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 Remarks
This Office action is considered fully responsive to the amendments filed 06/18/2026.
Response to Arguments
Applicant’s arguments, see Remarks, filed 06/18/2026, with respect to the rejection(s) of claim(s) 1-20 under U.S.C. 102 and/or U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of WO 2022057268 A1.
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.
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.
Claim(s) 1-5, 8, 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2023/0397102 A1 to Filin et al. (“Filin”) [provided by Applicant] in view of WO 2022057268 A1 to LYU et al. (“Lyu”) [Examiner cites to attached English translation].
As to claim 1, see similar rejection to claim 15. The apparatus teaches the
method. Separate markets are equivalent to separate functions.
As to claim 2, see similar rejection to claim 16. The apparatus teaches the
method.
As to claim 3, Filin further discloses the method of claim 1, wherein employing
Separate network energy saving model on the current network performance and
topology information to set the energy saving configuration for the at least one cell
comprises: determining if a first current load on the at least one cell and a second
current load on a primary cell associated with the selected cell match a predicted
amount of load (para. 0309, 0325, 0349, In the example in FIGS. 27 and 28, the base
station 2701 may decide to execute energy saving plan, for example, based on the
results of determination of the system-wide change in energy efficiency (i.e. match a
predicted amount of load) [para. 0213, if traffic load in a capacity booster cell is less
than a threshold, a base station managing this capacity booster cell may decide to
deactivate this cell to reduce energy consumption (e.g., energy cost) by this cell] and
based on the results of confirmation of the coverage compensation. The base station
2701 may select the cell 2 of the base station 2702 and the cell 4 of the base station
2704 as the compensating cells. For example, the cells 2 and 4 will be used for
offloading wireless devices from the cell 1. The base station 2701 may send to the base
station 2702 and to the base station 2704 the decision to execute energy saving plan
2728. The base station 2701 may select the cell 3 of the base station 2703 and the cell
5 of the base station 2705 as the compensating cells. For example, the cells 3 and 5 will
be used for coverage compensation for the cell 1. For example, the cells 3 and 5 may
also be used for offloading wireless devices from the cell 1.); and in response to
determining that the first current load on the at least one cell and the second current
load on the primary cell associated with the selected cell match the predicted amount of
load: setting the energy saving configuration for the at least one cell to power down
(para. 0309, 0325, 0349, In the example in FIGS. 27 and 28, the base station 2701 may
decide to execute energy saving plan, for example, based on the results of
determination of the system-wide change in energy efficiency and based on the results
of confirmation of the coverage compensation. The base station 2701 may select the
cell 2 of the base station 2702 and the cell 4 of the base station 2704 as the
compensating cells. For example, the cells 2 and 4 will be used for offloading wireless
devices from the cell 1. The base station 2701 may send to the base station 2702 and
to the base station 2704 the decision to execute energy saving plan 2728. The base
station 2701 may select the cell 3 of the base station 2703 and the cell 5 of the base
station 2705 as the compensating cells. For example, the cells 3 and 5 will be used for
coverage compensation for the cell 1. For example, the cells 3 and 5 may also be used
for offloading wireless devices from the cell 1 (i.e. power down).); and labeling the user
devices for offloading from the at least one cell to the primary cell (para. 0309, 0325,
0349, In the example in FIGS. 27 and 28, the base station 2701 may decide to execute
energy saving plan, for example, based on the results of determination of the system
wide change in energy efficiency and based on the results of confirmation of the
coverage compensation. The base station 2701 may select the cell 2 of the base station
2702 and the cell 4 of the base station 2704 as the compensating cells. For example,
the cells 2 and 4 will be used for offloading wireless devices from the cell 1. The base
station 2701 may send to the base station 2702 and to the base station 2704 the
decision to execute energy saving plan 2728. The base station 2701 may select the cell
3 of the base station 2703 and the cell 5 of the base station 2705 as the compensating
cells. For example, the cells 3 and 5 will be used for coverage compensation for the cell
1. For example, the cells 3 and 5 may also be used for offloading wireless devices from
the cell 1.).
Filin does not expressly disclose for each separate network function associated with the least one cell.
Lyu discloses at pages 2-4, deployment scenarios…have different characteristics…AI-based energy-saving models based on these characteristics (i.e. for each separate network function)…determining an energy saving policy for a base station (i.e. associated with the least one cell) …a first model training model, used for training multiple preset machine learning models …determines an energy-saving strategy for the target base station according to the fusion model.
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the models as taught by Lyu into
the invention of Filin. The suggestion/motivation would have been to determine a power saving policy of a base station (page 2, Remarks). Including the models as taught by Lyu into the invention of Filin was within the ordinary ability of one of ordinary skill in the art based on the teachings of Lyu.
As to claim 4, see similar rejection to claim 17. The apparatus teaches the
method.
As to claim 5, see similar rejection to claim 18. The apparatus teaches the
method.
As to claim 8, see similar rejection to claim 19. The apparatus teaches the
method.
As to claim 12, Filin further discloses the method of claim 1, wherein generating
the at least one network energy saving model comprises: generating a separate
network energy saving model for each separate subset of cells of a plurality of cells in
the cellular network (para. 0292-0293, the base station 2501 may select candidate
compensating cell(s) using AI/ML (i.e. model) capabilities (i.e. plural capabilities, plural
cells); para. 0297, Optionally, the base station 2502 and the base station 2504 may
perform the determination using AI/ML capabilities (e.g., using their own AI/ML models,
not shown in FIG. 25). The base station 2502 and the base station 2504 may send to
the base station 2501 the energy cost determination responses 2519, i.e. alternatively,
many models for each base station (being a cell)).
Filin does not expressly disclose the plurality of network energy saving models.
Lyu discloses at pages 2-4, deployment scenarios…have different characteristics…AI-based energy-saving models based on these characteristics…determining an energy saving policy for a base station …a first model training model, used for training multiple preset machine learning models …determines an energy-saving strategy for the target base station according to the fusion model.
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the models as taught by Lyu into
the invention of Filin. The suggestion/motivation would have been to determine a power saving policy of a base station (page 2, Remarks). Including the models as taught by Lyu into the invention of Filin was within the ordinary ability of one of ordinary skill in the art based on the teachings of Lyu.
As to claim 13, Filin and Lyu further discloses the method of claim 1, further comprising: modifying the energy saving configuration for the at least one cell to maintain a minimum quality of experience threshold for the cellular network (Filian, para. 0213, if traffic load in a capacity booster cell is less than a threshold, a base station managing this capacity booster cell may decide to deactivate this cell to reduce energy consumption (e.g., energy cost) by this cell, i.e. to keep it at or above the threshold). In addition, as the primary reference teaches the instant claim limitations, the same suggestion/motivation of claim 1 applies.
As to claim 14, Filin and Lyu further discloses the method of claim 1, further comprising:
storing a predicted load on a primary cell of the cellular network (Filin, para. 0253, An energy consumption (e.g., energy cost) determination may refer, for example, to an energy, resource, and/or power consumption determination, an energy, resource, and/or power consumption estimate, an energy, resource, and/or power consumption prediction; para. 0285, base station 1. Another example deployment of the functional architecture of FIG. 17 shown in FIG. 19 is, for example, if both the Model Training function 1702 and the Model Inference function 1703 are deployed in the base station 1; para. 0291, The input data for model inference may comprise measurements, predictions, and statistics. The base station 2501 may perform Model inference 2517); obtaining an actual load on the primary cell after utilization of the energy saving configuration for the at least one cell (Filin, para. 0296-0298, The base station 2501 may receive from the base station 2502 and from the base station 2504 the energy cost determination responses 2519 (i.e. measurements)); and employing the predicted load and the actual load on the primary cell as feedback to modify at least one network energy saving model (Filin, para. 0217, Input data from the Data Collection function 1701 to the Model Inference function 1703 may be called Inference Data. It may be used to generate an output in the Model Inference function 1702. It may also be used to generate Model Performance Feedback in the Model Inference function 1702. Examples of the Inference Data may include measurements, predictions, and statistics). In addition, as the primary reference teaches the instant claim limitations, the same suggestion/motivation of claim 1 applies.
As to claim 15, Filin discloses a system (para. 0291-0294, fig. 25), comprising: an
energy saving model manager (para. 0291, base station 2501) configured to: obtain
historical network performance and topology information regarding cells of a cellular
network (para. 0290-0294, UE…base stations…send input data for model inference
2516 to the base station 2501…The input data for model inference may comprise
measurements (i.e. historical performance), predictions, and statistics (i.e. historical
performance); base station 2501 may select candidate compensating cell(s) for the
candidate energy saving cell 25011. A candidate compensating cell may refer, for
example, to one of the neighbor cells (i.e. topology) of the candidate energy saving cell);
generate at least one network energy saving model from the historical network
performance and topology information (para. 0290-0295, The input data for model
inference may comprise measurements, predictions, and statistics. The base station
2501 may perform Model inference 2517…The base station 2501 may select candidate
compensating cell(s) for the candidate energy saving cell 25011); obtain current
network performance and topology information regarding the cells of the cellular
network (para. 0296-0298, The base station 2501 may receive from the base station
2502 and from the base station 2504 the energy cost determination responses 2519.);
employ the at least one network energy saving model on the current network
performance and topology information to set an energy saving configuration for at least
one cell in the cellular network (para. 0299-0305, The base station 2501 may decide
whether to execute the energy saving plan, for example, based on the results of
evaluation…for the energy saving cell 1); and a network energy saving configuration
manager (para. 0305, base station 2501) configured to: utilize the energy saving
configuration to reduce energy utilized by the at least one cell (para. 0305, Based on the
results of evaluation, the base station 2501 may decide whether to execute the energy
saving plan. In the example in FIG. 25, the base station 2501 may decide to execute the
energy saving plan and to use cell 2 and cell 3 as the compensating cells for the energy
saving cell).
Filin does not expressly disclose a plurality of network energy saving models…each separate network energy saving model of the plurality of network energy saving models is generated for separate markets within the cellular network…regarding at least one target cell in a target market…select at least one network energy saving model from the plurality of network energy saving models that corresponds to the target market…the at least one target cell.
Lyu discloses at pages 2-4, deployment scenarios…have different characteristics…AI-based energy-saving models based on these characteristics (i.e. a plurality of network energy saving models…each separate network energy saving model of the plurality of network energy saving models is generated for separate markets within the cellular network)…determining an energy saving policy for a base station (i.e. regarding at least one target cell in a target market…the at least one target cell) …a first model training model, used for training multiple preset machine learning models …determines an energy-saving strategy for the target base station according to the fusion model (i.e. select at least one network energy saving model from the plurality of network energy saving models that corresponds to the target market).
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the models as taught by Lyu into
the invention of Filin. The suggestion/motivation would have been to determine a power saving policy of a base station (page 2, Remarks). Including the models as taught by Lyu into the invention of Filin was within the ordinary ability of one of ordinary skill in the art based on the teachings of Lyu.
As to claim 16, Filin further discloses the system of claim 15, wherein the energy
saving model manager employs the at least one network energy saving model on the
current network performance and topology information to set the energy saving
configuration for the at least one cell by being further configured to: determine if
offloading of user devices from the at least one cell to a primary cell is possible (paras.
0309, 0325, 0349, The energy cost determination request may include number of
wireless devices/amount of traffic/amount of load to be offloaded (i.e. is possible) from
the candidate energy saving cell to the candidate compensating cell); and in response
to determining that offloading of user devices from the at least one cell to the primary
cell is possible: set the energy saving configuration for the at least one cell to power
down (paras. 0309, 0325, 0349, The energy cost determination request may include
number of wireless devices/amount of traffic/amount of load to be offloaded from the
candidate energy saving cell (i.e. a power down) to the candidate compensating cell);
and label the user devices for offloading from the at least one cell to the primary cell
(paras. 0309, 0325, 0349, The energy cost determination request may include number
of wireless devices/amount of traffic/amount of load to be offloaded from the candidate
energy saving cell to the candidate compensating cell).
Filin does not expressly disclose target cell.
Lyu discloses at pages 2-4, deployment scenarios…have different characteristics…AI-based energy-saving models based on these characteristics (i.e. a plurality of network energy saving models…each separate network energy saving model of the plurality of network energy saving models is generated for separate markets within the cellular network)…determining an energy saving policy for a base station (i.e. regarding at least one target cell in a target market…the at least one target cell) …a first model training model, used for training multiple preset machine learning models …determines an energy-saving strategy for the target base station according to the fusion model (i.e. select at least one network energy saving model from the plurality of network energy saving models that corresponds to the target market).
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the models as taught by Lyu into
the invention of Filin. The suggestion/motivation would have been to determine a power saving policy of a base station (page 2, Remarks). Including the models as taught by Lyu into the invention of Filin was within the ordinary ability of one of ordinary skill in the art based on the teachings of Lyu.
As to claim 17, Filin further discloses the system of claim 15, wherein the energy
saving model manager employs the at least one network energy saving model on the
current network performance and topology information to set the energy saving
configuration for the at least one cell by being further configured to: determine that the
at least one cell is labeled for full energy savings (para. 0215, By factoring overall (i.e.
full) energy consumption (e.g., energy cost) in a decision to deactivate a cell of a base
station and/or offload one or more wireless devices, system-wide energy saving may be
realized without affecting the functioning of the offloaded wireless devices); and set the
energy saving configuration for the at least one cell to power down (para. 0215, By
factoring overall (i.e. full) energy consumption (e.g., energy cost) in a decision to
deactivate (i.e. power down) a cell of a base station and/or offload (i.e. power down)
one or more wireless devices, system-wide energy saving may be realized without
affecting the functioning of the offloaded wireless devices).
Filin does not expressly disclose target cell.
Lyu discloses at pages 2-4, deployment scenarios…have different characteristics…AI-based energy-saving models based on these characteristics (i.e. a plurality of network energy saving models…each separate network energy saving model of the plurality of network energy saving models is generated for separate markets within the cellular network)…determining an energy saving policy for a base station (i.e. regarding at least one target cell in a target market…the at least one target cell) …a first model training model, used for training multiple preset machine learning models …determines an energy-saving strategy for the target base station according to the fusion model (i.e. select at least one network energy saving model from the plurality of network energy saving models that corresponds to the target market).
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the models as taught by Lyu into
the invention of Filin. The suggestion/motivation would have been to determine a power saving policy of a base station (page 2, Remarks). Including the models as taught by Lyu into the invention of Filin was within the ordinary ability of one of ordinary skill in the art based on the teachings of Lyu.
As to claim 18, Filin further discloses the system of claim 15, wherein the energy
saving model manager employs the at least one network energy saving model on the
current network performance and topology information to set the energy saving
configuration for the at least one cell by being further configured to: determine that the
at least one cell is labeled for partial energy savings (para. 0260, a cell that provides
some level of compensation to the candidate energy saving cell. For example, a
candidate compensating cell may be used for offloading all or some part (i.e. partial) of
traffic from a candidate energy saving cell.); and set the energy saving configuration to
reduce energy to at least one component associated with the at least one cell (para.
0260, a cell that provides some level of compensation to the candidate energy saving
cell. For example, a candidate compensating cell may be used for offloading (i.e.
reducing energy) all or some part of traffic from a candidate energy saving cell).
As to claim 19, Filin further discloses the system of claim 15, wherein the energy
saving model manager generates the at least one network energy saving model from
the historical network performance and topology information by being further configured
to: employ at least one artificial intelligence mechanism to train the at least one network
energy saving model from the historical network performance and topology information
regarding the cellular network (para. 0295, the Model inference 2517 may have an
output. The output of the Model inference 2517 may be based on the Model training
2514 (e.g., a deployment and/or update of an AI/ML model residing at base station
2501), the wireless device (e.g., UE) measurement report 2515, and/or the input data
for energy saving model inference 2516. The output of the Model inference 2517 may
comprise an energy saving plan.).
Filin does not expressly disclose target cell.
Lyu discloses at pages 2-4, deployment scenarios…have different characteristics…AI-based energy-saving models based on these characteristics (i.e. a plurality of network energy saving models…each separate network energy saving model of the plurality of network energy saving models is generated for separate markets within the cellular network)…determining an energy saving policy for a base station (i.e. regarding at least one target cell in a target market…the at least one target cell) …a first model training model, used for training multiple preset machine learning models …determines an energy-saving strategy for the target base station according to the fusion model (i.e. select at least one network energy saving model from the plurality of network energy saving models that corresponds to the target market).
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the models as taught by Lyu into
the invention of Filin. The suggestion/motivation would have been to determine a power saving policy of a base station (page 2, Remarks). Including the models as taught by Lyu into the invention of Filin was within the ordinary ability of one of ordinary skill in the art based on the teachings of Lyu.
As to claim 20, Filin discloses a computing device (fig. 15B, para. 0031,
computing device…any various devices described…; para. 0291, base station 2501),
comprising: a memory that stores computer instructions (para. 0206, computer
executable instructions stored on a computer-readable medium); and a processor
system that executes the computer instructions to (para. 0206, a processor executing
computer-executable instructions stored on a computer-readable medium): obtain
historical network performance and topology information regarding a cellular network
(para. 0290-0294, UE…base stations…send input data for model inference 2516 to the
base station 2501…The input data for model inference may comprise measurements
(i.e. historical performance), predictions, and statistics (i.e. historical performance);
base station 2501 may select candidate compensating cell(s) for the candidate energy
saving cell 25011. A candidate compensating cell may refer, for example, to one of the
neighbor cells (i.e. topology) of the candidate energy saving cell); generate at least one
network energy saving model from the historical network performance and topology
information (para. 0290-0295, The input data for model inference may comprise
measurements, predictions, and statistics. The base station 2501 may perform Model
inference 2517…The base station 2501 may select candidate compensating cell(s) for
the candidate energy saving cell 25011); obtain current network performance and
topology information regarding at least one cell of the cellular network (para. 0296-0298,
The base station 2501 may receive from the base station 2502 and from the base
station 2504 the energy cost determination responses 2519.); employ the at least one
network energy saving model on the current network performance and topology
information to set an energy saving configuration for the at least one cell (para. 0299
0305, The base station 2501 may decide whether to execute the energy saving plan, for
example, based on the results of evaluation…for the energy saving cell 1); and utilize
the energy saving configuration to reduce energy consumption by the at least one cell
(para. 0305, Based on the results of evaluation, the base station 2501 may decide
whether to execute the energy saving plan. In the example in FIG. 25, the base station
2501 may decide to execute the energy saving plan and to use cell 2 and cell 3 as the
compensating cells for the energy saving cell).
Filin does not expressly disclose and in response to one or more quality of experience thresholds being exceeded with respect to the at least one cell, modify the energy saving configuration for the at least one cell.
Lyu discloses the recommendation degree of the energy-saving strategy is obtained by formulas 1-6. If the two models with the highest (i.e. above threshold) recommendation degree are selected for fusion, the optimal recommendation of energy-saving strategies is realized, and the recommended energy-saving strategy model is used to calculate the threshold value to select the energy-saving technology (page 10). Further, at pages 2-4, deployment scenarios…have different characteristics…AI-based energy-saving models based on these characteristics …determining an energy saving policy for a base station (at least one cell).
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the models as taught by Lyu into
the invention of Filin. The suggestion/motivation would have been to determine a power saving policy of a base station (page 2, Remarks). Including the models as taught by Lyu into the invention of Filin was within the ordinary ability of one of ordinary skill in the art based on the teachings of Lyu.
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.
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.
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Publication No. 2023/0397102 A1 to Filin et al. (“Filin”) [provided by Applicant] in view of WO 2022057268 A1 to LYU et al. (“Lyu”) [Examiner cites to attached English translation] and in further view of U.S. Publication No. 2023/0370973 A1 to Santhanam et al. (“Santhanam”).
As to claim 6, Filin and Lyu does not expressly disclose the method of claim 5, wherein
setting the energy saving configuration to reduce energy to the at least one component
associated with the at least one cell comprises: setting the energy saving configuration
to reduce transmit energy of a power amplifier of a radio unit of the at least one cell.
Santhanam discloses an example of the one or more components that may be
powered off in an effort to save power may be baseband circuitry (e.g., an antenna, an
amplifier, a filter, a tuner, or a detector of the UE 115-a). Baseband circuitry may be
responsible for receiving and transmitting radio signals (para. 0066).
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the power saving as taught by
Santhanam into the invention of Filin and Lyu. The suggestion/motivation would have been to save power during periods of inactivity (Santhanam, para. 0003). Including the power saving as taught by Santhanam into the invention of Filin was within the ordinary ability of one of ordinary skill in the art based on the teachings of Santhanam and Lyu.
As to claim 7, Filin and Lyu does not expressly disclose the method of claim 5, wherein setting the energy saving configuration to reduce energy to the at least one component associated with the at least one cell comprises: setting the energy saving configuration to reduce number of transmission antennas being utilized by the at least one cell.
Santhanam discloses antennas (para. 0039) and an example of the one or more
components that may be powered off in an effort to save power may be baseband
circuitry (e.g., an antenna, an amplifier, a filter, a tuner, or a detector of the UE 115-a).
Baseband circuitry may be responsible for receiving and transmitting radio signals
(para. 0066).
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the power saving as taught by
Santhanam into the invention of Filin and Lyu. The suggestion/motivation would have been to
save power during periods of inactivity (Santhanam, para. 0003). Including the power
saving as taught by Santhanam into the invention of Filin and Lyu was within the ordinary ability of one of ordinary skill in the art based on the teachings of Santhanam.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S.
Publication No. 2023/0397102 A1 to Filin et al. (“Filin”) [provided by Applicant] in view of WO 2022057268 A1 to LYU et al. (“Lyu”) [Examiner cites to attached English translation] and in further view of U.S. Publication No. 2025/0220571 A1 to Kumar et al. (“Kumar”).
As to claim 9, Filin and Lyu does not expressly disclose the method of claim 1, wherein generating the plurality of network energy saving models comprises: receiving manual input from an administrator defining at least one network energy saving model.
Kumar discloses the administrator and admin device 111 may comprise IT
personnel (i.e. manual) (para. 0027), further VNA/AI engine 350 (i.e. model) may
generate recommendations regarding sustainability and efficiency of APs and switches.
VNA/AI engine 350 may generate recommendations to disable or reduce power
provided to one or more APs and/or switches based on the redundancy of those
switches. For example, VNA/AI engine 350 may recommend (i.e. as this is in response
from the model, it is an input; additionally the primary reference discloses training based
on feed back/measurements) that an administrator (i.e. manual) disable an AP to
reduce power consumption within a site, based on that AP having a sufficient
redundancy score (e.g., there being enough other APs to support clients of the disabled
AP). In addition (i.e. another interpretation that model receives input), VNA/AI engine
350 may determine that a given switch has a sufficient redundancy score to enable the
rebooting and servicing of the switch during regular hours (e.g., other switches and APs
can support the clients associated with the given switch while the given switch is offline)
(para. 0098). Even more (i.e. another interpretation that model receives input), ML
model 380 may generate recommendations based on weighted redundancy scores. ML
model 380 may include one or types of machine learning models, such as clustering
models. A developer or administrator may train ML model 380 based on one or more
objectives that include optimizing wireless service coverage by APs, ensuring sufficient
AP redundancy to avoid flooding of APs in case of failure (e.g., avoiding flooding a
single AP with clients in case of another AP failing), and/or avoiding flooding of switches
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the power saving as taught by Kumar
into the invention of Filin and Lyu. The suggestion/motivation would have been to monitor and troubleshoot computer networks (Kumar, para. 0002). Including the power saving as taught by Kumar into the invention of Filin and Lyu was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kumar.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S.
Publication No. 2023/0397102 A1 to Filin et al. (“Filin”) [provided by Applicant] in view of WO 2022057268 A1 to LYU et al. (“Lyu”) [Examiner cites to attached English translation] and in further view of U.S. Publication No. 2023/0362807 A1 to Kodaypak et al. (“Kodaypak”).
As to claim 10, Filin and Lyu does not expressly disclose the method of claim 1, wherein generating the plurality of network energy saving model comprises: receiving vendorgenerated functions that define the at least one network energy saving model.
Kodaypak discloses an example of this could be an Auto OEM, as an IoT
service provider (i.e. vendor) trying to extract the mobility context/state of its installed
5G IoT devices within their automotive fleet in a parking lot or dealership center from the
network provider (i.e. vendor) via CDDAF-C, to perform modifications to their firmware,
software, energy saving state changes and policies for reporting etc. CDDAF-C 632 has
a single pane of management view of the entire network analytics including energy
efficiency data at the composite level as well as at the individual domain analytics
function (AF) level to be able to synthesize, process and trend the domain specific data
insights as well as energy consumption data in the network. Such trending (i.e. from the
providers) is useful to generate predictive training models based on traffic and/or user
mobility patterns within a region, across multiple regions that span boundaries of service
areas. This trending and predictive training model is very useful for CDDAF-C to trigger
intelligent network and services subscription as well as selection changes via CDDFA-D
when users cross certain regional boundaries with heavy traffic intensity and mobility.
Such a proactive triggering will help in the regional CDDAF-D coordination and
allocation of domain specific network resources and intelligent rerouting with energy
efficiency targets taken into consideration where required due to mobility demands.
(para. 0092).
Prior to the effective filing date of invention, it would have been obvious to a
person of ordinary skill in the art to incorporate the models as taught by Kodaypak into
the invention of Filin and Lyu. The suggestion/motivation would have been to utilize advanced intelligent monitoring and data-driven proactive methods to conserve network resources effectively (Kodaypak, para. 0007). Including the models as taught by Kodaypak into the invention of Filin and Lyu was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kodaypak.
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 OMAR J GHOWRWAL whose telephone number is (571)270-5691. The examiner can normally be reached M-F 9:00am-6:00pm.
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/OMAR J GHOWRWAL/Primary Examiner, Art Unit 2463
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