Prosecution Insights
Last updated: August 17, 2026
Application No. 18/855,865

CHANNEL STATE INFORMATION PROCESSING METHODS AND APPARATUSES

Non-Final OA §103
Filed
Oct 10, 2024
Priority
Apr 12, 2022 — nonprovisional of PCTCN2022086462
Examiner
AHN, SAM K
Art Unit
2633
Tech Center
2600 — Communications
Assignee
Beijing Xiaomi Mobile Software Co., Ltd.
OA Round
2 (Non-Final)
86%
Grant Probability
Favorable
2-3
OA Rounds
1y 6m
Est. Remaining
96%
With Interview

Examiner Intelligence

Grants 86% — above average
86%
Career Allowance Rate
290 granted / 339 resolved
+23.5% vs TC avg
Moderate +10% lift
Without
With
+10.3%
Interview Lift
resolved cases with interview
Typical timeline
3y 4m
Avg Prosecution
9 currently pending
Career history
346
Total Applications
across all art units

Statute-Specific Performance

§101
13.7%
-26.3% vs TC avg
§103
39.5%
-0.5% vs TC avg
§102
17.8%
-22.2% vs TC avg
§112
20.8%
-19.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 339 resolved cases

Office Action

§103
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 arguments, see p.9, filed 4/23/2026, with respect to 101 rejection of claims 24 and 26 have been fully considered and are persuasive. The rejection of the claims has been withdrawn. Applicant’s arguments, see p.9-10, filed 4/23/2026, with respect to claim objection of claims 9, 11, 12, 14 and 26 have been fully considered and are persuasive. The objection of the claims has been withdrawn. Applicant’s arguments with respect to 102 rejection of claim(s) 1, 16, 24-26 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 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 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. Claim(s) 1, 3-7, 15, 16, and 24-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyung US 2025/0015844 (previously cited) in view of Tian US 2024/0323743 (priority PCT/CN2021/135940). Regarding claim 1, Kyung discloses a channel state information processing method (abstract), wherein the method is performed by a terminal device (UE;310), and the method comprises: determining a target compression algorithm (deep learning or machine learning) algorithm as determined target compression algorithm based on performance; para#54); compressing Channel State Information (CSI) using the target compression algorithm to obtain compressed CSI (para#54-55; obtaining CSI compression based the target compression algorithm of deep neural network or neural network); and sending the compressed CSI to a network device (para#55 transmitting the compressed CSI to base station;320). However, Kyung does not explicitly teach wherein the method further comprises: sending first indication information to the network device, wherein the first indication information is used to indicate the target compression algorithm. Tian teaches wherein the method further comprises: sending first indication information to the network device (sending to BS, see Fig.7A), wherein the first indication information is used to indicate the target compression algorithm (indication of CSI type, CSI information type, scenario type, channel feature index type, corresponding encoding/decoding type, note [0068]; compression algorithms [0072]). Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to incorporate the teaching of Tian into the system of Kyung of transmitting the first indication information for the purpose of improving MIMO transmission (note [0039]). Regarding claim 3, Tian further teaches wherein determining the target compression algorithm comprises: receiving an algorithm list sent by the network device, wherein the algorithm list comprises compression algorithms (see Fig.8A and note [0072]); and selecting the target compression algorithm from the compression algorithms in the algorithm list (see Fig.8A and note [0050] wherein channel state discriminative unit comprise algorithm list of compression algorithms [0072]). Regarding claim 4, Tian further teaches wherein determining the target compression algorithm comprises: receiving an algorithm list sent by the network device, wherein the algorithm list comprises compression algorithms and usage conditions corresponding to the compression algorithms (see Fig.8A and note [0072] different schemes for channel information compression, feedback and recovery to specific scenarios); and selecting the target compression algorithm from the compression algorithms according to the usage conditions corresponding to the compression algorithms (see Fig.8A and note [0050] wherein channel state discriminative unit comprise algorithm list of compression algorithms [0072]). Regarding claim 5, Tian further teaches wherein determining the target compression algorithm comprises: receiving usage conditions corresponding to compression algorithms sent by the network device (see Fig.8A and note [0072] different schemes for channel information compression, feedback and recovery to specific scenarios); and selecting the target compression algorithm from the compression algorithms in an algorithm list according to the usage conditions corresponding to the compression algorithms (see Fig.8A and note [0050] wherein channel state discriminative unit comprise algorithm list of compression algorithms [0072]). Regarding claim 6, Tian further teaches: sending at least one of an algorithm list or usage conditions corresponding to compression algorithms to the network device (see Fig.8B, note [0072] different schemes for channel information compression, feedback and recovery to specific scenarios). Regarding claim 7, Tian further teaches wherein the first indication information is an index identity, and wherein the index identity is used to indicate a position of the target compression algorithm in the algorithm list (note [0055], index feature including different scenarios and CSI indication information). Regarding claim 15, Tian further teaches: in response to a change in a compression algorithm used by the terminal device, sending second indication information to the network device, wherein the second indication information is used to indicate a changed compression algorithm used by the terminal device (see Fig.7B wherein changed information is sent to the base station, note [0067]). Regarding claim 16, Kyung discloses a channel state information (CSI) processing method (abstract), wherein the method is performed by a network device (base station;320), and the method comprises: receiving compressed CSI sent by a terminal device (transmitting compressed CSI by UE;310; para#55); thereby base station receiving compressed CSI), wherein the compressed CSI is obtained by the terminal device through compression of CSI using a target compression algorithm ((deep learning or machine learning) algorithm as determined target compression algorithm based on performance of deep neural network or neural network; para#54, where UE performs the compressed CSI based on target compression algorithm (para#54-55; obtaining CSI compression). However, Kyung does not explicitly teach wherein the method further comprises: receiving first indication information sent by the terminal device, wherein the first indication information is used to indicate the target compression algorithm: decompressing the compressed CSI according to the target compression algorithm to obtain the CSI before compression; and generating a precoding matrix according to a channel response matrix in the CSI before compression to perform encoding using the precoding matrix. Tian teaches receiving first indication information sent by the terminal device, wherein the first indication information is used to indicate the target compression algorithm (sending to BS, see Fig.7A), wherein the first indication information is used to indicate the target compression algorithm (indication of CSI type, CSI information type, scenario type, channel feature index type, corresponding encoding/decoding type, note [0068]; compression algorithms [0072]); decompressing the compressed CSI according to the target compression algorithm to obtain the CSI before compression (see Fig.9 of decompressing or decoding); and generating a precoding matrix according to a channel response matrix in the CSI before compression to perform encoding using the precoding matrix (configure data transmission based on CSI, see Fig.2 by the base station). Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to incorporate the teaching of Tian into the system of Kyung of transmitting the first indication information for the purpose of improving MIMO transmission (note [0039]). Regarding claim 24, the claim is rejected as applied to claim 1. Kyung further teaches a terminal device (UE, 310) comprising a processor (520) and a memory (510). Regarding claim 25, the claim is rejected as applied to claim 1. Kyung further discloses a non-transitory computer-readable storage medium storing instructions (para#74-75), wherein when the instructions are executed the method according to claim 1 is implemented (para#75,78). Regarding claim 26, the claim is rejected as applied to claim 1. Kyung further discloses a network device (BS;320; para#73), comprising a processor (510) and a memory (520), wherein a computer program is stored in the memory (para#74-75), and the processor executes the computer program stored in the memory to cause the network device (320) to perform the method according to claim 16 (para#78). Allowable Subject Matter Claims 8-14 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAM K. AHN whose telephone number is (571)272-3044. The examiner can normally be reached Monday-Friday, 9-5PM. 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. 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. /SAM K AHN/ Supervisory Patent Examiner, Art Unit 2633
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Prosecution Timeline

Oct 10, 2024
Application Filed
Jan 30, 2026
Non-Final Rejection mailed — §103
Apr 23, 2026
Response Filed
Jul 22, 2026
Non-Final Rejection mailed — §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

2-3
Expected OA Rounds
86%
Grant Probability
96%
With Interview (+10.3%)
3y 4m (~1y 6m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 339 resolved cases by this examiner. Grant probability derived from career allowance rate.

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