (Fig. 1, 9-11, UE 104, Fig. 12 UE/device 1202), the UE comprising:
a memory storing processor-readable code (¶162 Fig. 12 memory 1208); and at least one processor coupled to the memory, the at least one processor configured to execute the processor-readable code to cause the at least one processor (Fig. 12 Processor 1206) to
and A method of wireless communication performed by a user equipment (UE) (Figs. 1, 9-11 UE 104 and methods disclosed e.g. ¶28 “measurement and reporting for artificial intelligence based positioning”), the method comprising:
receiving positioning configuration information from a network entity (Fig. 10 1010, ¶100 “the location server 1008 (e.g., the LMF 120) configures the measurement configuration information for one or more target UEs 104 “; ¶¶39-42 “the UE measures the DL PRS RSRP of the received signals using assistance data received from the positioning server (also referred to herein as the location server)”; Fig. 11, 1110 ¶128 “information related to the model training criteria”);
performing, based on the positioning configuration information, one or more positioning operations with respect to one or more wireless communication channels (See Fig. 10 1014, ¶100,¶102 implicating UE takes measurements in response; See e.g. Fig. 3 ¶42 “UE measures the UE Rx-Tx measurements (and optionally DL PRS RSRP of the received signals) using assistance data received from the positioning server (also referred to herein as the location server”); and
transmitting, to a training entity to enable training of a machine learning (ML) positioning model to account for UE clock drift (¶102 “..This response message indicates, for example, deployment and training of an AI inference model using the labelled set of positioning measurements..” where “to account for UE clock drift” is an intended function met by disclosed structure), one or more positioning measurements generated based on performance of the one or more positioning operations (Fig. 10 1014, ¶102 “in response to the request at 1010. . . a UE 104 outputs, to the location server 1008, a response message including a labelled set of positioning measurements. . .”¶36).
Claim 2: Thomas teaches The method of claim 1 (supra), wherein the one or more positioning operations comprise:
monitoring for one or more positioning reference signals (PRSs) (¶41 “The UE measures the DL PRS RSRP of the received signals”) from a transmit/receive point (TRP) (¶42 “UE Rx-Tx measurements and DL PRS RSRP of downlink signals received from multiple TRPs”; ¶95 “..position measurement data may also be labelled according to . . .TRP”); and
measuring one or more channel impulse responses (CIRs) of the one or more wireless communication channels associated with the one or more PRSs (¶130 “..UE 104 labels the collected set of positioning measurements by associating each of one or more positioning measurements of the collected set of positioning measurements with any of various labels, such as an LOS path, an NLOS path, a number of detected paths, a polarization type, coherence bandwidth information, a channel frequency response, a Ricean factor, a combination thereof, and so forth..” where CIR can be determined from CFR thus equivalent).
Claim 3: Thomas teaches The method of claim 3 (supra), wherein each of the one or more positioning measurements includes a CIR of the one or more CIRs (¶130 supra) and a timestamp associated with the measuring of the CIR (¶95 “The time series variation of the position measurement data may also be captured via additional labels such as timestamps, timing windows, and so forth.”).
Claim 4: Thomas teaches The method of claim 3 (supra), wherein each of the one or more positioning measurements further includes a location of the UE during the measuring of the CIR (¶¶148-150 “the input data elemetns from one or more target UEs include . . UE Radio measurements (e.g., RRM measurements) related to serving and neighboring cells associated with UE location information, e.g., coordinates, serving cell ID, moving velocity”; where CIR taught ¶130 supra).
Claim 5 Thomas teaches The method of claim 1 (supra), wherein the positioning configuration information indicates a quantity of one or more positioning occasions associated with positioning at the UE (Fig. 11 1110 ¶128 “model training criteria” received from network; ¶168 “model training criteria including a defined area and time duration for which the AI inference model is valid”; ¶104 “target UE 104 measurements . . . based on a defined area in which the measurements were made (e.g., specific cell list, geographic area, etc.), based on a defined time duration in which the measurements were performed” where a ‘defined time duration’ indicates a quantity of positioning occasions as under BRI of indicates).
Claim 6: Thomas teaches The method of claim 5 (supra), wherein the one or more positioning operations are performed during the one or more positioning occasions while the UE remains at a fixed location during the one or more positioning occasions (Fig. 11 1110 ¶128 “model training criteria” received from network; ¶168 “model training criteria including a defined area and time duration for which the AI inference model is valid”; ¶104 “target UE 104 measurements . . . based on a defined area in which the measurements were made (e.g., specific cell list, geographic area, etc.), based on a defined time duration in which the measurements were performed”).
Claim 7: Thomas teaches The method of claim 1 (supra), wherein the positioning configuration information indicates a positioning duration associated with positioning at the UE (Fig. 11 1110 ¶128 “model training criteria” received from network; ¶168 “model training criteria including a defined area and time duration for which the AI inference model is valid”; ¶104 “target UE 104 measurements . . . based on a defined area in which the measurements were made (e.g., specific cell list, geographic area, etc.), based on a defined time duration in which the measurements were performed”).
Claim 8:. Thomas teaches The method of claim 7 (supra), wherein the one or more positioning operations are performed during the positioning duration while the UE remains at a fixed location during the positioning duration (Fig. 11 1110 ¶128 “model training criteria” received from network; ¶168 “model training criteria including a defined area and time duration for which the AI inference model is valid”; ¶104 “target UE 104 measurements . . . based on a defined area in which the measurements were made (e.g., specific cell list, geographic area, etc.), based on a defined time duration in which the measurements were performed”).
Claim 18: The UE of claim 11, wherein the positioning configuration information is included in positioning protocol assistance data message or a positioning broadcast message that is received from the network entity (See e.g. Fig. 3 ¶42 “UE measures the UE Rx-Tx measurements (and optionally DL PRS RSRP of the received signals) using assistance data received from the positioning server (also referred to herein as the location server” and/or Fig. 10 1010, ¶100 “the location server 1008 (e.g., the LMF 120) configures the measurement configuration information for one or more target UEs 104”; Fig. 11 1110).
Claim 28: The apparatus of claim 27, wherein the training entity comprises a server associated with the network entity (Figs. 10-11 Location Server 1008 ¶98 “a location server 1008. The location server 1008 implements, for example, the LMF 120.” Fig. 1).
Claim 30: The apparatus of claim 27, wherein the training entity comprises a server communicatively coupled to the apparatus (Figs. 10-11 Location Server 1008 ¶98 “a location server 1008. The location server 1008 implements, for example, the LMF 120.” Fig. 1).
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.
Claim(s) 9-10 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250142291 A1 Thomas; Robin Rajan et al. in view of US 20220322127 A1 Sha; Xiubin et al.
Claims 9 and 16: Thomas teaches The method of claim 1 (supra), further comprising:
UE positioning capability (Thomas ¶32 “The one or more UEs 104 may be devices in different forms or having different capabilities” ¶38 “separate positioning techniques can be currently configured and performed based on . . . and UE capabilities”)
Thomas does not teach transmitting positioning capability information to the network entity, wherein the positioning capability information indicates clock drift measuring capabilities of the UE.
Sha teaches transmitting positioning capability information to the network entity, wherein the positioning capability information indicates clock drift measuring capabilities of the UE ( Sha ¶73 “the UE must first send at least one of the following indications to the gNB: UE clock drift reporting capability”See Also Sha Fig. 5A ¶68 teaching requesting and reporting clock drift)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Thomas to include the noted teachings of Sha in order to for quality-of-service (QoS) and quality-of-experience (QoE) monitoring (Sha ¶4).
Claims 10 and 17: The combination teaches The method of claim 9 (supra), wherein the positioning capability information is transmitted during a positioning protocol capability exchange between the UE and the network entity (Sha ¶73 transmission BRI a “capability exchange” as claimed).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Thomas to include the noted teachings of Sha in order to for quality-of-service (QoS) and quality-of-experience (QoE) monitoring (Sha ¶4).
Claim(s) 12, 20-22 and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250142291 A1 Thomas; Robin Rajan et al. in view of US 20220046577 A1 SUNDARARAJAN; Jay Kumar et al.
Claims 12 and 20. Thomas teaches The UE of claim 11 (supra)
Thomas does not explicitly disclose the positioning configuration information an instruction to the UE to add simulated clock drift to reported positioning measurements.
Sundararajan teaches the positioning configuration information an instruction to the UE to add simulated clock drift to reported positioning measurements (Fig. 9 910 ¶213 “ at least one neural network function may be received from a network entity. . . a series of offsets, algorithms and/or processing rules referred to herein as a “neural network function”, which can be used to filter or process positioning measurement data”; ¶206-¶209 “particular subset of measurement parameters that may facilitate more precise positioning may vary between locations, based on gNB-specific or UE-specific configurations, and so on. Such measurement parameters may include:. . . UE-specific parameters (e.g., clock drift. . .”)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Thomas to include the noted teachings of Sundararajan in order to facilitate precise positioning with UE-specific configurations. See Sundararajan ¶206-¶209.
Examiner also notes related art, not relied upon in the current rejection of this claim, US 20220322127 A1Sha; Xiubin et al., teaches [0068] FIG. 5A shows an example procedure to trigger UE clock drift monitoring. As shown therein, the gNB sends a UE clock drift reporting indication to the UE, which can be sent by a System Information Block (SIB), a DL UE specific RRC message, MAC CE, or DCI. Upon reception of the UE clock drift reporting indication, the UE performs the UE clock monitoring (as shown in FIGS. 5B and 5C). After obtaining the clock drift monitoring results, the UE reports it to the gNB, which can be sent by a UL UE specific RRC message or MAC CE.
Claim 21: The combination teaches The non-transitory, computer-readable medium of claim 20 (Supra), wherein the operations further comprise:
transmitting, to the training entity to enable the training of the ML positioning model, the simulated clock drift information associated with the UE (Sundararajan ¶216 “[0216] At 940, UE 302 (e.g., transmitter 314, transmitter 324, etc.) reports the processed set of positioning measurement features to a network component..” Sundararajan ¶206-¶209 “UE-specific parameters (e.g., clock drift. . .”); SEE ALSO Chrabieh ¶211 “In the mobile 300, we estimate the Doppler at one or more time instants 302, we report the estimated Dopplers to the base station if desired 304, otherwise we perform a fit of the timing drift function τ.sub.m(t) 306 in the mobile”).
Claim 22: The combination teaches The non-transitory, computer-readable medium of claim 21, wherein the simulated clock drift information is transmitted during a positioning protocol capability exchange between the UE and the network entity (See claim 21 supra Sundararajan ¶216 and ¶206-209; where transmission meets BRI intended environment of “capability exchange”).
Claim 25: The combination teaches The non-transitory, computer-readable medium of claim 21, wherein the simulated clock drift information is transmitted in a reporting message that further includes an operating temperature range associated with the simulated clock drift information (See claim 21 supra Sundararajan ¶216 and ¶206-209; where transmission meets NFD claim elements as noted below).
Examiner notes that the contents of the reporting message are recited merely as printed matter or nonfunctional descriptive material (NFD) where the content of a reporting message does not cause an effect or functional transformation in the product and/or merely produces a predictable result that does not distinguish from the prior art (See MPEP 2115.05 Functional and Nonfunctional Descriptive Material and MPEP 2112.01 Nonfunctional printed matter does not distinguish claimed product from otherwise identical prior art product)
Claim 26: The combination teaches The non-transitory, computer-readable medium of claim 21, wherein the simulated clock drift information is transmitted in a reporting message that further includes group information indicating a group of UEs associated with the simulated clock drift information (See claim 21 supra Sundararajan ¶216 and ¶206-209; where transmission meets NFD claim elements as noted below)..
Examiner notes that the contents of the reporting message are recited merely as printed matter or nonfunctional descriptive material (NFD) where the content of a reporting message does not cause an effect or functional transformation in the product and/or merely produces a predictable result that does not distinguish from the prior art (See MPEP 2115.05 Functional and Nonfunctional Descriptive Material and MPEP 2112.01 Nonfunctional printed matter does not distinguish claimed product from otherwise identical prior art product)
Claim(s) 13-15, and 23-26 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250142291 A1 Thomas; Robin Rajan et al. in view of US 20220046577 A1 SUNDARARAJAN; Jay Kumar et al. and further in view of US 20220373638 A1 Chrabieh; Rabih et al.
Claim 13: The combination teaches The UE of claim 12, wherein performance of the one or more positioning operations generates initial positioning measurements (Thomas e.g. Fig. 3 ¶42 “UE measures the UE Rx-Tx measurements (and optionally DL PRS RSRP of the received signals) using assistance data received from the positioning server (also referred to herein as the location server”).
The combination does not teach wherein the at least one processor is further configured to, prior to transmission of the one or more positioning measurements:
adjust the initial positioning measurements based on simulated clock drift measurements to generate the one or more positioning measurements.
Chrabieh teaches wherein the at least one processor is further configured to, prior to transmission of the one or more positioning measurements: adjust the initial positioning measurements based on simulated clock drift measurements to generate the one or more positioning measurements (Fig. 3 310, 312, 314 ¶211 “estimate the TOA from several base stations at various times 310, and optionally compensate them 312 in the mobile for the timing drift function T.sub.m(t). The corrected estimates of TOA are optionally reported to the network in step 314”)).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Thomas to include the noted teachings of Chrabieh in order to modeling a receiver clock drift using doppler estimates of the TOAs. See Chrabieh ¶14.
Claim 14: The combination teaches The UE of claim 13, wherein the positioning configuration information indicates one or more clock drift parameters (Sundararajan ¶206-¶209 “particular subset of measurement parameters that may facilitate more precise positioning may vary between locations, based on gNB-specific or UE-specific configurations, and so on. Such measurement parameters may include:. . . UE-specific parameters (e.g., clock drift. . .”), and wherein the simulated clock drift measurements are generated based on the one or more clock drift parameters (¶215 “..UE 302 (e.g., processing system 332, measurement module 342, etc.) processes the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function. .”).
See also Sundararajan ¶214 “the positioning measurement data may comprise an estimate of a channel response associated with a reference signal.”
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the combination to include the noted teachings of Sundararajan in order to facilitate precise positioning with UE-specific configurations. See Sundararajan ¶206-¶209.
Claim 15: The combination teaches The UE of claim 13, wherein the simulated clock drift measurements are generated based on one or more clock drift parameters that are preprogrammed at the UE (Sundararajan ¶206-¶209 “particular subset of measurement parameters that may facilitate more precise positioning may vary between locations, based on gNB-specific or UE-specific configurations, and so on. Such measurement parameters may include:. . . UE-specific parameters (e.g., clock drift. . .”),.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the combination to include the noted teachings of Sundararajan in order to facilitate precise positioning with UE-specific configurations. See Sundararajan ¶206-¶209.
Claim 23: The combination in further view of Chrabieh teaches The non-transitory, computer-readable medium of claim 21, wherein the simulated clock drift information includes a median UE clock drift, a mean UE clock drift, a standard deviation UE clock drift, a percentile UE clock drift, a UE clock drift range, a probability distribution associated with UE clock drift, or a combination thereof (Chrabieh ¶211 “In the mobile 300, we estimate the Doppler at one or more time instants 302, we report the estimated Dopplers to the base station if desired 304, otherwise we perform a fit of the timing drift function τ.sub.m(t) 306 in the mobile” where “fit of timing drift function” renders obvious various parameters i.e. distribution, median, range of drift; and see also in combination with Sundararajan ¶117 “the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay, and delay spread of a second reference RF signal transmitted on the same channel”).
Claim 24: The combination in further view of Chrabieh teaches The non-transitory, computer-readable medium of claim 21, wherein the simulated clock drift information includes one or more clock drift estimates associated with the one or more positioning measurements (Sundararajan ¶216 “[0216] At 940, UE 302 (e.g., transmitter 314, transmitter 324, etc.) reports the processed set of positioning measurement features to a network component..” Sundararajan ¶206-¶209 “UE-specific parameters (e.g., clock drift. . .”); SEE ALSO Chrabieh ¶211 “In the mobile 300, we estimate the Doppler at one or more time instants 302, we report the estimated Dopplers to the base station if desired 304, otherwise we perform a fit of the timing drift function τ.sub.m(t) 306 in the mobile”).
Pertinent Prior Art(s)
The prior art made of record though not relied upon in the current rejection is considered pertinent to applicant's disclosure:
FR 2904498 A1 WILHELM NICOLAS et al.
The locating module ML is for example of the same type as that which equips the TOPSTAR 3000 navigation receiver of ALCATEL. Such an ML location module comprises for example an orbital Kaiman filter performing state vector propagation by means of an appropriate orbital force model and available navigation measurements. The state vector is composed of six orbital parameters, the value of the local clock drift and the local clock bias.
US 20240430849 A1 Paltin; Evgeny et al.
[0002] Another approach to determining a UE's position is a channel impulse response (CIR)-based direct AI/ML (artificial intelligence/machine learning) approach, which avoids the line-of-sight dependency by having an AI/ML model find a relationship between CIR data and a position coordinate; (this approach is called ‘Direct’ because it maps directly between the CIR and location coordinates without trying to model the process). However, the CIR-based direct AI/ML approach is very impractical in most scenarios because of being sensitive to the slightest variations manifested in the perceived CIR. More particularly, one of the most significant CIR-related variations is a clock instability, which can correspond to loose timing synchronization between transmit-receive points. When a clock drifts, the perceived time of arrival is incorrect and channel taps phase rotate, resulting in incorrect CIR data. CIR-based direct AI/ML algorithms thus require very tight network synchronization. One solution attempts to include virtually all of the targeted conditions in the training dataset; for clock-related issues, this means attempting to generate a training dataset with virtually all possible variations of clock behaviors among multiple transmit-receive points and a UE. Such a solution is not practical for real system deployments.
[0014] Various aspects of the technology described herein are generally directed towards having a trained artificial intelligence/machine learning (AI/ML) model map a dataset of round-trip time values, measured between transmit-receive points and user equipment at an unknown location, to an estimated location of the user equipment (e.g., as location coordinates [x, y] or [x, y, z]). Significantly, the model can be trained for combinations of line of sight and non-line of sight communication links, and is intended to be trained on measured round trip time data instead of channel impulse response data.
Conclusion
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UMAIR AHSAN
Primary Examiner
Art Unit 2647
/UMAIR AHSAN/Primary Examiner, Art Unit 2647
Prosecution Insights