DETAILED ACTION
This action is in response to applicant’s amendment filed on 16 June 2026. Claims 1, 5-7, 15, and 19-24 are now pending in the present application and claims 2-4, 8-14, and 16-18 are canceled.
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.
Election/Restrictions
Applicant’s election without traverse of claims 1-7 and 15-20 in the reply filed on 30 January 2026 is acknowledged.
Claims 8-14 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 30 January 2026.
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)(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.
Claim(s) 1, 5-7, 15, and 19-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vivo-3GPP (“Evaluation on AI/ML for CSI Feedback Enhancement”; 3GPP TSG RAN WG1 #110; R1-2206032).
Regarding claims 1, 15, and 23-24, Vivo discloses a method for channel state information (CSI) prediction by a user equipment (UE) in a wireless network { (see pg 1, section 1, 1st par.) }, the method comprising:
determining a channel state information (CSI) { (see pg. 1, section 1, 1st - 3rd agreement ), where the system provides CSI feedback (see pg. 7, section 2.1) };
inputting the determined CSI to at least one machine learning (ML) based CSI prediction model to obtain at least one predicted precoder { (see pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c), where the system provides CSI prediction };
determining a UE side precoder vector and a network apparatus side precoder vector based on the CSI { (see pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c), where the system provides CSI };
determining at least one candidate CSI to be reported for each channel rank indicator of a plurality of channel rank indicators based on the UE side precoder vector and the network apparatus side precoder vector { (see pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c), where the system provides CSI prediction };
determining at least one predicted CSI based on the at least one candidate CSI to be reported for each channel rank indicator of the plurality of channel rank indicators using the at least one machine learning, ML, based CSI prediction model { (see pg. 26, section 4.2.5; pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c) };
determining at least one predicted precoder for each channel rank indicator of the plurality of channel rank indicators based on the at least one predicted CSI { (see pg. 26, section 4.2.5; pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c) }
encoding the at least one predicted precoder into at least one bit stream using at least one ML based CSI encoding model or at least one non-ML based CSI encoding model { (see pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c), where the system provides CSI prediction }; and
transmitting the at least one encoded bit stream to a network apparatus in the wireless network for the CSI prediction at the network apparatus { (see pg. 1, section 1, 1st - 3rd agreement ), where the system provides CSI feedback (see pg. 7, section 2.1) }.
Regarding claims 2 and 16, Vivo discloses the method of claim 1, wherein inputting the determined CSI to at least one ML based CSI prediction model to obtain at least one predicted precoder comprises: determining at least one of a UE side precoder vector and a network apparatus side precoder vector based on the CSI; and inputting the UE side precoder vector and the network apparatus side precoder vector to the ML based CSI prediction model to obtain the at least one predicted precoder { (see pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c), where the system provides CSI prediction }.
Regarding claims 3 and 17, Vivo discloses the method of claim 1, wherein inputting the determined CSI to at least one ML based CSI prediction model to obtain at least one predicted precoder comprises: inputting the CSI to the ML based CSI prediction model to obtain a predicted CSI; and determining at least one of a UE side precoder vector and a network apparatus side precoder vector based on the predicted CSI { (see pg. 26, section 4.2.5; pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c), where the system provides CSI prediction }.
Regarding claims 4 and 18, Vivo discloses the method of claim 1, wherein inputting the determined CSI to at least one ML based CSI prediction model to obtain at least one predicted precoder comprises: determining at least one of a UE side precoder vector and a network apparatus side precoder vector based on the CSI; determining at least one candidate CSI to be reported for each channel rank indicator of a plurality of channel rank indicators based on the UE side precoder vector and the network apparatus side precoder vector; determining at least one predicted rank CSI based on the at least one candidate CSI to be reported for each channel rank indicator of the plurality of channel rank indicators using the at least one ML based CSI prediction model; and determining the at least one predicted precoder for each channel rank indicator of the plurality of channel rank indicators based on the at least one predicted CSI { (see pg. 26, section 4.2.5; pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c) }.
Regarding claims 5 and 19, Vivo discloses the method of claim 1, wherein the at least one non-ML based CSI encoding model comprises at least one of quantization of the at least one predicted precoder, a New Radio (NR) precoding Type I codebook, a NR precoding Type II codebook, or compressive sensing { (see pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c) }.
Regarding claims 6 and 20, Vivo discloses the method of claim 1, wherein the at least one encoded bit stream is transmitted to the network apparatus using a specified CSI reporting air interface { (see pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c) }.
Regarding claim 7, Vivo discloses the method of claim 1, further comprising: selecting an artificial intelligence model from a set of pre-trained encoders and decoders stored in a memory of the UE based on an AI model indicator received from the network apparatus for the ML based CSI encoding model{ (see pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c) }.
Regarding claims 21 and 22, Vivo discloses the method of claim 1, wherein inputting the determined CSI comprises determining a UE side precoder vector and a network apparatus side precoder vector based on the CSI { (see pg. 26, section 4.2.5; pg. 9, section 2.2.2; pp. 22-25, section 4.2.1; pp. 26-27, section 4.2.3; Figs. 2 &23a-b-c) }.
Response to Arguments
Applicant's arguments with respect to claims 1, 5-7, 15, and 19-24 are have been considered but are moot in view of the new ground(s) of rejection necessitated by the amended language, new limitations, and/or new claims.
In response to applicant’s arguments, the Examiner respectfully disagrees as the applied reference(s) provide more than adequate support and to further clarify (see the above claims for relevant citations and comments in this section).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liu (US 2026/0088870 A1) discloses CSI reporting method and apparatus, device, and system.
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 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 WILLIE J DANIEL JR whose telephone number is (571)272-7907. The examiner can normally be reached on 9 - 6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gary Mui can be reached on 571-270-1420. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIE J DANIEL JR/Primary Examiner, Art Unit 2465
WJD,Jr
28 August 2026
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