DETAILED ACTION
This communication is in response to Application No. 18/728,194 filed on 7/11/2024. The preliminary amendment presented on 7/11/2024, which cancels claims 1-20 and adds new claims 21-40, is hereby acknowledged. Claims 21-40 have been examined.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/20/2024 and 10/20/2025 is being considered by the examiner.
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.
Claims 21-22, 25-26, 29-30, 33-36, and 39-40 are rejected under 35 U.S.C. 103 as being unpatentable over Larish et al. (hereinafter Larish)(US 10,039,016) in view of Farooq et al. (hereinafter Farooq)(US 2024/0196231).
Regarding claims 21, 29, and 35, Larish teaches as follows:
A system that operates with a Radio Access Network (RAN), the system comprising: at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor (Device 200 may correspond, for example, to a component of cell tower 110, UE 140, RF optimization platform 150 (equivalent to applicant’s system), or another component of network environment 102. Device 200 may include a bus 210, a processor 220, a memory 230 with software 235, an input component 240, an output component 250, and a communication interface 260, see, col. 3, lines 52-59 and figure 2), cause the system at least to:
identify a plurality of cells within the RAN; group the cells into cell groups (process 700 may also include identifying a target cluster (block 730). For example, RF optimization platform 150 may cluster cell towers 110 and/or UEs 140 into groups according commonalties such as channel conditions, position, applications, and hardware resources… A target cluster 105 may include, for example, a group of cell towers 110 detected to have KPI deviations, a group of cell towers 110 that may be impacted by addition/subtraction of another local cell tower 110, a group of cell towers 110 that are selected for auditing, etc., see, col. 9, lines 36-49 and figure 7);
perform a training process to train group Machine-Learning (ML) models for the cell groups based on training data for the cell groups (process 700 may include receiving a training data set from a golden cluster (block 710) and generating a training model (block 720)… A training model may be extracted using a training data set from the golden cluster. RF optimization platform 150 may extract a model from the training data set with applications to monitoring, optimization and auto tuning network functionalities, see, col. 9, lines 19-34 and figure 7)(process 700 may further include applying a RRM model for the target cluster (block 750) and checking the RRM model for consistency and cell-specific policies (block 760). For example, ML engine 320 may translate the training model to include specific cell towers 110 in target cluster 105 and optimize settings for cell towers 110 in target cluster 105, see, col. 9, lines 60-66) and figure 7);
evaluate a performance of the group ML models for the cell groups based on evaluation data (interpreted as the policies and controls data)) for the cell groups (policies and controls for each cell tower 110 may be obtained and evaluated against the RRM model for compliance. For example, cell-specific policies may reserve resources for particular applications or subscribers. Furthermore, the RRM model may be compared against existing target cluster 105 data to verify application of the RRM model will reduce interference, reduce overshooting, and/or reduce handovers between cell towers 110, see, col. 9, line 66 to col. 10, line 7 and figure 7); and
provide the group ML models for the cell groups to a RAN management system when the performance of the group ML models satisfies a performance threshold (if the target cluster performance is acceptable (block 780—Yes), process 700 may include updating the training data set (block 790). For example, in one implementation, RF optimization platform 150 may update the training data set from the golden cluster to reflect learning from implementing the RRM model for target cluster 105, see, col. 10, lines 33-41).
Larish teaches identifying one target cluster which is equivalent to applicant’s one cell group but does not explicitly teach multiple cell groups.
Farooq teaches as follows:
The step involves extracting meta-features of the training data based on which similar cells can be grouped into clusters (see, ¶ [0045]);
the collaboration server can determine how to cluster the cells of the mobile network based on similarities in the feature vectors between the cells in the aggregation structure (Block 503)(see, ¶ [0063] and figure 5); and
once the clusters of edge nodes/collaboration clients are identified, then the collaboration server sends collaboration information to the edge nodes that identifies the members of the collaborative cells… The collaboration information can provide any information about each of the cluster members to enable each member to communicate with the other cluster members to complete the process such as performing a collaboration pre-check (see, ¶ [0054]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Larish with Farooq to include grouping similar cells into clusters as taught by Farooq in order for efficient collaboration within the cluster members (cells in a same group).
Regarding claims 22, 30, and 36, Farooq teaches as follows:
Receive cell information for the cells; generate feature vectors for the cells based on the cell information (the collaboration server extracts clutter features for each cell in the mobile network from data of a radio planning tool or similar source to form a clutter feature vector for each cell, see, ¶ [0062] and figure 5);
compare the feature vectors for the cells; and group the cells into the cell groups based on a similarity of the feature vectors for the cells (once the feature vector information is aggregated, the collaboration server can determine how to cluster the cells of the mobile network based on similarities in the feature vectors between the cells in the aggregation structure (Block 503). The identification of similarities can use any comparison algorithm, distance determination mechanism, scoring scheme, or other process to identify similarities, see, ¶ [0063] and figure 5).
Therefore, they are rejected for similar reason as presented above.
Regarding claims 25, 33, and 39, Larish teaches as follows:
Request a policy and group the cells into the cell groups based on the policy (tuning/policies module 440 may receive policies and controls on a per-cell basis. For example, tuning/policies module 440 may obtain policies from a network element in network 130 for cell towers 110 that are part of a target cluster 105. Tuning/policies module 440 may evaluate policies and controls for each cell tower 110 against the RRM model for compliance, see, col. 7, lines 14-20 and figure 4).
Regarding claims 26, 34, and 40, Larish in view of Farooq teaches similar limitations as presented above except for selecting a cell group for a new cell.
Larish in view of Farooq teaches clustering cells as presented above.
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Larish in view of Farooq to include adding a new cell into existing clusters in order to efficiently improve wireless network performance and capacity.
Claims 23-24, 31-32, and 37-38 are rejected under 35 U.S.C. 103 as being unpatentable over Larish et al. (hereinafter Larish)(US 10,039,016) in view of Farooq et al. (hereinafter Farooq)(US 2024/0196231), and further in view of Jung (US 2023/0105365).
Regarding claims 23-24, 31-32, and 37-38, Larish in view of Farooq teaches all limitations as presented above except for adjusting cell groups based on performance.
Jung teaches as follows:
In an example wherein the combined error rate (equivalent to applicant’s performance threshold) does not satisfy criteria at 612, the network equipment 200 can modify the number of clusters 620, e.g., by incrementing the input number of clusters. The incrementing can result in, e.g. the five clusters illustrated in FIG. 3, instead of the four clusters illustrated in FIG. 2. The network equipment 200 can repeat the operations 604, 606, 608, 610, and 612 for the new number of input clusters, resulting in the new trained ML models (see, ¶ [0067] and figure 6).
Increasing number of clusters means less number of cells in each cluster.
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Larish in view of Farooq with Jung to include modifying cluster size by adjusting number of clusters as taught by Jung in order to efficiently reduce error rate caused by large cluster size.
Claims 27-28 are rejected under 35 U.S.C. 103 as being unpatentable over Larish et al. (hereinafter Larish)(US 10,039,016) in view of Farooq et al. (hereinafter Farooq)(US 2024/0196231), and further in view of Zhou et al. (hereinafter Zhou)(US 2023/0070855).
Regarding claims 27-28, Larish in view of Farooq teaches all limitations as presented above except for implementing claimed limitations in a RAN Intelligent Controller (RIC) and a gNB Central Unit (gNB CU).
Zhou teaches as follows:
Base station RAN 200 (equivalent to applicant’s gNB) includes a software platform, known as a RAN Intelligent Controller (RIC), that enables the creation of open-source software aligned with an Open-RAN target architecture. RIC 210 includes open-source code to accelerate the deployment of a 5G RAN base station (equivalent to applicant’s gNB). RIC 210 provides a set of standard functions and interfaces that allow for increased optimizations through policy-driven closed loop automation and for faster, more flexible service deployments and programmability within the RAN 200. RIC 210 enables an intelligent, rapidly evolvable radio network by fostering the creation of a multi-vendor open ecosystem of interoperable components for a disaggregated RAN 200 (see, ¶ [0027] and figure 2A).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Larish in view of Farooq with Zhou to include the base station with a software platform known as a RAN Intelligent Controller (RIC) as taught by Zhou in order for increased optimization.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jeong S Park whose telephone number is (571)270-1597. The examiner can normally be reached Monday through Friday 8:00-4:30 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Glenton B Burgess can be reached at 571-272-3949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JEONG S PARK/Primary Examiner, Art Unit 2454
June 21, 2026
Prosecution Insights