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 .
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.
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 non-obviousness.
Claims 1, 4, 8, 14, 20, 29, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over US Pub. 2021/0167875 to Shen et al. (hereinafter Shen) in view of US Pub. 2017/0293229 to Godfried et al. (hereinafter Godfried).
In regard claim 1, Shen teaches or discloses a beam measurement parameter feedback method, comprising:
obtaining a beam measurement parameter set (see paragraphs [0009], [0012], [0095], and [0097], receiving, by a UE, reference signal respectively by at least two measuring beams. Receiving, by the UE, reference signals respectively by at least two measuring beams, may include: receiving the reference signals successively by the at least two measuring beams in accordance with a fixed receiving order or non-fixed receiving order of the at least two measuring beams),
wherein the beam measurement parameter set is used for determining a beam measurement parameter array corresponding to a neural network (see paragraphs [0010], [0011], [0023], [0043], and [0165], determining at least two measuring beams from receiving beams for the UE based on distribution of beams for the UE and a preset selection rule comprises at least one of the following: determining, from receiving beams for the UE, at least two measuring beams that are uniformly distributed in various directions of the entire spherical space centered on antenna array for the UE, and determining, from receiving beams for the UE, at least two measuring beams that are close to a normal direction of the antenna array for the UE. A predetermined prediction network comprises a combined network of a convolutional neural network model and a recurrent neural network model, the recurrent neural network model comprising a long short-term memory network model).
Shen may not explicitly teach or disclose the beam measurement parameter set comprises N elements, the beam measurement parameter array comprises M elements, N and M are positive integers greater than 1, and M is less than or equal to N; and feeding back the beam measurement parameter set.
However, Godfried teaches or discloses the beam measurement parameter set comprises N elements, the beam measurement parameter array comprises M elements, N and M are positive integers greater than 1, and M is less than or equal to N (see paragraphs [0008], [0105], [0109], [0110], [01031] [0137], and [0145]); feeding back the beam measurement parameter set (see paragraphs [0145]).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify method and UE for signal reception of Shen by including the beam measurement parameter set comprises N elements, the beam measurement parameter array comprises M elements, N and M are positive integers greater than 1, and M is less than or equal to N; and feeding back the beam measurement parameter set suggested by Godfried. This modification would provide a desired spatial intensity distribution read in paragraph [0008].
In regard claim 4, Shen may not explicitly teach or disclose the method according to claim 1, wherein the beam measurement parameter array is an array of elements from the beam measurement parameter set arranged in a preset order.
However, Godried teaches or discloses wherein the beam measurement parameter array is an array of elements from the beam measurement parameter set arranged in a preset order (see paragraphs [0008], [0028], [0029], and [0098]).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify method and UE for signal reception of Shen by including wherein the beam measurement parameter array is an array of elements from the beam measurement parameter set arranged in a preset order suggested by Godfried. This modification would provide a desired spatial intensity distribution read in paragraph [0008].
In regard claims 8 and 20, Shen teaches or discloses the method according to claim 1, wherein the beam measurement parameter set comprises a first beam measurement parameter subset and a second beam measurement parameter subset (see paragraphs [0031], [0036], [0037], and [0046])
In regard claim 14, Shen teaches or discloses a beam measurement parameter receiving method, comprising:
receiving a beam measurement parameter set (see paragraphs [0009], [0012], [0095], and [0097], receiving, by a UE, reference signal respectively by at least two measuring beams. Receiving, by the UE, reference signals respectively by at least two measuring beams, may include: receiving the reference signals successively by the at least two measuring beams in accordance with a fixed receiving order or non-fixed receiving order of the at least two measuring beams); and
determining, according to the beam measurement parameter set and a neural network, a beam measurement parameter array corresponding to the neural network (see paragraphs [0010], [0011], [0023], [0043], and [0165], determining at least two measuring beams from receiving beams for the UE based on distribution of beams for the UE and a preset selection rule comprises at least one of the following: determining, from receiving beams for the UE, at least two measuring beams that are uniformly distributed in various directions of the entire spherical space centered on antenna array for the UE, and determining, from receiving beams for the UE, at least two measuring beams that are close to a normal direction of the antenna array for the UE. A predetermined prediction network comprises a combined network of a convolutional neural network model and a recurrent neural network model, the recurrent neural network model comprising a long short-term memory network model),
Shen may not explicitly teach or disclose wherein the beam measurement parameter set comprises N elements, the beam measurement parameter array comprises M elements, N and M are integers greater than 1, and M is less than or equal to N.
However, Godfried teaches or discloses wherein the beam measurement parameter set comprises N elements, the beam measurement parameter array comprises M elements, N and M are integers greater than 1, and M is less than or equal to N (see paragraphs [0008], [0105], [0109], [0110], [01031] [0137], and [0145]).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify method and UE for signal reception of Shen by including wherein the beam measurement parameter set comprises N elements, the beam measurement parameter array comprises M elements, N and M are integers greater than 1, and M is less than or equal to N suggested by Godfried. This modification would provide a desired spatial intensity distribution read in paragraph [0008].
In regard amended claim 29, Shen teaches or discloses a non-transitory computer-readable storage medium, having a computer program stored therein, wherein the computer program is configured to, when executed by a processor, implement the steps of the method as claimed in claim 1 (see paragraphs [0026], [0029], [0043], [0044], [0049], and [0050]).
In regard amended claim 30, Shen teaches or discloses an electronic apparatus, comprising a memory, a processor, and a computer program stored on the memory, wherein the processor is configured to execute the computer program to implement the steps of the method as claimed in claim 1 (see paragraphs [0026], [0029], [0043], [0044], [0049], and [0050]).
Allowable Subject Matter
Claims 2-3, 5-7, 9-10, 13, 15, 18, 22, 25, and 26 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHIRIN SAM whose telephone number is (571)272-3082. The examiner can normally be reached Mon - Fri, 10:30am - 5pm.
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Date: 06/27/2026
/PHIRIN SAM/Primary Examiner, Art Unit 2476
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