Prosecution Insights
Last updated: April 19, 2026
Application No. 18/623,185

SYSTEMS AND METHODS FOR MODEL MONITORING OF CHANNEL STATE INFORMATION PREDICTION AND COMPRESSION

Non-Final OA §103§112
Filed
Apr 01, 2024
Examiner
CHANG, YU-WEN
Art Unit
2413
Tech Center
2400 — Computer Networks
Assignee
InterDigital Patent Holdings, Inc.
OA Round
1 (Non-Final)
81%
Grant Probability
Favorable
1-2
OA Rounds
3y 1m
To Grant
94%
With Interview

Examiner Intelligence

Grants 81% — above average
81%
Career Allow Rate
257 granted / 318 resolved
+22.8% vs TC avg
Moderate +13% lift
Without
With
+12.7%
Interview Lift
resolved cases with interview
Typical timeline
3y 1m
Avg Prosecution
20 currently pending
Career history
338
Total Applications
across all art units

Statute-Specific Performance

§101
2.4%
-37.6% vs TC avg
§103
64.2%
+24.2% vs TC avg
§102
17.4%
-22.6% vs TC avg
§112
10.9%
-29.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 318 resolved cases

Office Action

§103 §112
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 . Information Disclosure Statement The information disclosure statement (IDS) submitted on 04/01/2024, 04/03/2024 and 09/16/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections Claims 3, 5-6, 13 and 15-16 are objected to because of the following informalities: Regarding claim 3, in lines 1-2, “the CSI error type” should be “the CSI error condition type”. Regarding claim 5, in line 3, “the CSI error type” should be “the CSI error condition type”. Regarding claim 6, in line 2, “the CSI error type” should be “the CSI error condition type”. Regarding claim 13, in line 3, “the CSI error type” should be “the CSI error condition type”. Regarding claim 15, in line 3, “the CSI error type” should be “the CSI error condition type”. Regarding claim 16, in line 2, “the CSI error type” should be “the CSI error condition type”. Appropriate correction is required. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 8 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 8 recites the limitation "the determined CSI compression configuration" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim. Claim 18 recites the limitation "the determined CSI compression configuration" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim. 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. 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. Claims 1-2, 8-9, 11-12 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 2024/0162957) in view of Muralidhar et al. (US 2023/0132826). Regarding Claim 1, Kim teaches a method implemented by a wireless transmit/receive unit (WTRU), the method comprising: receiving configuration information for channel state information (CSI) prediction and compression ([0089] When the terminal performs AI-based channel prediction, the base station may determine configuration information including parameters relating to a prediction interval, a time-step for prediction, and the like. When the terminal performs AI-based CSI compression, the base station may determine configuration information including parameters relating to a CSI report mode, a compression mode (e.g., the number of feedback bits), and the like. The base station may transmit configuration information determined based on capability information of the terminal to the terminal; [0130] the base station may determine a CSI feedback mode indicator and configuration information for AI-based channel prediction and CSI compression, and configure the same to the terminal; [0155] When the terminal performs AI-based channel prediction, the base station may determine configuration information including parameters relating to a prediction interval, a time-step for prediction, and the like. When the terminal performs AI-based CSI compression, the base station may determine configuration information including parameters relating to a CSI report mode, a compression mode (e.g., the number of feedback bits), and the like. The base station may transmit configuration information determined based on capability information of the terminal to the terminal); determining a CSI error condition type based on the total combined CSI error, wherein the CSI error condition type comprises at least one of a CSI prediction limited condition type, a CSI compression limited condition type, or a combined CSI prediction and compression limited condition type ([0141] when the terminal identifies a decrease in channel covariance or a decrease in channel coherence time, based on channel estimation, the terminal may identify that channel variation has decreased, and accordingly, may transmit an AI prediction-based feedback mode as a preferred CSI feedback mode. ... when the terminal identifies an increase in channel covariance or an increase in channel coherence time, based on channel estimation, the terminal may identify that channel variation has increased, and accordingly, may transmit an AI compression-based feedback mode as a preferred CSI feedback mode (i.e., a CSI prediction limited condition type or a CSI compression limited condition type); [0142] when the terminal identifies a decrease in channel covariance, based on channel estimation, the terminal may identify that channel variation has decreased, and accordingly, may transmit the operating parameter in which a prediction time-step has been increased as a preferred parameter to the base station. For example, when the terminal identifies an increase in channel covariance, based on channel estimation, the terminal may identify that channel variation has increased, and accordingly, may transmit the operating parameter in which a compression degree has been increased as a preferred parameter to the base station); and sending a message comprising an indication of the determined CSI error condition type to a network ([0141] when the terminal identifies a decrease in channel covariance or a decrease in channel coherence time, based on channel estimation, the terminal may identify that channel variation has decreased, and accordingly, may transmit an AI prediction-based feedback mode as a preferred CSI feedback mode. ... when the terminal identifies an increase in channel covariance or an increase in channel coherence time, based on channel estimation, the terminal may identify that channel variation has increased, and accordingly, may transmit an AI compression-based feedback mode as a preferred CSI feedback mode; [0142] when the terminal identifies a decrease in channel covariance, based on channel estimation, the terminal may identify that channel variation has decreased, and accordingly, may transmit the operating parameter in which a prediction time-step has been increased as a preferred parameter to the base station. For example, when the terminal identifies an increase in channel covariance, based on channel estimation, the terminal may identify that channel variation has increased, and accordingly, may transmit the operating parameter in which a compression degree has been increased as a preferred parameter to the base station). However, Kim does not teach measuring a CSI prediction error and a CSI compression error to obtain a total combined CSI error. In an analogous art, Muralidhar teaches measuring a CSI prediction error ([0077] variance of linear prediction) and a CSI compression error ([0077] error variance due to the quantization) to obtain a total combined CSI error ([0077] For this the error variance of linear prediction, LCP and error variance due to the quantization need to be provided as feedback; [0116] If the feedback channel predictor error variance and quantization noise variance along with LPC coefficients are feedback, the BS (200) can estimate the channel more accurately). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Muralidhar’s method with Kim’s method so that the base station can estimate the channel more accurately (Muralidhar [0116]). Thus, the system performance and communication quality can be improved. Regarding Claim 2, Kim does not teach determining a relative CSI error based on the total combined CSI error and at least one of the CSI prediction error or the CSI compression error. In an analogous art, Muralidhar teaches determining a relative CSI error based on the total combined CSI error and at least one of the CSI prediction error or the CSI compression error ([0077] For this the error variance of linear prediction, LCP and error variance due to the quantization need to be provided as feedback; [0116] If the feedback channel predictor error variance and quantization noise variance along with LPC coefficients are feedback, the BS (200) can estimate the channel more accurately). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Muralidhar’s method with Kim’s method so that the base station can estimate the channel more accurately (Muralidhar [0116]). Thus, the system performance and communication quality can be improved. Regarding Claim 8, the combination of Kim and Muralidhar, specifically Kim teaches the message further comprises the determined CSI compression configuration and an associated index ([0112] The terminal may transmit, to the base station, index values corresponding to a plurality of feedback modes supported by the terminal. ... When the terminal supports AI-based compression feedback, the terminal may determine and report an index of {1, 3}). Regarding Claim 9, the combination of Kim and Muralidhar, specifically Kim teaches the message further comprises an indication of the measured CSI prediction error and CSI compression error ([0141] when the terminal identifies a decrease in channel covariance or a decrease in channel coherence time, based on channel estimation, the terminal may identify that channel variation has decreased, and accordingly, may transmit an AI prediction-based feedback mode as a preferred CSI feedback mode. ... when the terminal identifies an increase in channel covariance or an increase in channel coherence time, based on channel estimation, the terminal may identify that channel variation has increased, and accordingly, may transmit an AI compression-based feedback mode as a preferred CSI feedback mode; [0142] when the terminal identifies a decrease in channel covariance, based on channel estimation, the terminal may identify that channel variation has decreased, and accordingly, may transmit the operating parameter in which a prediction time-step has been increased as a preferred parameter to the base station. For example, when the terminal identifies an increase in channel covariance, based on channel estimation, the terminal may identify that channel variation has increased, and accordingly, may transmit the operating parameter in which a compression degree has been increased as a preferred parameter to the base station). Regarding Claim 11, the claim is interpreted and rejected for the same reason as set forth in Claim 1. Regarding Claim 12, the claim is interpreted and rejected for the same reason as set forth in Claim 2. Regarding Claim 18, the claim is interpreted and rejected for the same reason as set forth in Claim 8. Regarding Claim 19, the claim is interpreted and rejected for the same reason as set forth in Claim 9. Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. in view of Muralidhar et al. and Suarez Rivera et al. (US 2024/0406773). Regarding Claim 10, the combination of Kim and Muralidhar does not teach determining parameters associated with CSI prediction based on the measured CSI prediction error and CSI compression error and on a CSI prediction configuration. In an analogous art, Suarez Rivera teaches determining parameters associated with CSI prediction based on the measured CSI prediction error and CSI compression error and on a CSI prediction configuration ([0168] The UE may be in a better position to understand what is happening during the CSI feedback process, as the UE has access to both the channel measurements and the results of CSI prediction and compression, thus the UE is able to make computations on this data and provide more reliable signal indications on how to reconfigure the CSI parameters). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Suarez Rivera’s method with Kim’s method so that the UE is able to make computations on this data and provide more reliable signal indications on how to reconfigure the CSI parameters (Suarez Rivera [0168]). Moreover, an indication may be embedded in the CSI feedback from the UE to provide useful information to the access node on how to reconfigure these parameters in a straightforward manner, without a large additional information bit overhead in the CSI report (Suarez Rivera [0169]). Regarding Claim 20, the claim is interpreted and rejected for the same reason as set forth in Claim 10. Allowable Subject Matter Claims 3-7 and 13-17 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. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lee et al. (US 2026/0032021) teaches method of channel state information prediction using machine learning. Kumar et al. (US 2024/0113757) teaches method for CSI prediction in wireless networks. Any inquiry concerning this communication or earlier communications from the examiner should be directed to YU-WEN CHANG whose telephone number is (408)918-7645. The examiner can normally be reached M-F 8:00am-5:00pm PT. 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, Un Cho can be reached at 571-272-7919. 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. /YU-WEN CHANG/Primary Examiner, Art Unit 2413
Read full office action

Prosecution Timeline

Apr 01, 2024
Application Filed
Mar 19, 2026
Non-Final Rejection — §103, §112 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12604240
NEIGHBOR RELATIONS FOR DISPROPORTIONAL/DISSIMILAR BANDWIDTH/CARRIER ALLOCATION
2y 5m to grant Granted Apr 14, 2026
Patent 12588062
FREQUENCY ASSIGNMENT MECHANISMS FOR BACKSCATTER DEVICES
2y 5m to grant Granted Mar 24, 2026
Patent 12581337
METHOD AND APPARATUS FOR TRANSMITTING INFORMATION ABOUT CHANNEL STATE IN NR V2X
2y 5m to grant Granted Mar 17, 2026
Patent 12580717
ACTIVATION OR DEACTIVATION OF MULTIPLE DOWNLINK (DL) OR UPLINK (UL) POSITIONING REFERENCE SIGNALS (PRS) WITH A SINGLE MAC-CE COMMAND
2y 5m to grant Granted Mar 17, 2026
Patent 12581525
METHODS FOR COMMUNICATION DEVICES FOR OR ADJUSTING A PROCESSING GAIN, APPARATUS, VEHICLE AND COMPUTER PROGRAM
2y 5m to grant Granted Mar 17, 2026
Study what changed to get past this examiner. Based on 5 most recent grants.

AI Strategy Recommendation

Get an AI-powered prosecution strategy using examiner precedents, rejection analysis, and claim mapping.
Powered by AI — typically takes 5-10 seconds

Prosecution Projections

1-2
Expected OA Rounds
81%
Grant Probability
94%
With Interview (+12.7%)
3y 1m
Median Time to Grant
Low
PTA Risk
Based on 318 resolved cases by this examiner. Grant probability derived from career allow rate.

Sign in with your work email

Enter your email to receive a magic link. No password needed.

Personal email addresses (Gmail, Yahoo, etc.) are not accepted.

Free tier: 3 strategy analyses per month