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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. All claims have priority date of 12/21/2022.
Response to Amendment
Office action in response to amendment entered 1/12/2026. Claims 1 are amended, claims 4, 6 and 12 are canceled, and claims 17-19 are added; claims 1-3, 5, 7-11, and 13-19 remain pending in this application.
The examiner thanks the applicant for indicating the support for the claim amendments in the remarks.
Response to Arguments
Applicant’s arguments with respect to all claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-3, 5, 7-11, and 13-19 are rejected under 35 U.S.C. 103 as obvious over 20230003826 A1 Parker; Valerie et al. in view of US 20240259879 A1 RANGANATH; Sunku et al.
Claims 1 and 8 Parker (See Fig. 6, pars. 183, Fig. 23) discloses A Near-Real time RAN Intelligent Controller (Near RT RIC) (100) (near-real-time radio access network intelligent controller (near-RT-RIC) 618,) for predicting a location of a user equipment (UE) (500) (618 can include and/or otherwise implement xAPPs, such as a location engine xAPP, a first location-aware xAPP (identified by Location-Aware App1), and a second location-aware xAPP (identified by Location-Aware App2) in a wireless communication network (network depicted Fig. 6), wherein the Near RT RIC (100) comprises:
a memory (120) (Fig. 55 various memory components par. 468);
a processor (140) coupled to the memory (120) (Fig. 55 par. 469 processor platform);
a communicator (160) coupled to the memory (120) and the processor (140) (fig. 55 par. 469-470 interface 5520); and
a location management controller (180) coupled to the memory (120), the processor (140) and the communicator (160) (Fig. 2 location engine circuitry 200), and wherein the location management controller (180) is configured to:
receive collated UE measurements associated with the UE (500), from an access node (1000) over an E2 interface, wherein the collated UE measurements is determined based on a plurality of UE measurements associated with the UE (500) sent by at least three transmission receipt points (TRPs) (600a-c) to the access node (1000) (pars. 175, 180, 186, 192 teaching location related measurements collated and sent to near-RT-RIC as claimed and par. 168 teaching three TRP);
input the collated UE measurements to a location prediction model (184) located at the Near RT RIC (100); and predict a location of the UE (500) by the location prediction model (184) based on the collated UE measurements ( pars. 175, 180, 186, 192 “..The near-RT-RIC 618 can determine a location of the UE 602 based on the cellular data…”),
wherein the location prediction model is deployed by the Non RT RIC (See Fig. 6 618 Near_RT_RIC with location engine and location aware app 1, app 2; [0149] “Once training is complete, the location engine circuitry 200 can deploy the ML model(s) 296 for use as executable construct(s) that process(es) an input and provides output(s) based on the network of nodes and connections defined in the ML model(s) 296.”; ¶186 “he near-RT-RIC 618 can provide the location to one(s) of the other xAPPs and/or one(s) of the one or more rAPPs of the SMO service 620.”) at the Near RT RIC (See Fig. 6 618 ¶183 “. The near-RT-RIC 618 can include and/or otherwise implement xAPPs, such as a location engine xAPP, a first location-aware xAPP (identified by Location-Aware App1), and a second location-aware xAPP (identified by Location-Aware App2).” ¶280 “ location engine (LE) xAPP is an application configured and/or otherwise adapted to run on a near-RT RIC that identifies data to consume via a PLDC and provide location result”) over an A1 interface (Fig. 6 ¶281 “ interfaces can be specified by the O-RAN standard (e.g., A1, E2, O1, Open Fronthaul Interface, etc.).”);
Parker teaches wherein the location prediction model is trained (par. 141 teaching training location models, par. 143-149 detailing training steps; See also par. 101 teaching location engine circuitry 200 may be implemented in any of the various cloud or core devices CU RU DUs etc; See also par. 183 non-RT-RIC implementation)
Further teaches running policy and location management on the non-RT RIC (¶183-¶185) and lastly, “modifying the implementation of location engine circuitry “location engine circuitry 200 of FIG. 2 may be instantiated (e.g., creating an instance of, bring into being for any length of time, materialize, implement, etc.) by an ASIC or an FPGA structured to perform operations corresponding to the instructions. It should be understood that some or all of the location engine circuitry 200 may, thus, be instantiated at the same or different times” (Parker [0100]) in order for distributed and scalable high performance location and positioning (Parker [0002])
Parker does not explicitly teach wherein the location prediction model is trained by Non-Real Time RIC. RANGANATH is in the same field of art e.g. ¶168 “FIG. 10 illustrates an example O-RAN Architecture 1000 including Near-RT RIC interfaces. The Near-RT RIC 1014 is connected to the Non-RT RIC 1012 through the A1 interface (see e.g., [O-RAN.WG2.A1GAP]). The Near-RT RIC 1014 is a logical network node placed between the E2 nodes and the SMO 1002, which hosts the Non-RT RIC 1012.”
RANGANATH teaches wherein the location prediction model is trained by Non-Real Time RIC and deployed at the Near-Real Time RIC (RANGANATH ¶167 “In some examples, ML model training can be performed by the non-RT RIC 912 and/or the near-RT RIC 914, and the trained ML models can be operated to generate predictions/inferences in control loops 932, 934, and/or 935. The (trained) ML model runs in the near-RT RIC 914 for control loop 934, and the (trained) ML model runs in the O-DU 915 for control loop 935).
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 Parker to include the noted teachings of RANGANATH in order to train using the “combination of the non-RT RIC 912 and SMO 902 where intensive computation means can be made available.” Ranganath ¶167.
Claims 2 and 9. The combination teaches The Near RT RIC (100) as claimed in claim 8, wherein predicting the location of the UE (500) by the location prediction model (184) based on the collated UE measurements comprises predicting at least one of a distance from each TRP of the at least three TRPs (600a-c) and predicting an angle of reception of the SRS at each TRP of the at least three TRPs (600a-c) (par. 192 angle par. 196 distance par. 123 atleast three base stations)
Claim 3. The combination teaches The method as claimed in claim 1, wherein receiving, by the Near RT RIC (100), the collated UE measurements associated with the UE (500), from the access node (1000) over the E2 interface comprises:
receiving, by each TRP of at least three TRPs (600a-c) (600a-c), sounding reference signal (SRS) from the UE (500); determining, by each TRP of the at least three TRPs (600a- c), the UE measurements comprising at least a time of reception of the SRS from the UE (500);
sending, by each TRP of the at least three TRPs (600a-c), the UE measurements to the access node (1000); receiving, by the access node (1000), the UE measurements sent by each TRP of the at least three TRPs (600a-c); determining, by the access node (1000), the collated UE measurements associated with the UE (500) using the received UE measurements; sending, by the access node (1000), the collated UE measurements associated with the UE to the Near RT RIC (100) over the E2 interface; and receiving, by the Near RT RIC (100), the collated UE measurements associated with the UE (500) from the access node (1000) ([0168] “..For example, a first antenna, a second antenna, and a third antenna of the first base station 304 can obtain SRS data from the device 302. In some examples, the first base station 304 can determine a first TOA measurement based on reception of the SRS data by the first antenna, a second TOA measurement based on reception of the SRS data by the second antenna, and a third TOA measurement based on reception of the SRS data by the third antenna. In some examples, the location engine circuitry 200 can obtain the first, second, and/or third TOA measurements from the first base station 304, and determine a TDOA measurement based on the first, second, and/or third TOA measurements…” and pars 280-281 disclosing use of E2 interface to transfer data to NR-RT RIC, and Location Element LE).
Claim 5. The combination teaches The method as claimed in claim 1, wherein the location prediction model (184) is performed by Non-Real time RIC (200) (par. 141 teaching training location models, par. 143-149 detailing training steps; See also par. 101 teaching location engine circuitry 200 may be implemented in any of the various cloud or core devices CU RU Dus etc; See also par. 183 non-RT-RIC implementation) is trained by:
receiving, by the Non-Real time RIC (200), observed time difference of arrival (OTDOA) measurements indicating an exact location of the UE (500) from an Enhanced Serving Mobile Location Centre (E-SMLC) (2000) (Parker in combination with RANGANATH ¶167 “In some examples, ML model training can be performed by the non-RT RIC 912 and/or the near-RT RIC 914” thus when training performed by non-RT RIC it is obvious that all training data, See Parker ¶147 training data = TDOA measurements from any data source, is sent to non-RT RIC and received by non-RT RIC) periodically (Parker ¶228, ¶230 “ location determination of the UE 1602 can be determined based on a measurement periodicity configured by the LMF xAPP 1626. ..”)
receiving, by the Non-Real time RIC (200), collated UE measurements associated with a plurality of UEs (500a-N) from the Near RT RIC (100) over A1 interface periodically (¶186 “The PLDC 616 can obtain the cellular data at a particular or specified frequency, rate, etc., and provide the cellular data to the near-RT-RIC 618. The near-RT-RIC 618 can determine a location of the UE 602 based on the cellular data. The near-RT-RIC 618 can provide the location to one(s) of the other xAPPs and/or one(s) of the one or more rAPPs of the SMO service 620.” Where SMO service 620 is implemented at Non-RT RIC see Fig. 6; ¶238 teaching collating plurality of UEs by near-RT RIC); and
training, by the Non-Real time RIC (200) (See Ranaganath ¶167 supra), the location prediction model (184) based on the OTDOA measurements received from the E-SMLC (2000) and the collated UE measurements associated with the plurality of UEs (500a-N) from the Near RT RIC (100) (Parker ¶147 “.., the location engine circuitry 200 facilitates the training of the ML model(s) 296 using training data. In some examples, the location engine circuitry 200 utilizes (¶147 teaching training data “locally generated data, such as 5G L1 data, SRS data, TOA data, TOA measurements, TDOA data, TDOA measurements, AOA data, AOA measurements, radio identifiers, CIR data, SNR data, etc. In some examples, the location engine circuitry 200 utilizes training data that originates from externally generated data. For example, the location engine circuitry 200 can utilize L1 data, L2 data, etc., from any data source (e.g., a RAN system, a satellite, etc.).”); par. 182 “..specified periodicity..”).
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 Parker to include the noted teachings of RANGANATH in order to train using the “combination of the non-RT RIC 912 and SMO 902 where intensive computation means can be made available.” Ranganath ¶167.
Claims 7 and 13. The combination teaches The Near RT RIC (100) as claimed in claim 8, wherein the location management controller (180) is further configured to:
determine at least one of a policy for the UE (500) and a configuration for the UE (500) (par. 75 “..precise location and positioning services are needed for a range of services including situational aware content, autonomous-remote-control vehicles, and new 911 regulatory requirements..”) based on location information of the UEs over the E2 interface (par. 281 “..the near-RT RIC can be the logical node that enables near-RT control and/or optimization of RAN elements and resources via fine-grained data collection via the PLDC and actions over the E2 interface…”).
Claim 10. The combination teaches The Near RT RIC (100) as claimed in claim 8, wherein the collated UE measurements associated with the UE (500) is generated by the access node (1000) using UE measurements sent by each TRP of the at least three TRPs (600a-c) connected to the UE (500) and wherein the UE measurements comprises at least a time of reception of the SRS from the UE (500).
SEE CLAIM 3, REJECTION TEACHES ALL ELEMENTS
Claim 11. Parker also teaches The Near RT RIC (100) as claimed in claim 10, wherein the UE measurements is determined by each TRP of the at least three TRPs (600a-c) based on sounding reference signal (SRS) received from the UE (500) at each TRP of the at least three TRPs (600a-c).
SEE CLAIM 3, REJECTION TEACHES ALL ELEMENTS
Claim 14. The combination teaches A Non-Real time RAN Intelligent Controller (Non RT RIC) (200) for training a location prediction model (184) in a wireless communication network, wherein the Non RT RIC (200) comprises:
a memory (220);
a processor (240) coupled to the memory (220);
a communicator (260) coupled to the memory (220) and the processor (240); and
a model training controller (280) coupled to the memory (220), the processor (240) and the communicator (260), and wherein the model training controller (280) is configured to:
receive observed time difference of arrival (OTDOA) measurements indicating an exact location of a UE (500) from an Enhanced Serving Mobile Location Centre (E-SMLC) (2000) periodically;
receive collated UE measurements associated with a plurality of UEs (500a-N) from a Near RT RIC (100) over Al interface periodically; and
train a location prediction model (184) based on the OTDOA measurements received from the E-SMLC (2000)
and the collated UE measurements associated with the plurality of UEs (500a-N) from the Near RT RIC (100).
SEE CLAIM 5, REJECTION TEACHES ALL ELEMENTS
Claim 15. The combination teaches The Non RT RIC as claimed in claim 14, wherein the model training controller (280) is further configured to:
deploy the location prediction model (184) at the Near RT RIC (100) over the Al interface to predict a location of the UE (500) by the location prediction model (184) based on the collated UE measurements.
SEE CLAIM 6, REJECTION TEACHES ALL ELEMENTS
Claim 16. The combination teaches The Non RT RIC as claimed in claim 14, wherein the location prediction model (184) predicts the location of the UE (500) based on the collated UE measurements comprises predicting at least one of a distance from each TRP of at least three TRPs (600a-c) and predicting an angle of reception of the SRS at each TRP of at least three TRPs (600a-c).
SEE CLAIM 2, REJECTION TEACHES ALL ELEMENTS
Additional Art of Record
Examiner makes the following art on the record but not used for the current rejection:
US 2022/0353691 A1 ¶¶ [0058], [0096], [0121]
US 20220256315 A1 LEI; Jing et al. [0077] [0078]
US 20230284178 A1 Parker; Valerie et al.
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 extension fee 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 date of this final action.
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/UMAIR AHSAN/Primary Examiner, Art Unit 2647