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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/02/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Specification
The disclosure is objected to because of the following informalities: in each of Page 30, line 11 and Page 32, line 4 of applicant’s specification, filed 03/18/2024, the term “scream” should read “stream”.
Appropriate correction is required.
Claim Objections
Claims 4 and 14 are objected to because of the following informalities: in line 3 of each of the claims, the term “scream” should read “stream”. Appropriate correction is required.
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 1-4, 6-9, 11-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta Hyde et al. (US 2023/0188233 A1) cited by the applicant, hereinafter referred to as Gupta, in view of MacKenzie et al. (US 11,683,752 B2), hereinafter referred to as MacKenzie.
Regarding claim 1, Gupta teaches a method (Gupta – Paragraph [0028], note a method of operating a wireless network) comprising:
obtaining, by a device, data regarding a networking entity in a computer network and a set of possible configurations for the networking entity (Gupta – Fig. 2A; Paragraph [0085], note to select the configuration of the one or more network components, the processor 202 (of device 200, which may be a network access node, controller of the wireless network, etc., see Paragraph [0084]) may be configured to provide input data 212 to a trained machine learning model 214; Paragraph [0086], note the input data 212 may include information from one or more entities participating in wireless communications at the wireless network; Paragraph [0087], note the network environment 216 that the input data 212 describes may include: load information, traffic volume, type of traffic, cell configuration, average cell capacity, latency, network access time, throughput, time of day, day and/or month, season of the year, wireless device capabilities, network planning and deployment strategy, and/or combinations thereof);
determining, by the device, a power saving mechanism of the networking entity comprising one or more configurations from the set of possible configurations (Gupta – Fig. 2A; Paragraph [0089], note the trained machine learning model 214 may be configured to provide, based on the input data 212, output data 218 which may represent a prediction of the trained machine learning model 214 with respect to the effects that applying the plurality of configurations 220 may have on power consumption and quality of communication at the wireless network; Paragraph [0092], note the plurality of configurations 220 of the one or more network components may be associated with a plurality of power saving mechanisms of the wireless network (illustratively, a plurality of possible strategies for reducing power consumption at the wireless network));
estimating, by the device, an amount of energy savings associated with activating the power saving mechanism of the networking entity (Gupta – Fig. 3A; Paragraph [0134], note the first prediction portion (the first neural network 302a) may be configured to receive input data 314 (which may be configured as the input data 212 described in relation to Fig. 2A), the output data 316 may include score(s) representative of an expected reduction in power consumption (in energy-based metrics, see Paragraph [0090]) provided with the respective mechanism in the given network environment); and
selecting, by the device, a power saving mechanism of the networking entity based on the amount of energy savings estimated by the device (Gupta – Fig. 3A; Paragraph [0135], note the second prediction portion (the second neural network 302b) may be configured to receive as input data the output data 316 of the first prediction portion, the second prediction portion (the second neural network 302b) may be configured to provide (second) output data 318 representative of the operating configuration of network components that provides the greatest communication performance for implementing the power saving mechanism; Paragraph [0138], note determine (e.g., to select) a configuration of one or more network components of a wireless network (e.g., a configuration of the plurality of predefined configurations 220)).
Gupta does not teach wherein the power saving mechanism is a power degraded mode; and causing, by the device, a power degraded mode of the networking entity to be activated based on the amount of energy savings estimated by the device.
In an analogous art, MacKenzie teaches wherein the power saving mechanism is a power degraded mode (MacKenzie – Fig. 2; Col. 4 lines 22-42, note one or more of the base stations identified as potential energy saving base stations enter energy saving mode); and
causing, by the device, a power degraded mode of the networking entity to be activated based on the amount of energy savings estimated by the device (MacKenzie – Fig. 2, Fig. 3; Col. 4 lines 22-42, note the neutral host controller 42 evaluates a weighted score of a base station's suitability to enter energy saving mode (the “energy saving score”) for each base station entering energy saving mode; Col. 5 lines 20-27, note the energy saving solution having the greatest overall score is then selected as the energy saving solution to be implemented (S205)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of MacKenzie into Gupta in order to implement an energy saving mode as a power saving solution/mechanism, reducing energy consumption to meet energy targets (MacKenzie – Col. 4 lines 1-21).
Regarding claim 2, the combination of Gupta and MacKenzie, specifically Gupta teaches wherein the power degraded mode of the networking entity is activated based on a prediction that doing so will not decrease performance of the computer network below an acceptable level (Gupta – Paragraph [0135], note the output data 318 of the second prediction portion may be representative of the operating configuration of network components that provides the greatest communication performance for implementing the power saving mechanism; Paragraph [0147], note training the second prediction portion of the machine learning model by adapting the second weighting factors based on second target data in which a configuration of the one or more network components is associated with a power savings parameter above a predefined threshold).
Regarding claim 3, the combination of Gupta and MacKenzie, specifically Gupta teaches wherein the one or more configurations disable a redundant hardware component of the networking entity (Gupta – Paragraph [0094], note a configuration associated with an activation or deactivation of a carrier aggregation feature, a configuration associated with a turning off of dual connectivity, a configuration associated with a turning off of a massive multiple-input multiple-output feature, a configuration associated with a deactivation or offloading of a machine learning computation associated with a function of a protocol stack).
Regarding claim 4, the combination of Gupta and MacKenzie, specifically Gupta teaches wherein the networking entity comprises a wireless transceiver (Gupta – Paragraph [0036], note a “network component” as used herein may be or include a hardware-based component of a wireless network, such as a cell, a network access node (a base station), a server, transmission medium, an antenna, a transmitter, a receiver, a local oscillator, processing circuitry, a filter, and the like; Paragraph [0057], note wireless communication device 102, transceiver system 144), and
wherein the one or more configurations disable a wireless band of the wireless transceiver (Gupta – Paragraph [0094], note configurations 220, a configuration associated with a decrease of advertised bandwidth, a configuration associated with a variation of the bandwidth for each wireless communication device using a bandwidth part adaptation feature), disable a wireless transmit stream of the wireless transceiver (Gupta – Paragraph [0094], note configurations 220, a configuration associated with a turning off of dual connectivity, a configuration associated with a turning off of a massive multiple-input multiple-output feature), or reduce a transmit power of the wireless transceiver (Gupta – Paragraph [0036], note a “configuration of a network component” may include a power at which an antenna transmits a signal).
Regarding claim 6, Gupta does not teach wherein estimating the amount of energy savings associated with activating the power degraded mode of the networking entity comprises: comparing energy consumptions of entities of a same type as the networking entity and with the power degraded mode activated to energy consumptions of entities of the same type as the networking entity and with the power degraded mode deactivated.
In an analogous art, MacKenzie teaches wherein estimating the amount of energy savings associated with activating the power degraded mode of the networking entity comprises:
comparing energy consumptions of entities of a same type as the networking entity and with the power degraded mode activated to energy consumptions of entities of the same type as the networking entity and with the power degraded mode deactivated (MacKenzie – Col. 4 lines 22-42, note the neutral host controller 42 evaluates all possible variations of candidate energy saving solutions in which the one or more of the base stations identified as potential energy saving base stations (in the first process) enter energy saving mode, and one or more base stations identified for inclusion as part of the energy saving solution each act in energy saving mode, normal (active) mode, or compensation mode).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of MacKenzie into Gupta for the same reason as claim 1 above.
Regarding claim 7, the combination of Gupta and MacKenzie, specifically Gupta teaches wherein comparing the energy consumptions comprises:
computing, for each of the one or more configurations, an amount of energy savings (Gupta – Fig. 3A; Paragraph [0134], note the first prediction portion (the first neural network 302a) may be configured to receive input data 314 (which may be configured as the input data 212 described in relation to Fig. 2A), the output data 316 may include score(s) representative of an expected reduction in power consumption (in energy-based metrics, see Paragraph [0090]) provided with the respective mechanism in the given network environment; Paragraph [0135], note the second prediction portion (the second neural network 302b) may be configured to receive as input data the output data 316 of the first prediction portion, the output data 318 may be configured as the output data 218 described in relation to Fig. 2A, e.g. including a plurality of scores each associated with a configuration of a plurality of configurations of network components, the output data 318 of the second prediction portion may be representative of the operating configuration of network components that provides the greatest communication performance for implementing the power saving mechanism).
Regarding claim 8, Gupta does not teach wherein the networking entity does not have a built-in sleep mode or a built-in low power mode.
In an analogous art, MacKenzie teaches wherein the networking entity does not have a built-in sleep mode or a built-in low power mode (MacKenzie – Fig. 2; Col. 3 lines 50-67, note in S103, the neutral host controller 42 determines whether one or more of the plurality of metrics for each monitored base station satisfy at least one criterion for inclusion in an energy saving solution, the base station does not have any service offerings/commitments (e.g., capability) that prohibit a switch from normal (active) mode to either energy saving mode or compensation mode).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of MacKenzie into Gupta for the same reason as claim 1 above.
Regarding claim 9, the combination of Gupta and MacKenzie, specifically Gupta teaches wherein the data regarding the networking entity is obtained from a controller for the computer network (Gupta – Fig. 2A; Paragraph [0085], note to select the configuration of the one or more network components, the processor 202 (of device 200, which may be a network access node, controller of the wireless network, etc., see Paragraph [0084]) may be configured to provide input data 212 to a trained machine learning model 214; Paragraph [0086], note the input data 212 may include information from one or more entities participating in wireless communications at the wireless network; Paragraph [0087], note the network environment 216 that the input data 212 describes may include: load information, traffic volume, type of traffic, cell configuration, average cell capacity, latency, network access time, throughput, time of day, day and/or month, season of the year, wireless device capabilities, network planning and deployment strategy, and/or combinations thereof).
Regarding claim 11, Gupta teaches an apparatus (Gupta – Fig. 2A; Paragraph [0083], note device for use in a wireless network), comprising:
one or more network interfaces (Gupta – Fig. 1A; Paragraph [0046], note wireless network 100 may communicate with one or more wireless communication devices 102 via one or more network access nodes 104 over a physical interface 106 (e.g., an air interface));
a processor coupled to the one or more network interfaces and configured to execute one or more processes (Gupta – Fig. 2A; Paragraph [0083], note operation of device 200 may be communicatively coupled with the wireless network; Paragraph [0084], note the device 200 may include a processor 202 configured to select a configuration of the operation of one or more network components based on a network environment); and
a memory configured to store a process that is executable by the processor (Gupta – Fig. 2A; Paragraph [0111], note the device 200 may further include a memory 204 storing instructions and/or data for the processor 202, the memory 204 may be communicatively coupled with the processor 202 (e.g., via a wired or wireless connection)), the process when executed configured to:
obtain data regarding a networking entity in a computer network and a set of possible configurations for the networking entity (Gupta – Fig. 2A; Paragraph [0085], note to select the configuration of the one or more network components, the processor 202 (of device 200, which may be a network access node, controller of the wireless network, etc., see Paragraph [0084]) may be configured to provide input data 212 to a trained machine learning model 214; Paragraph [0086], note the input data 212 may include information from one or more entities participating in wireless communications at the wireless network; Paragraph [0087], note the network environment 216 that the input data 212 describes may include: load information, traffic volume, type of traffic, cell configuration, average cell capacity, latency, network access time, throughput, time of day, day and/or month, season of the year, wireless device capabilities, network planning and deployment strategy, and/or combinations thereof);
determine a power saving mechanism of the networking entity comprising one or more configurations from the set of possible configurations (Gupta – Fig. 2A; Paragraph [0089], note the trained machine learning model 214 may be configured to provide, based on the input data 212, output data 218 which may represent a prediction of the trained machine learning model 214 with respect to the effects that applying the plurality of configurations 220 may have on power consumption and quality of communication at the wireless network; Paragraph [0092], note the plurality of configurations 220 of the one or more network components may be associated with a plurality of power saving mechanisms of the wireless network (illustratively, a plurality of possible strategies for reducing power consumption at the wireless network));
estimate an amount of energy savings associated with activating the power saving mechanism of the networking entity (Gupta – Fig. 3A; Paragraph [0134], note the first prediction portion (the first neural network 302a) may be configured to receive input data 314 (which may be configured as the input data 212 described in relation to Fig. 2A), the output data 316 may include score(s) representative of an expected reduction in power consumption (in energy-based metrics, see Paragraph [0090]) provided with the respective mechanism in the given network environment); and
select the power saving mechanism of the networking entity based on the amount of energy savings estimated by the apparatus (Gupta – Fig. 3A; Paragraph [0135], note the second prediction portion (the second neural network 302b) may be configured to receive as input data the output data 316 of the first prediction portion, the second prediction portion (the second neural network 302b) may be configured to provide (second) output data 318 representative of the operating configuration of network components that provides the greatest communication performance for implementing the power saving mechanism; Paragraph [0138], note determine (e.g., to select) a configuration of one or more network components of a wireless network (e.g., a configuration of the plurality of predefined configurations 220)).
Gupta does not teach wherein the power saving mechanism is a power degraded mode; and causing, by the device, a power degraded mode of the networking entity to be activated based on the amount of energy savings estimated by the device.
In an analogous art, MacKenzie teaches wherein the power saving mechanism is a power degraded mode (MacKenzie – Fig. 2; Col. 4 lines 22-42, note one or more of the base stations identified as potential energy saving base stations enter energy saving mode); and
causing, by the device, a power degraded mode of the networking entity to be activated based on the amount of energy savings estimated by the device (MacKenzie – Fig. 2, Fig. 3; Col. 4 lines 22-42, note the neutral host controller 42 evaluates a weighted score of a base station's suitability to enter energy saving mode (the “energy saving score”) for each base station entering energy saving mode; Col. 5 lines 20-27, note the energy saving solution having the greatest overall score is then selected as the energy saving solution to be implemented (S205)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of MacKenzie into Gupta in order to implement an energy saving mode as a power saving solution/mechanism, reducing energy consumption to meet energy targets (MacKenzie – Col. 4 lines 1-21).
Regarding claim 12, the claim is interpreted and rejected for the same reason as claim 2 above.
Regarding claim 13, the claim is interpreted and rejected for the same reason as claim 3 above.
Regarding claim 14, the claim is interpreted and rejected for the same reason as claim 4 above.
Regarding claim 16, the claim is interpreted and rejected for the same reason as claim 6 above.
Regarding claim 17, the claim is interpreted and rejected for the same reason as claim 7 above.
Regarding claim 18, the claim is interpreted and rejected for the same reason as claim 8 above.
Regarding claim 19, the claim is interpreted and rejected for the same reason as claim 9 above.
Regarding claim 20, the claim is interpreted and rejected for the same reason as claim 1 above, except the claim is written in a non-transitory computer-readable medium (CRM) claim format, which is taught by Gupta (Gupta – Fig. 2A; Paragraph [0111], note memory 204 storing instructions and/or data for the processor 202; Paragraph [0237], note the term “memory” as used herein may be understood as a computer-readable medium (e.g., a non-transitory computer-readable medium), in which data or information can be stored for retrieval).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta in view of MacKenzie as applied to claims 1 and 11 above, and further in view of Sumedrea et al. (US 2025/0023779 A1), hereinafter referred to as Sumedrea
Regarding claim 5, the combination of Gupta and MacKenzie does not teach wherein obtaining the data regarding the networking entity in the computer network and the set of possible configurations for the networking entity comprises: querying a large language model trained on documentation associated with the networking entity.
In an analogous art, Sumedrea teaches wherein obtaining the data regarding the networking entity in the computer network and the set of possible configurations for the networking entity comprises:
querying a large language model trained on documentation associated with the networking entity (Sumedrea – Paragraph [0017], note an asset and cloud configuration management (ACCM) system acquires (e.g., retrieves, receives) a set of parameters (e.g., rules, configurations, and/or policies), associated with one or more network entities of a computing network, the ACCM system provides the set of parameters to a configuration management model trained to generate, based on semantic matching, recommended configurations (e.g., rules, policies, settings, and/or the like) for network entities and validated configurations for the network entities; Paragraph [0021], note the ACCM agent trains the LLM to generate, based on semantic matching, recommended configurations for network entities and validated configurations for the network entities).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Sumedrea into the combination of Gupta and MacKenzie in order to recommend and validate network configurations using an LLM, reducing errors caused by misconfiguration (Sumedrea – Paragraph [0016]).
Regarding claim 15, the claim is interpreted and rejected for the same reason as claim 5 above.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta in view of MacKenzie as applied to claim 1 above, and further in view of Decamp et al. (US 2019/0025355 A1) cited by the applicant, hereinafter referred to as Decamp.
Regarding claim 10, the combination of Gupta and MacKenzie does not teach wherein causing the power degraded mode of the networking entity to be activated based on the amount of energy savings estimated by the device comprises: providing an indication of the power degraded mode to a user interface.
In an analogous art, Decamp teaches wherein causing the power degraded mode of the networking entity to be activated based on the amount of energy savings estimated by the device comprises:
providing an indication of the power degraded mode to a user interface (Decamp – Fig. 8; Paragraph [0050], note the remote server receives power meter readings over a period of time, the remote server can instruct the microcontroller 120 to shut down one or more of the power outlets connected to the relay switches; Paragraph [0068], note user interface 800 having a dashboard for monitoring and controlling the system of Fig. 7, the dashboard 800 aggregates and displays the power information and other electrical data, and/or metadata collected from the various sensors in the system).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Decamp into the combination of Gupta and MacKenzie in order to reduce standby loads from idle devices (Decamp – Paragraph [0050]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ma et al. (US 10,609,711 B1) discloses an access point monitoring its power consumption, and offloading to a second access point if the power consumption is greater than a threshold.
Abotabl et al. (US 2023/0284135 A1) discloses a base station/network node configured with multiple energy saving modes based on UE operation.
Guzman et al. (US 2025/0068438 A1) discloses training a large language model on a device configuration database.
Barber et al. (US 2025/0323835 A1) discloses training LLM on network configuration data.
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/BAILOR C HSU/Primary Examiner, Art Unit 2461