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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 7, 9-13, 15, and 17-20 is/are rejected under 35 U.S.C. 102a(1) as being anticipated by Wei (XP091055925 NPL doc from IDS).
Referring to Claim 1, Wei teaches a beam training method, comprising:
constructing a near-field code word of each sampling point pair of a plurality of sampling point pairs (see bottom of pg. 11 which shows multiple pairs of sampled points) according to an array response vector of a near-field cascaded channel of a reconfigurable intelligent surface (RIS) (see first paragraph of pg. 3 which shows a cascaded array steering vector which is the array response vector of a near-field cascaded channel), wherein each sampling point pair comprises one first sampling point and one second sampling point (see second paragraph of pg. 4 which shows a codeword determined by a pair of sampled points), the first sampling point is a candidate location of a scatterer between a first node and the RIS, and the second sampling point is a candidate location of a scatterer between the RIS and a second node (see pg. 8 which shows the distances between the scatterers and the RIS and fig. 1 on pg. 5 which shows points between nodes and the RIS);
sending a training symbol through the RIS according to the near-field code word of each sampling point pair (see third paragraph of pg. 12 which shows effective symbol transmitted which is based on the codeword); and
receiving feedback information, wherein the feedback information comprises information of an optimal beam (see third paragraph of pg. 7 which shows feedback with information on the optimal codeword index which is the optimal beam).
Referring to Claim 7, Wei teaches a beam training method, comprising:
in a current search phase, constructing a near-field code word of each sampling point pair of a plurality of sampling point pairs (see bottom of pg. 11 which shows multiple pairs of sampled points) according to an array response vector of a near-field cascaded channel of a reconfigurable intelligent surface (RIS) (see first paragraph of pg. 3 which shows a cascaded array steering vector which is the array response vector of a near-field cascaded channel), wherein each sampling point pair comprises one first sampling point and one second sampling point in a current sampling range (see second paragraph of pg. 4 which shows a codeword determined by a pair of sampled points), the first sampling point is a candidate location of a scatterer between a first node and the RIS, and the second sampling point is a candidate location of a scatterer between the RIS and a second node (see pg. 8 which shows the distances between the scatterers and the RIS and fig. 1 on pg. 5 which shows points between nodes and the RIS);
sending a training symbol through the RIS according to the near-field code word of each sampling point pair (see third paragraph of pg. 12 which shows effective symbol transmitted which is based on the codeword);
receiving feedback information, wherein the feedback information comprises information of an optimal beam in the current sampling range (see third paragraph of pg. 7 which shows feedback with information on the optimal codeword index which is the optimal beam); and
updating the current sampling range according to the information of the optimal beam and entering a next search phase, and returning to perform an operation of constructing the near-field code word of each sampling point pair of the plurality of sampling point pairs until a search stop condition of the optimal beam is satisfied (see pg. 14 which shows the searching from the 1st level sub-codebook until the search stop condition which is searching the last level sub-codebook).
Referring to Claim 15, Wei teaches a beam training method, comprising:
receiving a training symbol through a reconfigurable intelligent surface (RIS), wherein the training symbol is sent according to a near-field code word (see third paragraph of pg. 12 which shows effective symbol transmitted which is based on the codeword) of each sampling point pair of a plurality of sampling point pairs in a sampling range (see bottom of pg. 11 which shows multiple pairs of sampled points), the near-field code word is constructed according to an array response vector of a near-field cascaded channel of the RIS (see first paragraph of pg. 3 which shows a cascaded array steering vector which is the array response vector of a near-field cascaded channel), each sampling point pair comprises one first sampling point and one second sampling point (see second paragraph of pg. 4 which shows a codeword determined by a pair of sampled points), the first sampling point is a candidate location of a scatterer between a first node and the RIS, and the second sampling point is a candidate location of a scatterer between the RIS and a second node (see pg. 8 which shows the distances between the scatterers and the RIS and fig. 1 on pg. 5 which shows points between nodes and the RIS);
determining an optimal beam according to received energy of the training symbol, and sending feedback information, wherein the feedback information comprises information of the optimal beam in the sampling range (see third paragraph of pg. 12 which shows effective symbol transmitted which is based on the codeword and third paragraph of pg. 7 which shows feedback with information on the optimal codeword index which is the optimal beam).
Referring to Claim 2, also teaches constructing a codebook according to the near-field code word of each sampling point pair, wherein the codebook comprises a plurality of non-repeated near-field code words (see third paragraph of pg. 4 which shows the codebook designed using codewords from sampled points).
Referring to Claims 3 and 9, also teaches determining a first sampling point set and a second sampling point set, wherein the first sampling point set comprises a plurality of first sampling points, and the second sampling point set comprises a plurality of second sampling points (see bottom of pg. 11 which shows multiple pairs of sampled points).
Referring to Claim 4, also teaches obtaining the first sampling point set and the second sampling point set by sampling a sampling range according to a set sampling step size (see fourth paragraph of pg. 4 which shows sampling steps as step sizes since they gradually become smaller).
Referring to Claims 5 and 13, Wei also teaches wherein the near-field code word of each sampling point pair is associated with a sum of a distance between the first sampling point and the RIS and a distance between the second sampling point and the RIS (See third paragraph of pg. 10 which shows the sum of the distance from the sampling points to the RIS).
Referring to Claim 10, Wei also teaches obtaining the first sampling point set and the second sampling point set by sampling the current sampling range according to a set sampling step size of the current search phase (see fourth paragraph of pg. 4 which shows sampling steps as step sizes since they gradually become smaller).
Referring to Claim 11, Wei also teaches reducing the set sampling size of the current search phase according to a set proportion and using the reduced set sampling step size as a set sampling step size of the next search phase (see fourth paragraph of pg. 4 which shows sampling steps as step sizes which become reduced).
Referring to Claim 12, Wei also teaches constructing a codebook corresponding to the current sampling range according to the near-field code word of each sampling point pair in the current sampling range (see third paragraph of pg. 4 which shows the codebook designed using codewords from sampled points).
Referring to Claim 17, Wei also teaches a first node, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when executing the program, the processor performs the beam training method (see pg. 2 which shows a base station which is known in the art to have a non-transitory computer-readable storage medium).
Referring to Claim 18, Wei also teaches second node, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when executing the program, the processor performs the beam training method (see pg. 2 which shows a user device which is known in the art to have a non-transitory computer-readable storage medium).
Claim 19 has similar limitations as claim 1.
Referring to Claim 20, Wei also teaches a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, causes the processor to perform the beam training method (see pg. 2 which shows a base station which is known in the art to have a non-transitory computer-readable storage medium).
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.
Claim(s) 6, 14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei in view of Huang (CN 113300747).
Referring to Claims 6 and 14, Wei does not teach traversing the near-field code word of each sampling point pair, setting a reflection coefficient of the RIS to a currently traversed near-field code word, and sending the training symbol through the RIS based on the reflection coefficient. Huang teaches traversing the near-field code word of each sampling point pair, setting a reflection coefficient of the RIS to a currently traversed near-field code word, and sending the training symbol through the RIS based on the reflection coefficient (see English translation of ABSTRACT which shows RIS reflection coefficient determined to determine optimal path based on a codebook). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to provide the teachings of Huang to the device of Wei in order to better ensure optimum signal quality when using RIS.
Referring to Claim 16, Huang also teaches using a near-field code word corresponding to a training symbol having maximum received energy as the optimal beam (see English translation of ABSTRACT and Claim 1 which shows optimum path having a maximum received signal strength). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to provide the teachings of Huang to the device of Wei in order to better ensure optimum signal quality when using RIS.
Allowable Subject Matter
Claim 8 is 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 Claim 8, Wei and Huang do not teach setting the current sampling range to a range using a near-field code word corresponding to the optimal beam as a center and one half of a set sampling step size of the current search phase as a distance from a front boundary of the range to the center and as a distance from a back boundary of the range to the center.
Conclusion
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/EUGENE YUN/ Primary Examiner, Art Unit 2648
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