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 .
EXAMINER’S COMMENT
Specification
The modifications to the specification were received on 01/21/2026. These modifications are accepted by the Examiner.
Response to Arguments
Regarding Independent claim 1:
Applicant's arguments filed 01/21/2026 have been fully considered but they are not persuasive.
The Applicant contends:
“R1-2300448 fails to disclose a wireless device that outputs an indication of the calculated set of positioning model capability metrics that comprises a latency and an accuracy of the positioning model, as recited by amended independent claim 1. In fact, R1-2300448 fails to disclose any signaling whatsoever. In short, R1-2300448 fails to disclose, inter alia, receive, output an indication of the calculated set of positioning model capability metrics, wherein the set of positioning model capability metrics comprises a latency and an accuracy of the positioning model, as recited in amended independent claim 1.” (emphasis in original)
The Examiner disagrees, and asserts that, R1-2300448 specifically discloses “In the previous sections, we mainly evaluate the positioning accuracy performance and generalization capability for AI/ML based positioning, and observe that AI technology has great potential to improve positioning accuracy. On the other hand, power consumption, computational complexity, parameter quantity, training data requirements and hardware requirements (including for given processing delays) associated with enabling respective AI/ML scheme are essential for practical deployment of AI based positioning.”
See the definition of latency “Latency is a measurement of delay in a system.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “R1-2300448 fails to disclose any signaling whatsoever”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The Applicant contends:
“Similarly, Hasegawa fails to disclose a wireless device that outputs an indication of the calculated set of positioning model capability metrics that comprises, a latency and an accuracy of the positioning model, as recited by amended independent claim 1. In short, like R1-2300448, Hasegawa fails to disclose, inter alia, receive, output an indication of the calculated set of positioning model capability metrics, wherein the set of positioning model capability metrics comprises a latency and an accuracy of the positioning model, as recited in amended independent claim 1” (emphasis in original)
The Examiner disagrees, and asserts that, Hasegawa specifically discloses “Accuracy and latency for positioning are important performance metrics” … “The positioning request may also include information related to positioning QoS (e.g., accuracy, latency, integrity).”
See also Applicant Admitted Prior Art in page 1 of the present Application “5G NR is part of a continuous mobile broadband evolution promulgated by Third Generation Partnership Project (3GPP) to meet new requirements associated with latency, reliability, security, scalability (e.g., with Internet of Things (IoT)), and other requirements.”
For these reasons and the reasons of the previous Office action the rejection of claim 1 is maintained.
Regarding Independent claims 13, 24 and 28:
Applicant's arguments filed 01/21/2026 have been fully considered but they are not persuasive.
The Applicant contends:
“Independent claims 13, 24, and 28 include limitations similar to those presented in independent claim 1 discussed above. As such, the arguments for the patentability of claim 1 above apply to claims 13, 24, and 28 with equal force.”
The Examiner disagrees, and asserts that, because the rejection of claim 1 is maintained, the rejection of claims 13, 24 and 28 are also rejected.
For these reasons and the reasons of the previous Office action the rejection of claims 13, 24 and 28 are maintained.
Regarding Independent claims 2-12, 14-23, 25-27 and 29-30:
Applicant's arguments filed 01/21/2026 have been fully considered but they are not persuasive.
The Applicant contends:
“Dependent claims 2-12, 14-23, 25-27, and 29-30 each also depend, either directly or indirectly, from one of independent claims 1, 13, 24, or 28. They are believed to be allowable at least based on their dependence on the allowable respective base claim and further based on the additional elements recited therein and in any intervening claim”
The Examiner disagrees, and asserts that, because the rejection of claim 1 is maintained, the rejection of claims 2-12, 14-23, 25-27, and 29-30are also rejected.
For these reasons and the reasons of the previous Office action the rejection of claims 2-12, 14-23, 25-27, and 29-30 are maintained.
Regarding Independent claim 6:
Applicant’s arguments, see Amendment/Request for Reconsideration-After Non-Final Rejection, filed 01/21/2026, with respect to claim 6 have been fully considered and are persuasive. The rejections of claim 6 has been withdrawn.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 8-16, and 19-30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pan ("Evaluation on Al/ML for positioning accuracy enhancement", 3GPP DRAFT; R1-2300448; 17 February 2023).
Regarding claims 1 and 24, Pan discloses measure a set of positioning signals and calculate a set of estimated positioning results using a positioning model based on the measured set of positioning signals (section 3.1 AI/ML positioning, the UE, which is a device intrinsically comprising a processor and a memory, determines its position via measurements and using an Al/ML model as shown in figure 2, "3.1. Direct AI/ML positioning for direct AI/ML positioning, UE position can be directly estimated according to multiple TRPs’ Channel Impulse Response (CIR) vectors, as shown in Figure 2. Note that, AI/ML model can be deployed at the UE side or network side”); obtain a set of verified positioning labels associated with the set of estimated positioning results, calculate a set of positioning model capability metrics based on the set of estimated positioning results and the set of verified positioning label the set of positioning model capability metrics comprises a latency and an accuracy of the positioning model and output an indication of the calculated set of positioning model capability metrics (section 7.2.1. “Ground truth label-based model monitoring. When ground truth labels can be collected from deployed PRUs, comparing the difference between the location estimated by AI/ML model and the corresponding ground truth label is the most direct and reliable manner to monitor the mapping relationship between model input space and output space” … section 8 “The positioning accuracy performance and generalization capability for AI/ML based positioning, and observe that AI technology has great potential to improve positioning accuracy. On the other hand, power consumption, computational complexity, parameter quantity, training data requirements and hardware requirements (including for given processing delays) associated with enabling respective AI/ML scheme are essential for practical deployment of AI based positioning.”).
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Regarding claims 13 and 28, Pan discloses transmitting a request for a wireless device to indicate its support for calculating a set of positioning results using a positioning model and receiving, based on the request, a response comprising a set of positioning model capability metrics, the set of verified positioning label the set of positioning model capability metrics comprises a latency and an accuracy of the positioning model and output an indication of the calculated set of positioning model capability metrics (section 3.1 AI/ML positioning, the UE, which is a device intrinsically comprising a processor and a memory, determines its position via measurements and using an Al/ML model as shown in figure 2, "3.1. Direct AI/ML positioning For direct AI/ML positioning, UE position can be directly estimated according to multiple TRPs’ Channel Impulse Response (CIR) vectors, as shown in Figure 2. Note that, AI/ML model can be deployed at the UE side or network side”) … section 8 “The positioning accuracy performance and generalization capability for AI/ML based positioning, and observe that AI technology has great potential to improve positioning accuracy. On the other hand, power consumption, computational complexity, parameter quantity, training data requirements and hardware requirements (including for given processing delays) associated with enabling respective AI/ML scheme are essential for practical deployment of AI based positioning.”)
Regarding claims 2 and 25, Pan discloses claims 1 and 24, Pan also discloses receiving the set of positioning signals prior to measuring of the set of positioning signals, and calculating the set of estimated positioning results using the positioning model based on the received measured set of positioning signals (section 7.2.1 “When ground truth labels can be collected from deployed PRUs, comparing the difference between the location estimated by AI/ML model and the corresponding ground truth label is the most direct and reliable manner to monitor the mapping relationship between model input space and output space”)
Regarding claims 3 and 26, Pan discloses claims 2 and 25, Pan also discloses apply a set of positioning configurations prior to a reception of the set of positioning signals and receive the set of positioning signals based on the set of positioning configurations (section 4.2.2 “We evaluate the generalization capability of AI/ML model across clutter parameters for AI/ML assisted positioning. As shown in Table 16, it is observed that while AI/ML model performs well when training dataset and test dataset are sampled from the same clutter parameter configuration”)
Regarding claim 4, Pan discloses claim 1, Pan also discloses a positioning model power consumption (section 321 classification accuracy metrics, section 7 “delay spread” … “Thus, it is concluded that AI/ML based out-of-distribution detection can achieve flexible and accurate model monitoring without need of frequent model training and large-scale data collection” section 8 “cost evaluation In the previous sections, we mainly evaluate the positioning accuracy performance and generalization capability for AI/ML based positioning, and observe that AI technology has great potential to improve positioning accuracy. On the other hand, power consumption, computational complexity, parameter quantity, training data requirements and hardware requirements (including for given processing delays) associated with enabling respective AI/ML scheme are essential for practical deployment of AI based positioning“)
Regarding claim 5, Pan discloses claim 1, Pan also discloses at least one of: a percentile of accuracy errors; an average of the accuracy errors; a range of positioning errors; a confidence interval (section 3.1 table 1 3.2.2 confidence metric table 9)
Regarding claims 8 and 27, Pan discloses claims 1 and 24, Pan also discloses a plurality of supported positioning models and associated sets of model capability metrics, wherein each supported positioning model of the plurality of supported positioning models is associated with an associated set of model capability metrics of the associated sets of model capability metrics, wherein the set of supported positioning models comprises the positioning model, wherein the associated sets of model capability metrics comprise the calculated set of positioning model capability metrics (section 6 model training section 7 and 8 “metrics/methods” 8 cost evaluation table 50 AI models FNN1 FNN2 and Vision Transformer).
Regarding claim 9, Pan discloses claim 1, Pan also discloses set of sounding reference signals (SRSs) or a set of positioning reference signals (PRSs) (section 6.2 “For example, one way to collect unlabeled data at network side is that UEs report CIRs estimated from PRS measurement” section 6.3)
Regarding claim 10, Pan discloses claim 1, Pan also discloses a user equipment (UE) or a network node (section 3.1 UE and network side)
Regarding claim 11, Pan discloses claim 1, Pan also discloses transmit, for a network entity, the indication of the calculated set of positioning model capability metrics (section 3.1 UE and network side section 6 model training section 7 “metrics/methods” 8.1 model evaluation).
Regarding claim 12, Pan discloses claim 1, Pan also discloses store, in a second memory or a cache, the indication of the calculated set of positioning model capability metrics (section 3.1 the UE, which is a device intrinsically comprising a processor and a memory, determines its position via measurements and using an Al/ML model as shown in figure 2, "Direct Al /ML positioning. For direct Al /ML positioning, UE position can be directly estimated according to multiple TRPs' Channel Impulse Response (CIR) vectors, as shown in Figure 2. Al/ML model can be deployed at the UE side or network side").
Regarding claims 14 and 29, Pan discloses claims 13 and 28, Pan also discloses transmit a set of positioning configurations based on the configured set of positioning signals (section 7.2.1 “When ground truth labels can be collected from deployed PRUs, comparing the difference between the location estimated by AI/ML model and the corresponding ground truth label is the most direct and reliable manner to monitor the mapping relationship between model input space and output space”)
Regarding claim 15, Pan discloses claim 13, Pan also discloses a positioning model power consumption (section 321 classification accuracy metrics, section 7 “delay spread” … “Thus, it is concluded that AI/ML based out-of-distribution detection can achieve flexible and accurate model monitoring without need of frequent model training and large-scale data collection” section 8 “cost evaluation In the previous sections, we mainly evaluate the positioning accuracy performance and generalization capability for AI/ML based positioning, and observe that AI technology has great potential to improve positioning accuracy. On the other hand, power consumption, computational complexity, parameter quantity, training data requirements and hardware requirements (including for given processing delays) associated with enabling respective AI/ML scheme are essential for practical deployment of AI based positioning “)
Regarding claim 16, Pan discloses claim 13, Pan also discloses at least one of a percentile of accuracy errors; an average of the accuracy errors; a range of positioning errors; a confidence interval or any combination thereof (section 3.1 table 1 3.2.2 confidence metric table 9)
Regarding claim 19, Pan discloses claim 13, Pan also discloses set of sounding reference signals (SRSs) or a set of positioning reference signals (PRSs) (section 6.2 “For example, one way to collect unlabeled data at network side is that UEs report CIRs estimated from PRS measurement” section 6.3)
Regarding claim 20, Pan discloses claim 13, Pan also discloses a location management function (section 7.1.3 “Thanks for the powerful capabilities of feature extraction, we have verified that AI/ML technology can be used not only for positioning function, but also for model monitoring”)
Regarding claims 21 and 30, Pan discloses claims 13 and 28, Pan also discloses output an indication of the configured set of positioning signals after the configuration of the set of positioning signals based on the set of positioning model capability metrics (section 6 model training section 7 “metrics/methods” 8 cost evaluation table 50)
Regarding claim 22, Pan discloses claim 21, Pan also discloses transmit, to the wireless device, the indication of the configured set of positioning signals (section 3.1 UE and network side section 6 model training section 7 “metrics/methods” 8.1 model evaluation).
Regarding claim 23, Pan discloses claim 21, Pan also discloses store in a second memory or a cache the indication of the configured set of positioning signals (section 3.1 the UE, which is a device intrinsically comprising a processor and a memory, determines its position via measurements and using an Al/ML model as shown in figure 2, "Direct Al /ML positioning. For direct Al /ML positioning, UE position can be directly estimated according to multiple TRPs' Channel Impulse Response (CIR) vectors, as shown in Figure 2. Al/ML model can be deployed at the UE side or network side").
Claims 1-5, 8-16 and 19-30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hasegawa (WO 2022155244 A2).
Regarding claims 1 and 24, Hasegawa discloses measure a set of positioning signals and calculate a set of estimated positioning results using a positioning model based on the measured set of positioning signals (abstract “The disclosure pertains to methods and apparatus for using artificial intelligence and machine learning for positioning of nodes (e.g., wireless transmit/receive units (WTRUs)) in wireless communications. In an example, a method implemented by a WTRU for wireless communications includes receiving configuration information indicating a plurality of positioning methods and a threshold, determining a respective weight for each of the plurality of positioning methods, and sending the respective weights for the plurality of positioning methods based on determining that at least one of the respective weights is greater than the threshold and/or after a preconfigured time period”); obtain a set of verified positioning labels associated with the set of estimated positioning results, calculate a set of positioning model capability metrics based on the set of estimated positioning results and the set of verified positioning label the set of positioning model capability metrics comprises a latency and an accuracy of the positioning model and output an indication of the calculated set of positioning model capability metrics (abstract paragraph [0117] “A refence point is defined as a location estimate of the WTRU. The WTRU will use the reference point during training of the machine learning model where the reference point serves as the “actual location of the WTRU”. Therefore, the WTRU needs to obtain the location estimate from a reliable source, e.g., GNSS when line of sight is available between the WTRU and GNSS satellites. FIG. 5 is a signal flow diagram illustrating an example of a WTRU initiated training procedure for positioning” paragraph [0122] “The positioning request may also include information related to positioning QoS (e.g., accuracy, latency, integrity).”).
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Regarding claims 13 and 28, Hasegawa discloses transmitting a request for a wireless device to indicate its support for calculating a set of positioning results using a positioning model and receiving, based on the request, a response comprising a set of positioning model capability metrics comprises a latency and an accuracy of the positioning model and output an indication of the calculated set of positioning model capability metrics (abstract “The disclosure pertains to methods and apparatus for using artificial intelligence and machine learning for positioning of nodes (e.g., wireless transmit/receive units (WTRUs)) in wireless communications. In an example, a method implemented by a WTRU for wireless communications includes receiving configuration information indicating a plurality of positioning methods and a threshold, determining a respective weight for each of the plurality of positioning methods, and sending the respective weights for the plurality of positioning methods based on determining that at least one of the respective weights is greater than the threshold and/or after a preconfigured time period” paragraph [0122] “The positioning request may also include information related to positioning QoS (e.g., accuracy, latency, integrity)”); and configuring a set of positioning signals based on the set of positioning model capability metrics (abstract paragraph [0117] “A refence point is defined as a location estimate of the WTRU. The WTRU will use the reference point during training of the machine learning model where the reference point serves as the “actual location of the WTRU”. Therefore, the WTRU needs to obtain the location estimate from a reliable source, e.g., GNSS when line of sight is available between the WTRU and GNSS satellites. FIG. 5 is a signal flow diagram illustrating an example of a WTRU initiated training procedure for positioning”).
Regarding claims 2 and 25, Hasegawa discloses claims 1 and 24, Hasegawa also discloses receiving the set of positioning signals prior to measuring of the set of positioning signals, and calculating the set of estimated positioning results using the positioning model based on the received measured set of positioning signals (section 5.2 training using a reference paragraph [0117])
Regarding claims 3 and 26, Hasegawa discloses claims 2 and 25, Hasegawa also discloses apply a set of positioning configurations prior to a reception of the set of positioning signals and receive the set of positioning signals based on the set of positioning configurations (section 5.2 training using a reference paragraph [0117]).
Regarding claim 4, Hasegawa discloses claim 1, Hasegawa also discloses power consumption (paragraph [0039] [0045] “The processor 118 may receive power from the power source 134, and may be configured to distribute and/or control the power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102)
Regarding claim 5, Hasegawa discloses claim 4, Hasegawa also discloses at least one of: a percentile of accuracy errors; an average of the accuracy errors; a range of positioning errors; or a confidence interval or any combination thereof (claim 21).
Regarding claims 8 and 27, Hasegawa discloses claims 1 and 24, Hasegawa also discloses a plurality of supported positioning models and associated sets of model capability metrics, wherein each supported positioning model of the plurality of supported positioning models is associated with an associated set of model capability metrics of the associated sets of model capability metrics, wherein the set of supported positioning models comprises the positioning model, wherein the associated sets of model capability metrics comprise the calculated set of positioning model capability metrics (paragraph [0117] loss metric, claim 12]).
Regarding claim 9, Hasegawa discloses claim 1, Hasegawa also discloses set of sounding reference signals (SRSs) or a set of positioning reference signals (PRSs) (paragraph [0191], [0194] SRS)
Regarding claim 10, Hasegawa discloses claim 1, Hasegawa also discloses a user equipment (UE) or a network node (abstract figure 6 WTRU)
Regarding claim 11, Hasegawa discloses claim 1, Hasegawa also discloses transmit, for a network entity, the indication of the calculated set of positioning model capability metrics (paragraph [0117] loss metric, claim 12]).
Regarding claim 12, Hasegawa discloses claim 1, Hasegawa also discloses store, in a second memory or a cache, the indication of the calculated set of positioning model capability metrics (WTRU paragraph [0117] loss metric, claim 12]).
Regarding claims 14 and 29, Hasegawa discloses claims 13 and 28, Hasegawa also discloses transmit a set of positioning configurations based on the configured set of positioning signals (section 5.2 training using a reference paragraph [0117]).
Regarding claim 15, Hasegawa discloses claim 13, Hasegawa also discloses power consumption (paragraph [0039] [0045] “The processor 118 may receive power from the power source 134, and may be configured to distribute and/or control the power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102)
Regarding claim 16, Hasegawa discloses claim 15, Hasegawa also discloses at least one of a percentile of accuracy errors; an average of the accuracy errors; a range of positioning errors; a confidence interval or any combination thereof (claim 21).
Regarding claim 19, Hasegawa discloses claim 13, Hasegawa also discloses set of sounding reference signals (SRSs) or a set of positioning reference signals (PRSs) (paragraph [0191], [0194] SRS).
Regarding claim 20, Hasegawa discloses claim 13, Hasegawa also discloses a location management function (figure 5 LMF paragraphs [0105] [0109]-[0110] [0117] “The WTRU performs positioning and determines the loss metric between estimated position and reference source (511) a. If the loss metric is larger than the threshold, the WTRU may request from the network a new reference source or new configurations for RS for training for LMF and the LMF may provide assistance data for the new reference source via LPP”)
Regarding claims 21 and 30, Hasegawa discloses claims 13 and 28, Hasegawa also discloses output an indication of the configured set of positioning signals after the configuration of the set of positioning signals based on the set of positioning model capability metrics (paragraph [0117] loss metric, claim 12]).
Regarding claim 22, Hasegawa discloses claim 21, Hasegawa also discloses transmit, to the wireless device, the indication of the configured set of positioning signals (figure 5 block 507 block [0117] claim 10).
Regarding claim 23, Hasegawa discloses claim 21, Hasegawa also discloses store in a second memory or a cache the indication of the configured set of positioning signals (WTRU paragraph [0117] loss metric, claim 12]).
Allowable Subject Matter
Claims 6-7 and 17-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/JUAN A TORRES/ Primary Examiner, Art Unit 2634