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 .
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)(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, 4, 7-9 & 19-21 are rejected under 35 U.S.C. 102a2 as being anticipated by Bedekar et al. WO 2019/141894 A1.
Claim 1:
Bedekar discloses a method performed in a radio access network (RAN) for Physical Uplink Control Channel (PUCCH) format configuration of a user equipment (UE) currently being served by a network node in the RAN (See fig. 2-3 and page 41, lines 3-14, the PUCCH sizing configuration is performed by the RAN), the method comprising: obtaining information, the information comprising at least one of: UE information about the UE currently being served by the network node in the RAN or network information about the RAN currently serving the UE (See fig. 4 and 5 page 41, lines 13-25, for one or more UE provide speed information, PUCCH resource allocation information, UL data, number of RRC connected UEs and UL/DL throughput); processing the obtained information using a machine learning model (See page 39, line 29 to page 40 line 3, “RAN controller to learn the relevant characteristics of each cell based on statistical analysis or machine learning applied to the data parameter set”); selecting a PUCCH format configuration from a plurality of PUCCH format configurations based on the processing (See fig. 4-5, the PUCCH size is determined); and determining whether to initiate a configuration of the UE to the selected PUCCH format configuration (See page 40, lines 5-6, the size of the PUCCH is configured).
Claim 4:
Bedekar discloses transmitting a message to initiate a configuration of the UE to the selected PUCCH format configuration (See fig. 4 step 407, communicate updated PUCCH size).
Claim 7:
Bedekar discloses that the UE information comprises: a measurement related to a location and/or speed of the UE (See fig. 4 and 5 page 41, lines 13-25, for one or more UE provide speed information).
Claim 8:
Bedekar discloses that the network information comprises: a measurement related to a location and/or speed of the UE (See fig. 4 and 5 page 41, lines 13-25, for one or more UE provide speed information).
Claim 9:
Bedekar discloses that the selecting the PUCCH configuration format comprises: obtaining an output from the machine learning model, wherein the output indicates the selected PUCCH format configuration (See page 39, line 29 to page 40 line 6, “This may furthermore enable the RAN controller to learn the relevant characteristics of each cell, based on statistical analysis or machine learning applied to the data parameter set. From this determination of the relevant characteristics of each cell, the RAN controller may then be configured to communicate with the RAN to make (optimal) adjustments to the PUCCH sizing”).
Claim 19:
Bedekar discloses obtaining one or more measurements of the UE or a communication channel between the UE and the network node from the UE after the UE has been configured with the selected PUCCI format configuration (See page 39, line 29 to page 40 line 6, “This may furthermore enable the RAN controller to learn the relevant characteristics of each cell, based on statistical analysis or machine learning applied to the data parameter set. From this determination of the relevant characteristics of each cell, the RAN controller may then be configured to communicate with the RAN to make (optimal) adjustments to the PUCCH sizing”); and determining a success or failure of the selected PUCCH format based on the one or more measurements (See page 39, line 29 to page 40 line 6, “This may furthermore enable the RAN controller to learn the relevant characteristics of each cell, based on statistical analysis or machine learning applied to the data parameter set. From this determination of the relevant characteristics of each cell, the RAN controller may then be configured to communicate with the RAN to make (optimal) adjustments to the PUCCH sizing”).
Claim 20:
Bedekar discloses that the one or more measurements comprises one or more of: a traffic throughput of the communication channel between the UE and the network node; a buffer-status report (BSR) of the UE; an amount of physical resource blocks (PRBs) scheduled in one or more cells of the RAN; a number of physical resources in downlink control channel allocated for uplink scheduling grants; or a discontinuous transmission (DTX) rate (See fig. 4 and 5 page 41, lines 13-25, for one or more UE provide speed information, PUCCH resource allocation information, UL data, number of RRC connected UEs and UL/DL throughput).
Claim 21:
Bedekar discloses updating the machine learning model based on the determining the success or failure of the selected PUCCH format or a success or failure of one or more selected PUCCH formats (See page 39, line 29 to page 40 line 6, “This may furthermore enable the RAN controller to learn the relevant characteristics of each cell, based on statistical analysis or machine learning applied to the data parameter set. From this determination of the relevant characteristics of each cell, the RAN controller may then be configured to communicate with the RAN to make (optimal) adjustments to the PUCCH sizing”).
Allowable Subject Matter
Claims 10, 12 and 14-17 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.
Response to Arguments
Applicant's arguments filed 07/13/2026 have been fully considered but they are not persuasive. With regards to claim 1 and its corresponding claims, on page 2 of the applicant’s remarks, the Applicant argues that Bedekar does not disclose determination of whether to initiate configuration of UE according to the selected PUCCH format or not”.
The examiner respectfully disagrees. Badekar’s page 40 lines 5-6, the RAN determines whether to change the size of the PUCCH based on the conditions in the cell. This changing of the size corresponds to “initiating a configuration of the UE to the selected PUCCH format configuration”.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASHIM S BHATTI whose telephone number is (571)270-7748. The examiner can normally be reached Mon-Fri 9:00am-5:30pm.
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HASHIM S. BHATTI
Primary Examiner
Art Unit 2472
/HASHIM S BHATTI/Primary Examiner, Art Unit 2475
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