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 .
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-13, in the reply filed on 3/5/26 is acknowledged. Claims 14-18 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim.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 3/5/26.
Claim Objections
Claim 10 is objected to because of the following informalities:
It appears claim 10 should depend from claim 9 instead of claim 8, as claim 10 recites regarding “using a supervised learning network” in a “wherein” clause, but claim 8 does not recite using a supervised learning network, whereas claim 9 does introduce such a network.
Appropriate correction is required.
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.
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 1-3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Jagyasi et al. U.S. Pat. App. Pub. No. 2025/0323689 in view of Obeid et al. U.S. Pat. App. Pub. No. 2025/0167835.
Regarding claim 1, Jagyasi discloses a method of characterizing a communication channel, comprising: receiving, at one or more receivers, a first signal from a set of transmitters reflected along a reflected channel from each element of a reconfigurable intelligent surface (RIS) set at a nominal angle (¶ [0102]), where Jagyasi further indicates that channel estimation (i.e. CSI – see ¶ [0115]) measurements are made for a RIS state associated with an RIS with M elements (Fig. 2, ¶ [0118]), where an RIS state reflects an incident beam in a desired direction (¶ [0114]) and thus is considered to be associated with a nominal angle for a first given direction (see also ¶ [0193]), and multiple RIS states are used for CSI determination and CSI reports (¶¶ [0118], [0151]), and thus each separate state would be associated with a signal reflected in the reflected channel at a different adjusted angle; using first and second signals to determine a transfer function for a combined channel comprised of the reflected channel and a direct channel between the transmitters and the one or more receivers, as the WTRU estimates the channels (including direct and RIS-aided channels) from the TRP to the WTRU to determine the transfer function (see ¶¶ [0103], [0171]); and using the transfer function to optimize settings for elements of the RIS as the RIS state Φ is selected that maximizes a received power or SNR (¶ [0171]).
Jagyasi does not expressly show that the transfer function is used as an input to a machine learning network to determine the optimized settings for the elements of the RIS. Obeid discloses the use of machine learning in the channel estimation process performed in a channel including reconfigurable intelligent surfaces to optimize settings for elements of the RIS (¶¶ [0034]-[0038], [0064]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to employ machine learning in the optimization of RIS settings in a channel estimation, as suggested by Obeid, in the method of Jagyasi, as such machine learning processes help in reducing the overhead required in performing the channel estimation (see Obeid, ¶ [0064]).
Regarding claim 2, in the proposed combination, the optimized settings are used to set the RIS elements (see Jagyasi, ¶ [0089]).
Regarding claim 3, in the proposed combination, Jagyasi discloses receiving the pilots/ reference signals include receiving the first signal and the second signal from one element of the RIS for each of a set of transmitters, as multiple pilots for different RIS states may be transmitted/received (see Jagyasi, ¶ [0151]), and repeating the receiving of the first and second signals from one element for each element of the RIS as other RIS elements may be subsequently turned on one-by-one (Jagyasi, ¶ [0104]).
Regarding claim 6, in the proposed combination, Jagyasi further suggests repeating the receiving of the first and second signal for multiple adjusted angles as multiple pilots for different RIS states may be transmitted/received (see Jagyasi, ¶ [0151]), thereby producing multiple matrices (¶¶ [0103], [0108], [0167], [0183]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Jagyasi et al. in view of Obeid et al. as applied to claim 1 above, and further in view of Oh et al. U.S. Pat. App. Pub. No. 2026/0045972 (hereinafter “Oh '972”).
Regarding claim 9, Jagyasi in combination with Obeid disclose a method characterizing a communication channel using machine learning, as described above, but do not expressly disclose that the machine learning network comprises a supervised learning network.
Oh '972 discloses that supervised learning may be employed with channel estimation in systems employing reconfigurable intelligent surfaces (see Fig. 22, ¶ [0208]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to employ supervised learning as suggested by Oh, in the machine learning network of Jagyasi in combination with Obeid, as supervised learning may provide for more accurate predictions when compared to other machine learning techniques (see Oh '972, ¶ [0111]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Jagyasi et al. in view of Obeid et al. as applied to claim 1 above, and further in view of Oh et al. U.S. Pat. App. Pub. No. 2024/0356587 (hereinafter “Oh '587”).
Regarding claim 11, Jagyasi in combination with Obeid disclose a method characterizing a communication channel using machine learning, as described above, and Jagyasi further discloses that a vector is derived from the transfer function of the combined channel (¶ [0092]), but the proposed combination does not disclose the machine learning network uses unsupervised learning with a vector derived from the transfer function of the combined channel as an input.
Oh '687 discloses that unsupervised learning may be employed with channel estimation in systems employing reconfigurable intelligent surfaces (see ¶¶ [0183]-[0185]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to employ unsupervised learning as suggested by Oh '687, in the machine learning network of Jagyasi in combination with Obeid, as such reinforcement unsupervised learning may perform faster and more efficient search for machine learning (see Oh '587, ¶ [0161]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Jagyasi et al. in view of Obeid et al. and Oh '587 as applied to claim 11 above, and further in view of Shoor U.S. Pat. App. Pub. No. 2008/0095283.
Regarding claim 12, Jagyasi in combination with Obeid and Oh '587 disclose a method characterizing a communication channel using unsupervised machine learning, as described above, but the proposed combination does not expressly disclose using a vector of an averaged transfer function or a least mean squares (LMS) method.
Shoor discloses use of the LMS filter in continuously tracking an averaged channel in channel estimation (¶ [0027]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to employ an algorithm employing the LMS technique for channel estimation, in the method of Jagyasi in combination with Obeid and Oh '587 as it aids in reducing noise attributable to error signals (see Shoor, abstract).
Allowable Subject Matter
Claims 4, 5, 7, 8, 10 and 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.
Regarding claims 14-18 withdrawn above, it is noted that a rejoinder of withdrawn claims would be considered if claim 14 were amended to include limitations analogous to limitations of claims indicated above as containing allowable subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to David B. Lugo whose telephone number is 571-272-3043. The examiner can normally be reached M-F, 9-6.
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/DAVID B LUGO/Primary Examiner, Art Unit 2631 4/17/2026
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