Detailed office 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 .
This office action in response to the communication dated 5/17/24.
Original claims 1-20 are pending.
Allowable Subject Matter
Claims 5-13 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.
Claims 14-20 are allowed.
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.
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.
Claims 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over DUAN et al. U.S. Patent Application Publication No. 20250211294 A1[hereinafter DUAN] in view of Li et al. U.S. Patent Application Publication No. 20250220471 [herein after Li]
As per claim 1, Duan discloses a user equipment, comprising:
a processor; and
a memory that stores executable instructions that, when executed by the
processor, facilitate performance of operations, the operations comprising(UE 702, fig. 7 and fig .10).
obtaining environment state data representative of an environment state applicable to the user equipment operating in a coverage area corresponding to a base station, the environment state data comprising reference signal data representative of a reference signal transmitted from the base station to the user equipment(see par. 0099, 0125, obtaining the WB CSI from the UE 702, as shown at 712, the network entity 704 may be configured to transmit a set of reference signals (RSs) to the UE 702 via the channel.
determining environment state data, channel state information report data comprising a precoding matrix, channel state information matrices, a precoding matrix indicator, a rank indicator(see par. 0060), and ACK/NACK information(se par. 0060)(see fig. 8 and pars. 0109, 0113, 0121); and
communicating the channel state information report data to the base station(see par. 0048, 0072)
Duan does not explicitly disclose wherein the determining step is from utilizing .
LI discloses a system for predicted output, which is generated by a trained ML model based on certain input data including wherein the determining step is from utilizing a trained model(see par. 0109). Therefore, it would have been obvious to one having ordinary skill in the art prior to effective filing date of the claimed invention to incorporate the teachings of Li in this way the trained ML-model can be utilized for assisting CSI (Channel State Information) estimation. In such a setup, the ML-model is a specific ML-model deployed at the UE side and an ML-model deployed at the network side. The ML-model can assist the UE in channel estimation or interference estimation for channel estimation and to predict a suitable CQI (Channel Quality Indicator), PMI (Precoding Matrix Indicator), RI (Rank Indicator) or similar value at the future time instances.
As per 2, Li discloses the user equipment of claim 1, wherein the reference signal data comprises channel state information reference signal data(see par. 0117)
As per 3, Li discloses The user equipment of claim 1, wherein the reference signal data comprises cell-specific reference signal data of at least one interfering base station(se par. 0115, 0133).
As per claim 4, LI discloses the user equipment of claim 3, wherein the trained model comprises a deep reinforcement learning model(see par. 0003).
Conclusion
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/ABDULLAHI E SALAD/Primary Examiner, Art Unit 2466
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