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 .
1. Claims 1-15, 17-18 and 27-29 are pending. Claims 16 and 19-26 are cancelled.
Information Disclosure Statement
2. The Information Disclosure Statements dated 08/15/2023 and 02/19/2025.
Claim Rejections - 35 USC § 102
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 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.
3. Claim(s) 1, 6-7, 9-10, 13, 17 and 28-29 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Meng et al, WO 2020/199687 A1 hereafter Meng.
As for claim 1, Meng discloses:
A user selection method, comprising:
acquiring user feature information (abstract, page 3, first paragraph, Acquiring first feature information of a first user on the selection of respective targets in a first target set; acquiring second feature information of a plurality of second users on the selection of respective targets in the first target set);
determining user association information according to the user feature information (abstract, page 3, first paragraph, Generating, according to the first feature information and the second feature information, first association relationship information between every two targets); and
selecting users according to the user association information and a preset condition (abstract, page 3, first paragraph, Selecting users according to association relationship information and a preset clustering algorithm).
As for claim 6, Meng discloses:
wherein the user association information is numeric data or non-numeric data; and the non-numeric data includes at least one of: a degree of user association (Meng, page 3, third paragraph, Using a degree of similarity); a user-related level; or a user-related type (Meng, page 3, paragraph 1, Targets classified into a same type based on preset rules).
As for claim 7, Meng discloses:
wherein the determining user association information according to the user feature information, includes: acquiring corresponding user association information according to a distance between user feature information (Meng, page 15, paragraph 3, The attribute features include one or more of the following sub-features: distance between the user and the target), wherein the user association information is limited within a set range (Meng, page 26 last line – page 27 line 3, Using a selectable range).
As for claim 9, Meng discloses:
wherein the preset condition includes at least one of: a first preset condition including an objective function of selected users; a second preset condition including a constraint condition of the selected users; or a third preset condition including a determination condition of the selected users (Meng, page 3, paragraph 2, generating third association relationship information between the first user and the second user according to the first feature information and the second feature information).
As for claim 10, Meng discloses:
the first preset condition includes at least one of: a sum of user association information between the selected users reaching a minimum value (Meng, page 14, third paragraph, the average second similarity degree may be compared with a preset second similarity degree threshold, and a target whose average second similarity degree is greater than the preset second similarity threshold value may be used as a candidate target.); a product of the user association information between the selected users reaching a minimum value; maximum user association information among the user association information between the selected users reaching a minimum value (Meng, page 14, third paragraph, the average second similarity degree may be compared with a preset second similarity degree threshold, and a target whose average second similarity degree is greater than the preset second similarity threshold value may be used as a candidate target.)
As for claim 13, Meng discloses:
wherein the selecting users according to the user association information and the preset condition (abstract, page 3, first paragraph, Selecting users according to association relationship information and a preset clustering algorithm), includes: grouping users according to the user association information to acquire user grouping information (abstract, page 3, first paragraph, Grouping users according to association relationship information to acquire a selected group); determining an initial set of selected users according to the user grouping information and a second preset condition; and adjusting a set of selected users according to the first preset condition and the second preset condition until a third preset condition is satisfied, and then taking users in a set of selected users as selected users (Meng, page 13, lines 1-20, the preset clustering algorithm is a K means clustering algorithm, which is an iterative solution clustering analysis algorithm. The step is to randomly select K targets as the initial cluster centers, and then calculate The distance between each target and each cluster center, and each target is assigned to the cluster center closest to it. The cluster centers and the targets assigned to them represent a cluster. Each time a target is assigned, the cluster center of the cluster will be recalculated based on the existing objects in the cluster. This process will be repeated until a certain termination condition is met.).
As for claim 17, Meng discloses:
the adjusting the set of selected users according to the first preset condition and the second preset condition, includes: determining an adjustment manner for the set of selected users according to the second preset condition; and adjusting the set of selected users according to the adjustment manner, wherein users in the set of selected users satisfy the first preset condition (Meng, page 13, lines 1-20, Recalculating/adjusting the targets/users in the cluster. The preset clustering algorithm is a K means clustering algorithm, which is an iterative solution clustering analysis algorithm. The step is to randomly select K targets as the initial cluster centers, and then calculate The distance between each target and each cluster center, and each target is assigned to the cluster center closest to it. The cluster centers and the targets assigned to them represent a cluster. This process will be repeated until a certain termination condition is met.)
As for claim 27, Meng discloses:
An information sending method, comprising: sending user feature information (abstract, page 3, first paragraph, Sending first feature information of a first user on the selection of respective targets in a first target set; acquiring second feature information of a plurality of second users on the selection of respective targets in the first target set), wherein the user feature information is used to determine user association information (abstract, page 3, first paragraph, Generating, according to the first feature information and the second feature information, first association relationship information between every two targets) and a user selection result (abstract, page 3, first paragraph, Selecting users according to association relationship information and a preset clustering algorithm).
As for claim 28, Meng discloses:
A communication node, comprising a memory, a processor, and a computer program stored on the memory and runnable on the processor, wherein when executing the computer program (Meng, page 7, fourth paragraph, the device includes a processor and a memory; the memory is used to store instructions, and when the instructions are executed by the processor), the processor implements the user selection method according to claim 1.
As for claim 29, Meng discloses:
A non-transitory computer-readable storage medium having stored a computer program, wherein when executed by a processor (Meng, page 7, fourth paragraph, the device includes a processor and a memory; the memory is used to store instructions, and when the instructions are executed by the processor), the computer program implements the user selection method according to claim 1.
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.
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 nonobviousness.
4. Claim(s) 2-4 and 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meng et al, WO 2020/199687 A1 in view of US 2017/0034841 hereafter ‘4841.
As for claim 2, Meng does not explicitly disclose the user feature information includes at least one piece of following information for each user: a large-scale parameter; instantaneous channel information; an instantaneous throughput; an average throughput.
However, ‘4841 discloses the user feature information includes at least one piece of following information for each user: a large-scale parameter; instantaneous channel information; an instantaneous throughput; an average throughput ([0030], [0059], [0061], the user rate information wherein the user rates are averaged over the time period)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Meng with the user feature information includes at least one piece of following information for each user: a large-scale parameter; instantaneous channel information; an instantaneous throughput; an average throughput as taught by ‘4841 to provide load balancing capabilities (‘4841, [0004]).
As for claim 3, Meng does not explicitly disclose
the user feature information includes a set of K elements, and K is equal to a total number of users; and each element represents at least one piece of following information for a user: an instantaneous throughput; an average throughput; a SINR; traffic; or a latency requirement.
However, ‘4841 discloses the user feature information includes a set of K elements, and K is equal to a total number of users; and each element represents at least one piece of following information for a user: an instantaneous throughput; an average throughput ([0030], [0059], [0061], the user rate information wherein the user rates are averaged over the time period); a SINR; traffic; or a latency requirement.
As for claim 4, Meng does not explicitly disclose the user feature information includes a set of K element groups, and K is equal to a total number of users
However, ‘4841 discloses the user feature information includes a set of K element groups, and K is equal to a total number of users ([0059], The user set size)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Meng with the user feature information includes a set of K element groups, and K is equal to a total number of users as taught by ‘4841 to provide load balancing capabilities (‘4841, [0004]).
As for claim 11, Meng does not explicitly disclose wherein the second preset condition includes at least one of: a number of the selected users being not less than α; the number of the selected users being not more than μ; the number of the selected users being ξ; or the number of the selected users being not less than α and not more than μ; wherein α≤K, μ≤K, ξ≤K, and α, μ and ξ are all positive integers, and K is equal to a total number of users.
However, ‘4841 discloses the second preset condition includes at least one of: a number of the selected users being not less than α ([0095], The set size s can be between 1 to s.sub.max where M>>s.sub.max.); the number of the selected users being not more than μ ([0095], The set size s can be between 1 to s.sub.max where M>>s.sub.max.); the number of the selected users being ξ ([0095], D is the set of all possible scheduling set sizes); or the number of the selected users being not less than α and not more than μ ([0095], The set size s can be between 1 to s.sub.max where M>>s.sub.max.); wherein α≤K, μ≤K, ξ≤K, and α, μ and ξ are all positive integers, and K is equal to a total number of users ([0095], The set size s can be between 1 to s.sub.max where M>>s.sub.max.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Meng with wherein the second preset condition includes at least one of: a number of the selected users being not less than α; the number of the selected users being not more than μ; the number of the selected users being ξ; or the number of the selected users being not less than α and not more than μ; wherein α≤K, μ≤K, ξ≤K, and α, μ and ξ are all positive integers, and K is equal to a total number of users as taught by ‘4841 to provide load balancing capabilities (‘4841, [0004]).
As for claim 12, Meng does not explicitly disclose the third preset condition includes a minimum throughput among throughputs of the selected users being not less than a first throughput threshold.
However, ‘4841 discloses the third preset condition includes a minimum throughput among throughputs of the selected users being not less than a first throughput threshold ([0018], the utility function is the minimum of user rates).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Meng with the third preset condition includes a minimum throughput among throughputs of the selected users being not less than a first throughput threshold as taught by ‘4841 to provide load balancing capabilities (‘4841, [0004]).
5. Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meng et al, WO 2020/199687 A1 in view of US 2018/004137 hereafter ‘1378.
As for claim 8, Meng does not explicitly disclose the user association information constitutes a matrix of K rows and K columns, diagonal elements of the matrix are 1, and K is equal to a total number of users; and each element in the matrix represents user association information between a user corresponding to a row where the element is located and a user corresponding to a column where the element is located.
However, ‘1378 discloses the user association information constitutes a matrix of K rows and K columns, diagonal elements of the matrix are 1, and K is equal to a total number of users; and each element in the matrix represents user association information between a user corresponding to a row where the element is located and a user corresponding to a column where the element is located ([0017], The 2-dimensional matrix comprises a first dimension for categories with which user endpoint devices may be associated and a second dimension for the network elements. The first and second dimensions are represented by rows and columns of the matrix, respectively. Then, element ij of the matrix (i.e., row i and column j) is for a user group that includes user endpoint devices associated category i that are associated with network element j. When the matrix is an m by n matrix, m×n user groups may be established.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Meng with the user association information constitutes a matrix of K rows and K columns, diagonal elements of the matrix are 1, and K is equal to a total number of users; and each element in the matrix represents user association information between a user corresponding to a row where the element is located and a user corresponding to a column where the element is located as taught by ‘4137 to provide an efficient method for trouble isolation (‘1378, [0012]).
Allowable Subject Matter
6. Claims 5, 14-15 and 18 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
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US 2008/0219194 discloses in step 410, the base station calculates throughput for each of the generated terminal groups, selects a particular terminal group, a sum of the calculated throughput of which is maximized, determines an optimal beam subset corresponding to the particular terminal group, and generates random beams of the optimal beam subset.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENEE HOLLAND whose telephone number is (571)270-7196. The examiner can normally be reached 8:30 AM - 5:00 PM.
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JENEE HOLLAND
Examiner
Art Unit 2469
/JENEE HOLLAND/Primary Examiner, Art Unit 2469