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
Claims 1-30 are pending in this application. Claims 1, 12, 16 and 28 have been amended, no claim has been canceled or newly added.
Response to Arguments
Applicant's arguments filed on Mar 03, 2026 have been fully considered but they are
not persuasive.
The Applicant alleged that Mellqvist et al (US20200344314A1) fails to anticipate "receive, from a base station, a configuration to report an out of distribution (OOD) dataset for a machine learning model, wherein the configuration includes at least one of data associated with the report or a set of instructions to refine the machine learning model" in Claim 1.
In response the Examiner respectfully disagrees because Mellqvist’314 discloses: “detector models may, for example, involve machine learning” (0034);
control data is indicative of at least one of a timestamp of the at least one anomaly, and a label associated with the at least one anomaly, the label being identified in accordance with a respective detector model used by the respective mobile device of the plurality of mobile devices for detecting the anomalies in the time series of measurement values (par 0025).
“label” as used herein may refer to an identifier that represents the at least one anomaly when detected using a detector model that may be preconfigured by the network node.
a label may be assigned to the at least one anomaly if the at least one anomaly is detectable using a network-configured detector model and therefore represents a “known anomaly pattern”. Different labels may correspond to different anomalies.
The labeled anomaly pattern may furthermore be associated with location information, meaning that the detector model not only detects an anomaly but also implicitly finds the current location of the mobile device.
Example labels include: road bump; left turn; right turn; highway entry; highway exit; speed bumps; etc. (par 0027-0030).
based on the respective uplink training control data and on the outcome of the comparison of anomalies indicated by the uplink training control data from the plurality of mobile devices, the respective detector model may be configured and also be further improved as more sensor data is captured in a live system. This may help to more reliable detect anomalies. Further, new types of anomalies can be trained. Respective labels may be assigned (par 0061).
Therefore Mellqvist’314 discloses: network node preconfigured/assigned label for known anomaly pattern used in uplink training control data (reporting known anomaly pattern), and improve detector model (machine learning model) according to uplink training control data from mobile devices. And thus Mellqvist’314 discloses: receive, from a base station, a configuration to report an out of distribution (OOD) dataset for a machine learning model, wherein the configuration includes at least one of data associated with the report or a set of instructions to refine the machine learning model. Here preconfigured/assigned label can be equated to configuration includes data associated with the report.
Therefore, the cited references teach the claimed limitations in claim 1 in question with adequate reasons and suggestions of combining the teachings. The same conclusion applies to claims 12, 16 and 28.
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 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 1-7, 9-13, 15-23, 25-28 and 30 are rejected under 35 U.S.C. 102(a1) as being anticipated by Mellqvist et al (US20200344314A1).
Regarding claim 1 (Currently Amended), Mellqvist’314 discloses an apparatus for wireless communication at a user equipment (UE) (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), comprising:
a memory (memory, par 0079);
a transceiver (Fig. 6, wireless interface to receive and transmit, par 0141, 0144); and
a processor (Fig. 6, processor, par 0140), communicatively connected to the memory and the transceiver (processor coupled to memory and wireless interface, par 0079, 0141), the processor configured to (Fig. 6, processor adapt to, par 0142):
receive, from a base station (Fig. 3, network node can be equated to base station, par 0027, 0119), a configuration (pre-scheduled resources for periodic reporting or dedicated resources assigned for aperiodic reporting and label to report known anomaly pattern can be equated to configuration, par 0025, 0027, 0073-0074) to report an out of distribution (OOD) dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location and known anomaly pattern in report can be equated to OOD dataset, par 0017, 0025, 0027, 0104) for a machine learning model (see, mobile device receives from network node the resource configuration for reporting anomalies and preconfigured/assigned label to report known anomaly pattern for detector model (machine learning model), par 0017, 0025, 0027, 0034, 0073-0074), wherein the configuration (preconfigured/assigned label for known anomaly pattern used in uplink training control data can be equated to configuration includes data associated with the report, par 0025, 0027-0030) includes at least one of data associated with the report or a set of instructions to refine the machine learning model (see, preconfigured/assigned label by network node for known anomaly pattern in reported uplink training control data used to improve detector model (machine learning model), par 0025, 0027, 0034, 0061. Noted, the examiner picks an option to reject);
detect an occurrence of one or more OOD (anomalies (refers to events in a given dataset with unexpected pattern) can be equated to OOD events, par 0017) events (see, Fig. 1 step 12, mobile device detects anomalies, par 0102);
report the OOD dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) comprising the one or more OOD events (anomalies (refers to events in a given dataset with unexpected pattern) can be equated to OOD events, par 0017) based on the configuration (pre-scheduled resources for periodic reporting or dedicated resources assigned for aperiodic reporting can be equated to configuration, par 0073-0074) to report OOD dataset (see, Fig. 1 step 15A, mobile device selects periodic or aperiodic report using corresponding configured periodic or aperiodic resources to report anomalies with corresponding timestamp, measurement values and measured location, par 0017, 0073-0074, 0104, 0106); and
receive, from the base station (Fig. 3, network node can be equated to base station, par 0119), an update (update and further improve the detector model by configuration can be equated to update, par 0061, 0126) to the machine learning (detector model using machine learning can be equated to machine learning model, par 0034) model (see, mobile device receives parameter of the detector model from network node to update and further improve the detector model (using machine learning) in a live system, par 0034, 0058, 0061, 0126).
Regarding claim 2 (Original), Mellqvist’314 discloses the apparatus of claim 1 (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), wherein the OOD dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) comprises raw data (portion of the time series of measurement values can be equated to raw data, par 0104) related to the one or more OOD (anomalies (refers to events in a given dataset with unexpected pattern) can be equated to OOD events, par 0017) events (see, Fig. 1 step 15A, mobile device reports anomalies with corresponding timestamp, measurement values and measured location, par 0017, 0104, 0106).
Regarding claim 3 (Original), Mellqvist’314 discloses the apparatus of claim 1 (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), wherein the OOD dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) comprises extracted latent data (label indicates known anomaly pattern can be equated to latent data corresponding to features of raw data related to the one or more OOD, par 0131) corresponding to features (pattern of peaks and/or dips in measurement values can be equated to features, par 0017) of raw data (measurement value can be equated to raw data, par 0017) related to the one or more OOD (anomalies (refers to events in a given dataset with unexpected pattern) can be equated to OOD events, par 0017) events (see, mobile device reports anomalies comprises label with each label indicating a known anomaly pattern, par 0017, 0131).
Regarding claim 4 (Original), Mellqvist’314 discloses the apparatus of claim 3 (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), wherein the configuration (assigned labels to new type of anomalies for network-configured detector model, par 0028, 0061) to report (see, report anomalies can be equated to report OOD, par 0104) the OOD dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location can be equated to OOD dataset, par 0017, 0104) includes instructions (assigned labels to new type of anomalies can be equated to instruction for obtaining the extracted latent data, par 0028, 0061) for obtaining the extracted latent (label indicates known anomaly pattern can be equated to latent data, par 0131) data (see, mobile devices receives assigned labels to new types of anomalies used in reporting anomalies to indicate different known anomaly patterns, par 0017, 0028, 0061, 0104, 0131).
Regarding claim 5 (Original), Mellqvist’314 discloses the apparatus of claim 1 (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), wherein the configuration (group sensor reporting assignments can be equated to configuration, par 0024) to report (see, report anomalies can be equated to report OOD, par 0104) the OOD dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location can be equated to OOD dataset, par 0017, 0104) includes a content configuration (group sensor report assignments can be equated to configuration, par 0024) for a type of content (various group sensor reporting assignment including temperature, humidity and location can be equated to type of content, par 0024) included in the OOD dataset (see, network node assigns plurality of mobile devices into sensor groups for various group sensor reporting anomalies with temperature, humidity or location, par 0024, 0110, 0112. Noted, reported measured data by sensor can be temperature, humidity or location, and thus a type of content, par 0024- 0025).
Regarding claim 6 (Original), Mellqvist’314 discloses the apparatus of claim 1 (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), wherein reporting the OOD dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) is based on a schedule within the configuration (pre-scheduled resources for periodic reporting or dedicated resources assigned for aperiodic reporting can be equated to schedule within the configuration, par 0073-0074) to report the OOD dataset (see, Fig. 1 step 15A, mobile device selects periodic or aperiodic report for anomalies using corresponding configured periodic or aperiodic resources and reports anomalies with corresponding timestamp, measurement values and measured location, par 0017, 0073-0074, 0104, 0106).
Regarding claim 7 (Original), Mellqvist’314 discloses the apparatus of claim 6 (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), wherein the schedule (pre-scheduled resources for periodic reporting or dedicated resources assigned for aperiodic reporting can be equated to schedule, par 0073-0074) comprises a periodic pattern (pre-scheduled periodic resources can be equated to periodic pattern, par 0074) for the reporting the OOD (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) dataset (see, pre-scheduled periodic resources for mobile device to report anomalies with corresponding timestamp, measurement values and measured location, par 0017, 0074, 0104, 0106).
Regarding claim 9 (Original), Mellqvist’314 discloses the apparatus of claim 1 (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), wherein the OOD dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) is reported in response to the occurrence of each OOD (anomalies (refers to events in a given dataset with unexpected pattern) can be equated to OOD events, par 0017) event (see, Fig. 1 step 12 and 15A, mobile device detects at least one anomaly in the respective time series of measurement values and reports the at least one anomaly with timestamp, measurement values and measured location, par 0102-0104).
Regarding claim 10 (Original), Mellqvist’314 discloses the apparatus of claim 1 (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), wherein to report the OOD dataset (see, Fig. 1 step 15A, mobile device selects periodic or aperiodic report using corresponding configured periodic or aperiodic resources to report anomalies with corresponding timestamp, measurement values and measured location, par 0017, 0073-0074, 0104, 0106), the processor (Fig. 6, processor, par 0140) is further configured to:
transmit a request (uplink scheduling request for aperiodic reporting anomalies can be equated to request, par 0073) to report the OOD (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) dataset (see, mobile device sends uplink scheduling request for aperiodic reporting anomalies, par 0073);
receive a grant (downlink scheduling grant can be equated to grant, par 0073) to report the OOD (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) dataset (see, mobile device receives downlink scheduling grant for aperiodic reporting anomalies, par 0073); and
transmit the OOD dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) based on the grant (see, Fig. 1 step 15A, mobile device selects aperiodic report using corresponding granted aperiodic resources to report anomalies with corresponding timestamp, measurement values and measured location, par 0017, 0073, 0104, 0106).
Regarding claim 11 (Original), Mellqvist’314 discloses the apparatus of claim 1 (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), wherein the update (update the algorithm/method of the detector model can be equated to update, par 0126) to the machine learning model (detector model using machine learning can be equated to machine learning model, par 0034) comprises a new machine learning model or a difference (see, update to one parameter out of multiple parameters can be equated to difference, par 0126) to the machine learning model (see, update detector model using machine learning by configurating one parameter of detector model from default detector model with multiple parameters, par 0104, 0126. Noted, the examiner picks option to reject).
Regarding claim 12 (Currently Amended), Claim 12 recites a method of wireless communication at a user equipment (UE) performing the steps recited in claim 1 and thereby, is rejected for the reasons discussed above with respect to claim 1.
Regarding claim 13 (Original), Claim 13 recites a method of wireless communication at a user equipment (UE) performing the steps recited in claims 2 & 3 and thereby, is rejected for the reasons discussed above with respect to claims 2 & 3.
Regarding claim 15 (Original), Claim 15 recites a method of wireless communication at a user equipment (UE) performing the steps recited in claim 10 and thereby, is rejected for the reasons discussed above with respect to claim 10.
Regarding claim 16 (Currently Amended), Mellqvist’314 discloses an apparatus for wireless communication at a base station (Fig. 3 and 7, mobile devices detects and reports anomalies to network node, par 0117-0122), comprising:
a memory (see, memory, par 0079);
a transceiver (see, Fig. 7, network interface to receive and transmit, par 0073, 0148); and
a processor (Fig. 7, processor, par 0148), communicatively connected to the memory and the transceiver (processor coupled to memory and network interface, par 0079, 0148), the processor configured to (Fig. 7, processor adapted to, par 0149):
transmit, to a user equipment (UE) (mobile device can be equated to UE, par 0025, 0073), a configuration (pre-scheduled resources for periodic reporting or dedicated resources assigned for aperiodic reporting and label to report known anomaly pattern can be equated to configuration, par 0025, 0027, 0073-0074) to report an out of distribution (OOD) dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location and known anomaly pattern in report can be equated to OOD dataset, par 0017, 0025, 0027, 0104) for a machine learning model (see, network node configures mobile device with the resource configuration for reporting anomalies and preconfigured/assigned label to report known anomaly pattern for detector model (machine learning model), par 0017, 0025, 0027, 0034, 0073-0074), wherein the configuration (preconfigured/assigned label for known anomaly pattern used in uplink training control data can be equated to configuration includes data associated with the report, par 0025, 0027-0030) includes at least one of data associated with the report or a set of instructions to refine the machine learning model (see, preconfigured/assigned label by network node for known anomaly pattern in reported uplink training control data used to improve detector model (machine learning model), par 0025, 0027, 0034, 0061. Noted, the examiner picks an option to reject);
transmit, to a user equipment (UE), a configuration (pre-scheduled resources for periodic reporting or dedicated resources assigned for aperiodic reporting can be equated to configuration, par 0073-0074) to report an out of distribution (OOD) dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) for a machine learning (detector model using machine learning can be equated to machine learning model, par 0034) model (see, network node transmits to mobile device the resource configuration for reporting anomalies detected using machine learning model, par 0017, 0034, 0073-0074);
receive, from the UE, the OOD dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) comprising one or more OOD events (anomalies (refers to events in a given dataset with unexpected pattern) can be equated to OOD events, par 0017) based on the configuration (pre-scheduled resources for periodic reporting or dedicated resources assigned for aperiodic reporting can be equated to configuration, par 0073-0074) to report OOD dataset (see, Fig. 1 step 15A, network node receives from mobile device the anomaly report after mobile device selects periodic or aperiodic report using corresponding configured periodic or aperiodic resources to report anomalies with corresponding timestamp, measurement values and measured location, par 0017, 0073-0074, 0104, 0106);
update (update and further improve the detector model by configuration can be equated to update, par 0061, 0126) the machine learning model (detector model using machine learning can be equated to machine learning model, par 0034) based on the OOD dataset (see, network node updates and further improves the detector model (using machine learning) in a live system, par 0034, 0058, 0061, 0126); and
transmit, to the UE, an update to the machine learning model (see, mobile device receives parameter of the detector model from network node to update and further improve the detector model (using machine learning) in a live system, par 0034, 0058, 0061, 0126).
Regarding claim 17 (Original), Mellqvist’314 discloses the apparatus of claim 16 (Fig. 3 and 7, mobile devices detects and reports anomalies to network node, par 0117-0122), wherein the processor is further configured to (Fig. 7, processor adapted to, par 0149):
configure the configuration (downlink scheduling grant to aperiodic reporting anomalies can be equated to configuration to report OOD, par 0073) to report the OOD dataset (anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location in report can be equated to OOD dataset, par 0017, 0104) for the machine learning (detector model using machine learning can be equated to machine learning model, par 0034) model (see, network node sends downlink scheduling grant for resources to aperiodic reporting anomalies detected using network-configured detector model, par 0017, 0073, 0104. Noted, downlink scheduling grant for resources to report anomalies and thus configure the configuration for resource, par 0073).
Regarding claim 18 (Original), Claim 18 recites an apparatus for wireless communication at a base station performing the steps recited in claim 2 and thereby, is rejected for the reasons discussed above with respect to claim 2.
Regarding claim 19 (Original), Claim 19 recites an apparatus for wireless communication at a base station performing the steps recited in claim 3 and thereby, is rejected for the reasons discussed above with respect to claim 3.
Regarding claim 20 (Original), Claim 20 recites an apparatus for wireless communication at a base station performing the steps recited in claim 4 and thereby, is rejected for the reasons discussed above with respect to claim 4.
Regarding claim 21 (Original), Claim 21 recites an apparatus for wireless communication at a base station performing the steps recited in claim 5 and thereby, is rejected for the reasons discussed above with respect to claim 5.
Regarding claim 22 (Original), Claim 22 recites an apparatus for wireless communication at a base station performing the steps recited in claim 6 and thereby, is rejected for the reasons discussed above with respect to claim 6.
Regarding claim 23 (Original), Claim 23 recites an apparatus for wireless communication at a base station performing the steps recited in claim 7 and thereby, is rejected for the reasons discussed above with respect to claim 7.
Regarding claim 25 (Original), Claim 25 recites an apparatus for wireless communication at a base station performing the steps recited in claim 9 and thereby, is rejected for the reasons discussed above with respect to claim 9.
Regarding claim 26 (Original), Claim 26 recites an apparatus for wireless communication at a base station performing the steps recited in claim 10 and thereby, is rejected for the reasons discussed above with respect to claim 10.
Regarding claim 27 (Original), Claim 27 recites an apparatus for wireless communication at a base station performing the steps recited in claim 11 and thereby, is rejected for the reasons discussed above with respect to claim 11.
Regarding claim 28 (Currently Amended), Claim 28 recites a method of wireless communication at a base station performing the steps recited in claim 16 and thereby, is rejected for the reasons discussed above with respect to claim 16.
Regarding claim 30 (Original), Claim 30 recites a method of wireless communication at a base station performing the steps recited in claim 10 and thereby, is rejected for the reasons discussed above with respect to claim 10.
Claim Rejections - 35 USC § 103
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 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 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 col. 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 8, 14, 24 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Jha’458 in view of Sato et al (US20210248908A1, Priority Date: Feb 7, 2020).
Regarding claim 8 (Original), Mellqvist’314 discloses the apparatus of claim 1 (Fig. 3 and 6, mobile devices detects and reports anomalies to network node, par 0117-0122), the processor is further configured to (Fig. 6, processor adapt to, par 0142), reporting of the OOD dataset (see, reporting anomalies (refers to events in a given dataset with unexpected pattern) with timestamp, measurement values and measured location, par 0017, 0104)
Mellqvist’314 discloses all the claim limitations but fails to explicitly teach: receive a triggering message from the base station, wherein the reporting of the OOD is triggered in response to receipt of the triggering message.
However Sato’908 from the same field of endeavor (see, Fig. 1, vehicles (with RCMS agent) communicates with RCMS servers in networked system, par 0023- 0024) discloses:
receive a triggering message (instruction from RVCMS server can be equated to triggering message, par 0008) from the base station (see, vehicle receives instruction from RVCMS server to upload image of suspected obstacle (e.g. a pothole) concerning Abnormal vehicle events, par 0008), wherein the reporting of the OOD dataset (reporting of road conditions from abnormal vehicle events such as sudden braking, sharp turns, evasive actions and pothole impact etc. can be equated to reporting of the OOD dataset, par 0008) is triggered in response to receipt of the triggering message (see, vehicle uploads image of suspected obstacle (e.g. a pothole) concerning abnormal vehicle events (as reporting to abnormal vehicle events) upon receiving instruction from RVCMS server, par 0008).
In view of the above, it would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains to implement the apparatus as taught by Sato’908 into that of Mellqvist’314. The motivation would have been to improve navigation information and driving by crowdsourcing between participants to achieve a cumulative result (par 0002-0003).
Regarding claim 14 (Original), Claim 14 recites a method of wireless communication at a user equipment (UE) performing the steps recited in claim 8 and thereby, is rejected for the reasons discussed above with respect to claim 8.
Regarding claim 24 (Original), Claim 24 recites an apparatus for wireless communication at a base station performing the steps recited in claim 8 and thereby, is rejected for the reasons discussed above with respect to claim 8.
Regarding claim 29 (Original), Claim 29 recites a method of wireless communication at a base station performing the steps recited in claim 8 and thereby, is rejected for the reasons discussed above with respect to claim 8.
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 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUAN LU whose telephone number is (571)272-2844. The examiner can normally be reached on Monday - Friday 7:30am-5:30pm.
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/XUAN LU/Primary Examiner, Art Unit 2473