Prosecution Insights
Last updated: April 19, 2026
Application No. 18/691,116

METHOD AND APPARATUS FOR MULTIPLE-INPUT AND MULTIPLE-OUTPUT (MIMO) CHANNEL STATE INFORMATION (CSI) FEEDBACK

Final Rejection §103
Filed
Mar 12, 2024
Examiner
FOTAKIS, ARISTOCRATIS
Art Unit
2633
Tech Center
2600 — Communications
Assignee
MediaTek Inc.
OA Round
2 (Final)
71%
Grant Probability
Favorable
3-4
OA Rounds
2y 11m
To Grant
99%
With Interview

Examiner Intelligence

Grants 71% — above average
71%
Career Allow Rate
531 granted / 745 resolved
+9.3% vs TC avg
Strong +31% interview lift
Without
With
+30.8%
Interview Lift
resolved cases with interview
Typical timeline
2y 11m
Avg Prosecution
35 currently pending
Career history
780
Total Applications
across all art units

Statute-Specific Performance

§101
4.3%
-35.7% vs TC avg
§103
53.6%
+13.6% vs TC avg
§102
19.3%
-20.7% vs TC avg
§112
16.5%
-23.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 745 resolved cases

Office Action

§103
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 Arguments Applicant’s amendment, filed January 14, 2026, with respect to the rejections of claims have been fully considered. Applicant's amendment necessitated the new grounds of rejection presented below by introducing the new references of Hajri et al (US 2023/0361842) and O’Shea et al (US 2018/0367192). 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. The factual inquiries 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. 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. Claim 6 – 9 are rejected under 35 U.S.C. 103 as being unpatentable over Sangdeh et al (“LB-SciFi: Online Learning-Based Channel Feedback for MU-MIMO in Wireless LANs”, Michigan State University, University of Houston, 978-1-7281-6992-7/20, 2020 IEEE) in view of Hajri et al (US 2023/0361842). Re claim 6, Sangdeh teaches of a method for channel state information (CSI) feedback, the method comprising: collecting, at a base station (BS), CSI data of a communication channel between the BS and a user equipment (UE) (Figures 1 – 2 and 10, collected CSI at the AP, Col 1, Paragraph 1, Page 2 and B.Online Training: data collection, Col 2, Page 4), wherein the UE is configured with an encoder model to compress CSI and the BS is configured with a decoder model to decompress CSI (DNN-AE as shown in Fig. 1, which is composed of two parts: encoder and decoder. The encoder will be used on each STA to compress its estimated CSI for feedback, and the decoder will be used to recover CSI at the AP, Col 1, Paragraph 1, Page 4 and I.V LB-SCIFI, Col 2, Page 3); and performing, at the base station and based on the collected CSI data, online training on a previously trained encoder-decoder model pair including the encoder model and the decoder model (the AP trains the DNN-AEs using reported CSI (uncompressed Ψ and Φ) from the STAs, Col 2, Page 4) to generate updated models for the encoder and the decoder, respectively (Updating DNN-AEs, Page 6). However, Sangdeh does not specifically teach of receiving, at the base station (BS), a reference signal from the user equipment (UE) measuring, at the BS, CSI data of a communication channel between the BS and the UE based on the reference signal without receiving any CSI report from the UE. Hajri teaches of receiving, at the BS, a reference signal from the UE (SRS, Fig.5); and measuring, at the BS, the CSI data based on the reference signal (#302a, #302b and #304, Fig.5 and Paragraph 0047) without receiving any CSI report from the UE (the measuring, at the BS, of the CSI data is without receiving any CSI report from the UE, Fig.5). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have measured, at the BS, the CSI data based on the reference signal from the UE in a reciprocal channel for accurate CSI measurements and so as to reduce the computational strain on the UE. Re claim 7, Sangdeh teaches of further comprising: sending, from the BS to the UE, the updated model for the encoder model (Col 2, Paragraph 1, Page 6); updating, at the BS, the decoder model of the BS based on the updated model for the decoder model (update the DNN-AEs, Col 2, Page 7 and Col 2, Page 6); and decompressing, at the BS, a compressed CSI element based on the updated decoder model (Fig.10). Re claim 8, Sangdeh teaches of wherein the encoder model of the UE is updated based on the updated model for the encoder model sent from the BS (update the DNN-AEs, Col 2, Page 7 and Col 2, Page 6), and wherein the compressed CSI element is generated by the UE based on the updated encoder model (CSI compression, Fig.10). Re claim 9, Sangdeh teaches of wherein the updated models include at least partial parameters of the encoder model and the decoder model (parameters, Col 2, Paragraph 1, Page 6). Claims 12 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sangdeh in view of O’Shea et al (US 2018/0367192). Re claim 12, Sangdeh teaches of a method for channel state information (CSI) feedback, the method comprising: collecting, at a base station, CSI data of a communication channel between a user equipment (UE) and a base station (BS) (Figures 1 – 2 and 10, collected CSI at the AP, Col 1, Paragraph 1, Page 2 and B.Online Training: data collection, Col 2, Page 4), wherein the UE is configured with an encoder model to compress CSI and the BS is configured with a decoder model to decompress CSI (DNN-AE as shown in Fig. 1, which is composed of two parts: encoder and decoder. The encoder will be used on each STA to compress its estimated CSI for feedback, and the decoder will be used to recover CSI at the AP, Col 1, Paragraph 1, Page 4 and I.V LB-SCIFI, Col 2, Page 3); and performing, at the base station and based on the collected CSI data, online training on a previously trained encoder-decoder model pair including the encoder model and the decoder model (the AP trains the DNN-AEs using reported CSI (uncompressed Ψ and Φ) from the STAs, Col 2, Page 4) to generate updated models for the encoder and the decoder, respectively (Updating DNN-AEs, Page 6). Sangdeh further teaches in a different embodiment that the collecting and performing steps are performed by a cloud server (“If an AP is not capable of doing the training by itself, it can take advantage of its wired Internet connection and a cloud server to run the training”, Col 2, Page 8). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have the collecting and performing steps performed by a cloud server so as to ease the computational complexity of training. Sangdeh does not specifically teach of wherein the CSI data is collected at the server from at least one of the BS and the UE. O’Shea teaches of a method for channel state information (CSI) feedback, the method comprising: collecting, at a server (#724, Fig.7, #916, Figures 9A – 9B and Paragraph 0177), CSI data of a communication channel (Paragraph 0152) between a user equipment (UE) and a base station (BS) (Paragraphs 0042, 0066 – 0067 and 0173 – 0175), wherein the UE is configured with an encoder model to encode CSI (encoder, Fig.9B) and the BS is configured with a decoder model to decode CSI (decoder, Fig.9B) (Paragraph 0061); and performing, at the server and based on the collected CSI data, training on a previously trained encoder-decoder model pair including the encoder model and the decoder model to generate updated models for the encoder and the decoder, respectively (previously trained encoder-decoder model pair is updated, See Figures 7 and 9A – 9B), wherein the CSI data is collected at the server from at least one of the BS (for DL encoding, Fig.9A) and the UE (for UL encoding, Fig.9A). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have the CSI data collected at the server from at least one of the BS and the UE so as to configure both the uplink and the downlink. Re claim 13, Sangdeh teaches of further comprising: sending, from the base station to the UE, the updated model for the encoder model (Col 2, Paragraph 1, Page 6), wherein the updated model for the decoder model at the base station is used by the BS to update the decoder of the BS (update the DNN-AEs, Pages 6 and 7). Sangdeh further teaches in a different embodiment that the collecting and performing steps are performed by a cloud server (“If an AP is not capable of doing the training by itself, it can take advantage of its wired Internet connection and a cloud server to run the training”, Col 2, Page 8). Therefore, one skilled in the art would have sent, from the server to the BS, the updated models for the encoder model and the decoder model so as to update the decoder model at the BS and the encoder model at the UE. It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have sent, from the server to the BS, the updated models for the encoder model and the decoder model so as to update the decoder model at the BS and the encoder model at the UE for efficient CSI compression. Re claim 14, Sangdeh teaches of wherein the updated model for the decoder model sent from the server (as taught by the different embodiment of Sangdeh, see claims 12 – 13) is used by the BS to update the decoder of the BS (update the DNN-AEs, Pages 6 and 7), and the updated model for the encoder model sent from the server is transmitted by the BS to the UE (Col 2, Paragraph 1, Page 6). Re claim 15, Sangdeh teaches of wherein the updated model for the encoder model transmitted from the BS is used by the UE to update the encoder model of the UE (Col 2, Paragraph 1, Page 6). Re claim 16, Sangdeh teaches of further comprising: sending, from the base station to the UE, the updated model for the encoder model (Col 2, Paragraph 1, Page 6), wherein the updated model for the decoder model at the base station is used by the BS to update the decoder of the BS (update the DNN-AEs, Pages 6 and 7). Sangdeh further teaches in a different embodiment that the collecting and performing steps are performed by a cloud server (“If an AP is not capable of doing the training by itself, it can take advantage of its wired Internet connection and a cloud server to run the training”, Col 2, Page 8), where the server generates the updated models for the encoder to be used in the UE and the decoder to be used in the base station (Updating DNN-AEs, Page 6). One skilled in the art could have sent the updated models to the UE needed by the UE to perform CSI compression. It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have sent, from the server to the UE, the updated models for the encoder model and the decoder model so as to efficiently perform CSI compression. Re claim 17, Sangdeh teaches of wherein the updated model for the encoder model sent from the server (as taught by the different embodiment of Sangdeh) is used by the UE to update the encoder model of the UE (update the DNN-AEs, Pages 6 and 7). One skilled in the art would have known that the UE would have been capable to transmit the updated model for the decoder model sent from the server to the BS. It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have the UE transmit the updated model for the decoder model sent from the server to the BS so as to perform CSI decompression. Re claim 18, Sangdeh teaches of wherein the updated model for the decoder model transmitted from the UE is used by the BS to update the decoder model of the BS (update the DNN-AEs, Pages 6 and 7). Re claim 19, Sangdeh teaches of further comprising: sending, from the base station to the UE, the updated model for the encoder model (Col 2, Paragraph 1, Page 6), wherein the updated model for the decoder model at the base station is used by the BS to update the decoder of the BS (update the DNN-AEs, Pages 6 and 7). Sangdeh further teaches in a different embodiment that the collecting and performing steps are performed by a cloud server (“If an AP is not capable of doing the training by itself, it can take advantage of its wired Internet connection and a cloud server to run the training”, Col 2, Page 8), where the server generates the updated models for the encoder to be used in the UE and the decoder to be used in the base station (Updating DNN-AEs, Page 6). Sangdeh does not specifically teach of further comprising: sending, from the server to the BS, the updated model for the decoder model; and sending, from the server to the UE, the updated model for the encoder model. However, one skilled in the art would have known that since the server performed the online training and generated updated models for the encoder of the UE and the decoder of the base station (Updating DNN-AEs, Page 6), it would be required to have the updated models for the UE and the base station communicated to the UE and the base station for CSI compression and CSI decompression, respectively. It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have the encoder model communicated to the UE and the decoder model communicated to base station so as to perform CSI compression and CSI decompression, respectively. 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 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 ARISTOCRATIS FOTAKIS whose telephone number is (571)270-1206. The examiner can normally be reached M-F 8:30am-5:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sam K Ahn can be reached at (571) 272-3044. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ARISTOCRATIS FOTAKIS/ Primary Examiner, Art Unit 2633
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Prosecution Timeline

Mar 12, 2024
Application Filed
Oct 14, 2025
Non-Final Rejection — §103
Jan 14, 2026
Response Filed
Mar 06, 2026
Final Rejection — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
71%
Grant Probability
99%
With Interview (+30.8%)
2y 11m
Median Time to Grant
Moderate
PTA Risk
Based on 745 resolved cases by this examiner. Grant probability derived from career allow rate.

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