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 .
Response to Amendment
Applicant’s amendment filed on 08/25/2026 has been entered. Claims 1 and 12-13 have been amended. No Claim has been canceled in this amendment. No New Claim has been added in this amendment.
Claims 1-13 are pending in this application, with claims 1,12 and 13 being independent.
Response to Arguments
Applicant's arguments with respect to objections for the disclosure have been considered and are persuasive. Therefore, the objections are withdrawn.
Applicant's arguments with respect to Claim Objections have been considered and are persuasive. Therefore, the Objections are withdrawn.
Applicant’s arguments with respect to rejection of Claims 1 and 12-13 under 35 U.S.C. §103 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
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.
Claims 1 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over ZHANG et al. (US 2023/0239784 Al, hereinafter referred to as “Zhang”)-IDS in view of HONG et al. (US 2022/0066526 Al, hereinafter referred to as “Mukherji”) and further in view of HONG et al. (US 2022/0066526 Al, hereinafter referred to as “Hong”) .
Regarding claims 1 and 12-13, Zhang discloses a network energy consumption management system (Zhang Fig.3 A network (i.e. system)) and a method, comprising: a training unit configured to acquire training data from at least one network node and train an energy consumption management model using the training data to obtain a trained energy consumption management model (Zhang Fig.3 Para[0108-113] A machine learning model using the load characteristic (i.e. training data) of a cell (i.e. network node)); a prediction unit configured to obtain input data from the at least one network node and provide the input data to the trained energy consumption management model to obtain output strategy data (Zhang Fig.3 Para[0114-115] The load trend of the target cell is predicted using the trained machine learning prediction model); and an execution unit configured to control the at least one network node to perform an energy saving operation based on the strategy data (Zhang Fig.3 Para[0107,0120] The energy saving strategy is determined and sent to the target cell).
Zhang does not explicitly disclose wherein the training data and the input data comprise movement track prediction or movement history information of the at least one network node.
However, Mukherji from the same field of invention discloses wherein the training data and the input data comprise movement track prediction or movement history information of the at least one network node (Mukherji Col:6,9 A machine learning model used for movement analysis and input to the model is Location estimates (i.e. movement history)).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zhang and Mukherji to have the feature of “wherein the training data and the input data comprise movement track prediction or movement history information of the at least one network node” as taught by Mukherji. The motivation would have been to characterize movement of nodes in the network (Mukherji Col:1).
Zhang in view of Mukherji does not explicitly disclose wherein the energy consumption management model comprises: a status prediction module configured to predict one or more of: user location information, traffic load and energy consumption of the at least one network node based on the input data; and an energy saving strategy module configured to make an energy saving strategy based on the predicted user location information, the predicted traffic load, the predicted energy consumption of the at least one network node, and a current status of a target network node in the at least one network node to be subjected to energy consumption management.
However, Hong from the same field of invention discloses wherein the energy consumption management model comprises: a status prediction module configured to predict one or more of: user location information (Not given patentable weight due to non-selective option in the claim), traffic load and energy consumption of the at least one network node (Not given patentable weight due to non-selective option in the claim) based on the input data (Hong Para[0073-78,0093-97] A prediction of load balancing is used to determine an action (i.e. status)); and an energy saving strategy module configured to make an energy saving strategy based on the predicted user location information (Not given patentable weight due to non-selective option in the claim), the predicted traffic load, the predicted energy consumption of the at least one network node (Not given patentable weight due to non-selective option in the claim), and a current status of a target network node in the at least one network node to be subjected to energy consumption management (Hong Para[0073-78,0093-97] The repeated learning is used to acquire a status prediction and used to determine an optimum energy dispatch (i.e. strategy) policy, see [0080]).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zhang and Mukherji to have the feature of “wherein the energy consumption management model comprises: a status prediction module configured to predict one or more of: user location information, traffic load and energy consumption of the at least one network node based on the input data; and an energy saving strategy module configured to make an energy saving strategy based on the predicted user location information, the predicted traffic load, the predicted energy consumption of the at least one network node, and a current status of a target network node in the at least one network node to be subjected to energy consumption management” as taught by Hong. The motivation would have been to manage energy in a self-powered network (Hong Para[0002]).
Specifically for claims 1 and 13, Zhang discloses the system that includes a processor (Zhang Fig.17 A processor), memory (Zhang Fig.17 A memory), and different units (Zhang Fig.6,11).
Claims 2-4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Mukherji, Hong and further in view of REN et al. (US 2024/0265311 Al, hereinafter referred to as “Ren”).
Regarding claim 2, Zhang in view of Mukherji and Hong discloses the system and the method as explained above for Claim 1. Zhang further discloses an administrative unit configured to maintain and manage the at least one network node (Zhang Fig.6 Para[0040] The control unit (i.e. administrative unit) manages a base station). Zhang in view of Mukherji and Hong does not explicitly disclose wherein the target network node of the at least one network node subjected to energy consumption management comprises a central unit and a distribution unit (Ren Fig.3 Para[0066-68] A node with CU and DU).
However, Ren from a similar field of invention discloses wherein the target network node of the at least one network node subjected to energy consumption management comprises a central unit and a distribution unit.
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zhang, Mukherji and Hong to have the feature of “wherein the target network node of the at least one network node subjected to energy consumption management comprises a central unit and a distribution unit” as taught by Ren. The motivation would have been to provide efficient machine learning (Ren Para[0003]).
Regarding claim 3, Zhang in view of Mukherji, Hong and Ren discloses the system and the method as explained above for Claim 1. Zhang further discloses wherein the training unit and the prediction unit are arranged in the administrative unit (Zhang Para[0199,0204,0243] The control unit perform training and prediction).
Regarding claim 4, Zhang in view of Mukherji, Hong and Ren discloses the system and the method as explained above for Claim 1. Zhang further discloses wherein the training unit is arranged in the administrative unit and the prediction unit is arranged in a central unit of the target network node (Zhang Para[0152,0157] The control unit performs training and the base station performs energy saving strategy).
Regarding claim 11, Zhang in view of Mukherji, Hong and Ren discloses the system and the method as explained above for Claim 1. Zhang further discloses wherein, upon receiving the strategy data, the prediction unit sends the strategy data to the target network node via a first interface between the administrative unit and the target network node (Zhang Fig.6 Para[0107] The energy saving strategy is provided to the base station).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Mukherji, Hong, Ren and further in view of SUN et al. (US 2023/0345283 Al, hereinafter referred to as “Sun”).
Regarding claim 6, Zhang in view of Mukherji, Hong and Ren discloses the system and the method as explained above for Claim 1. Zhang in view of Mukherji, Hong and Ren does not explicitly disclose wherein the training unit sends a training data request to the at least one network node via a first interface between the administrative unit and the at least one network node, the at least one network node sends the training data to the training unit via the first interface in response to the training data request.
However, Sun from a similar field of invention discloses wherein the training unit sends a training data request to the at least one network node via a first interface between the administrative unit and the at least one network node, the at least one network node sends the training data to the training unit via the first interface in response to the training data request (Sun Fig.1 Para[0108] A direct path (i.e. interface) between devices for training data request and response).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zhang, Mukherji, Hong and Ren to have the feature of “wherein the training unit sends a training data request to the at least one network node via a first interface between the administrative unit and the at least one network node, the at least one network node sends the training data to the training unit via the first interface in response to the training data request” as taught by Sun. The motivation would have been to provide efficient machine learning (Sun Para[0003]).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Mukherji, Hong, Ren and further in view of PANTELIDOU et al. (US 2024/0098586 Al, hereinafter referred to as “Pante-8586”).
Regarding claim 8, Zhang in view of Mukherji, Hong and Ren discloses the system and the method as explained above for Claim 1. Zhang in view of Mukherji, Hong and Ren does not explicitly disclose wherein, the prediction unit sends an input data request to the at least one network node via a first interface between the administrative unit and the at least one network node, the at least one network node sends the input data to the prediction unit via the first interface in response to the input data request.
However, Pante-8586 from a similar field of invention discloses wherein, the prediction unit sends an input data request to the at least one network node via a first interface between the administrative unit and the at least one network node, the at least one network node sends the input data to the prediction unit via the first interface in response to the input data request (Pante-8586 Fig.1,4 Para[0092-94] A request for predicted data and response received).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zhang, Mukherji, Hong and Ren to have the feature of “wherein, the prediction unit sends an input data request to the at least one network node via a first interface between the administrative unit and the at least one network node, the at least one network node sends the input data to the prediction unit via the first interface in response to the input data request” as taught by Pante-8586. The motivation would have been to improve network performance with machine learning predicted data (Pante-8586 Para[0002]).
Regarding claim 9, Zhang in view of Mukherji, Hong and Ren discloses the system and the method as explained above for Claim 1. Zhang in view of Mukherji, Hong and Ren does not explicitly disclose wherein the prediction unit sends an input data request to another network node via a second interface between the target network node and the other network node, the other network node sends the training data to the prediction unit via the second interface in response to the input data request.
However, Pante-8586 from a similar field of invention discloses wherein the prediction unit sends an input data request to another network node via a second interface between the target network node and the other network node, the other network node sends the training data to the prediction unit via the second interface in response to the input data request (Pante-8586 Fig.1,4 Para[0092-94] A request for predicted data and response received).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zhang, Mukherji, Hong and Ren to have the feature of “wherein the prediction unit sends an input data request to another network node via a second interface between the target network node and the other network node, the other network node sends the training data to the prediction unit via the second interface in response to the input data request” as taught by Pante-8586. The motivation would have been to improve network performance with machine learning predicted data (Pante-8586 Para[0002]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Mukherji, Hong, Ren and further in view of ALI et al. (US 2024/0155553 Al, hereinafter referred to as “Ali”).
Regarding claim 10, Zhang in view of Mukherji, Hong and Ren discloses the system and the method as explained above for Claim 1. Zhang in view of Mukherji, Hong and Ren does not explicitly disclose wherein, the prediction unit further sends an energy consumption management model request to the administrative unit via a first interface between the administrative unit and the target network node, the administrative unit sends the trained energy consumption management model to the prediction unit via the first interface in response to the energy consumption management model request.
However, Ali from a similar field of invention discloses wherein, the prediction unit further sends an energy consumption management model request to the administrative unit via a first interface between the administrative unit and the target network node, the administrative unit sends the trained energy consumption management model to the prediction unit via the first interface in response to the energy consumption management model request (Ali Fig.6 Para[0072] The trained ML model is communicate between server and base station).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zhang, Mukherji, Hong and Ren to have the feature of “wherein, the prediction unit further sends an energy consumption management model request to the administrative unit via a first interface between the administrative unit and the target network node, the administrative unit sends the trained energy consumption management model to the prediction unit via the first interface in response to the energy consumption management model request” as taught by Ali. The motivation would have been to use machine learning for orientation detection in UE and improve user experience (Ali Para[0038]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Mukherji, Hong, Ren and further in view of Pantelidou et al. (US 2023/0044727 Al, hereinafter referred to as “Pantelidou”).
Regarding claim 5, Zhang in view of Mukherji, Hong and Ren discloses the system and the method as explained above for Claim 1. Zhang in view of Mukherji, Hong and Ren does not explicitly disclose wherein the training unit and the prediction unit are arranged in a central unit of the target network node.
However, Pantelidou from a similar field of invention discloses wherein the training unit and the prediction unit are arranged in a central unit of the target network node (Pantelidou Para[0070] The ML model at the CU).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zhang, Mukherji, Hong and Ren to have the feature of “wherein the training unit and the prediction unit are arranged in a central unit of the target network node” as taught by Pantelidou. The motivation would have been to provide increased user experience (Pantelidou Para[0009]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Mukherji, Hong, Ren, Pantelidou and further in view of Sun.
Regarding claim 7, Zhang in view of Mukherji, Hong, Ren and Pantelidou discloses the system and the method as explained above for Claim 1. Zhang in view of Mukherji, Hong, Ren and Pantelidou does not explicitly disclose wherein, the training unit sends a training data request to another network node via a second interface between the target network node and the other network node, the other network node sends the training data to the training unit via the second interface in response to the training data request.
However, Sun from a similar field of invention discloses wherein, the training unit sends a training data request to another network node via a second interface between the target network node and the other network node, the other network node sends the training data to the training unit via the second interface in response to the training data request (Sun Fig.1 Para[0108] A direct path (i.e. interface) between devices for training data request and response).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zhang, Mukherji, Hong, Ren and Pantelidou to have the feature of “wherein, the training unit sends a training data request to another network node via a second interface between the target network node and the other network node, the other network node sends the training data to the training unit via the second interface in response to the training data request” as taught by Sun. The motivation would have been to provide efficient machine learning (Sun Para[0003])
Although specific columns, figures, reference numerals, lines of the reference(s), etc. have been referred to, Applicant should consider the entire applied prior art reference(s).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sudesh M. Patidar whose telephone number is (571)272-2768. The examiner can normally be reached M-F:: 10AM-6:30PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Rutkowski can be reached at (571) 270-1215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Sudesh M. Patidar/Primary Examiner, Art Unit 2415
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