Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The Office Action is in response to claims filed on 12/16/2025 where claims 1-20 are pending and ready for Examination.
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 Examiner has reviewed the Applicant’s arguments submitted on 12/16/2025 in their entirety. Applicant’s argument are moot based on a new prior art rejection comprising Bellamkonda (US 20220232399) which provides one of ordinary skill in the art to contemplate the newly amended claims (see e.g. Bella ([015], [0055], [0057], [0073], [0075])
The Examiner notes in the previous Office Action the Examiner took Official Notice regarding the subject matter of claim 7. The subject matter is now considered Applicant Admitted Prior Art as the Applicant has not traversed the Official Notice (MPEP 2144.03)
The Examiner clarifies that claim 13 is an independent claims and the prior characterization of claims 13-20 as dependent claims is corrected accordingly.
Claims 1-2, 4-8, 10-14 and 16-20 are rejected under 35 USC 103 as being unpatentable over 3GPP TR 28.908, December 22 in view of Styles (US 20230373337) and in further view of Bellamkonda (US 20220232399)
Regarding claim 1, 3GPP discloses an apparatus for use by a communication network element or communication network function acting as an artificial intelligence, Al, machine learning, ML, management service consumer, the apparatus comprising
at least one processing circuitry (3GPP; 3GPP discloses a network computing environment which requires processing circuitry (e.g. processor, ASIC, etc.) to be inherently present), and
at least one memory for storing instructions that, when executed by the at least one processor, cause the apparatus at least (3GPP; 3GPP discloses a network computing environment which requires memory storing instructions to be inherently present)
to request an AI/ML energy consumption parameter from an AI/ML management service producer offering services related to at least one Al/ML entity (3GPP;
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see e.g. Section 5.8.2.1 comprising Figure 5.8.2.1-1 which shows that the consumer may wish to obtain AI/ML capabilities to determine how to use them (for the consumer’s needs, e.g., for its intent targets or other automation targets;
see e.g. Section 5.8.3 “... capabilities of an ML entity as a decision described as a triplet <object(S), parameters, metrics> with the entries respectively indicating the object types for which the ML entity an undertake optimization or control ...”
The Examiner note capability is equivalent to energy consumption related parameter)
to receive, from the Al/ML management service producer, the requested Al/ML energy consumption parameter (3GPP;
see e.g. Section 5.8.2.1 comprising Figure 5.8.2.1-1 which shows that the consumer may wish to obtain AI/ML capabilities to determine how to use them (for the consumer’s needs, e.g., for its intent targets or other automation targets;
Section 5.8.2.1 teaches the consumer use the capability information to make decisions aligned with their intent or automation targets which is the processing or usage of the energy consumption parameter
see e.g. Section 5.8.3
The triplet structure <object(s), parameters, metrics> in Section 5.8.3 directly maps to object types, parameters (i.e. energy consuption related configuration parameters, as explicitly supported earlier in Section 5.8.2.1 and Figure 5.8.2.1-1. Section 5.8.3 requires that the producer deliver such information as part of the triplet of objects, parameters, and metrics. Because the consumer obtains this report “to determine how to use” the capability (Section 5.8.2.1), the act of determining the requested energy-consumption parameter for at least one mL entity is inherently present in the disclosed workflow) , and
to process the Al/ML energy consumption parameter for deriving an energy saving strategy considering the Al/ML energy consumption related parameter (3GPP;
Sections 5.8.2.1, 5.8.3, 5.8.4, 5.8.5
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The Examiner notes the determination is within the context of energy consumption
See e.g. Section 5.10 AI/ML Configuration Management: 5.10.1 Description
“... use AI to formulate energy saving solutions ...”
See e.g. 5.10.2.5 “... per the performance of the energy efficiency result by execution of the inference output ...”
Under the Broadest Reasonable Interpretation, the claimed invention is anticipated. The cited figures and sections disclose that the consumer evaluates AI/ML capability data comprised of configuration and performance metrics (e.g., Z in the triplet), which inherently include parameters that may relate to energy consumption. These metrics are used by the ML entity to “optimize through its actions” which is functionally equivalent to processing the energy parameter to derive an energy saving strategy.
Furthermore, Section 5.8.5 confirms that the AI/ML capability data (triplet X,Y,Z) is returned to the consumer either via discovery MNS or the direct reading from the MNI, thereby supporting the consumer’s processing behavior. The strategy derivation in inherently present, as the entire framework is designed to inform the consumer’s decisions and optimization behavior, consisted with the claimed function.)
3GGP strongly suggests but does not expressly disclose:
wherein the energy consumption parameter comprises an AI/ML energy consumption model
when processing the AI/ML energy consumption parameter for deriving the energy saving strategy considering the AI/ML energy consumption parameter, to consider information related to at least one of network performance or user targets.
However in analogous art Styles discloses:
wherein the energy consumption parameter comprises an AI/ML energy consumption model (Styles;
see e.g. [0010]
[0010] In some examples, predicting the energy consumption profile further comprises implementing machine learning to dynamically update the predicted energy consumption profile.,
[0020] In some examples, predicting the energy consumption profile further comprises the processor being operable to implement machine learning to dynamically update the predicted energy consumption profile.
[0062] In some examples, a plurality of input parameters can be fed into a processor (such as processor 304 as described with reference to FIG. 3) as part of a machine learning algorithm to develop a model depicting the charging requirements of the itinerary based on the input parameters. Accordingly, the processor 304 can dynamically update the predicted energy consumption profile based on the number and type of input parameters. The input parameters of the machine learning algorithm can include a plurality of attributes associated with the itinerary (or the specific task(s) within the itinerary). For example, the attributes may comprise (but are not limited to): the vehicle's make and model, a driver's driving habits, a vehicle's previous use patterns for a specific itinerary (for example, previous distances traveled for previous itineraries), a vehicle's previous accessory pattern for a specific itinerary, a number of required vehicle occupants for an itinerary or a specific task, physical characteristics of at least one required vehicle occupant, and/or any other suitable contextual factors.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Styles parameter and profile associated with machine learning schemes. The motivation being the combined solution provides for implementing a known technique resulting in increased efficiencies of managing system performance.
3GPP in view of Styles does not expressly disclose:
when processing the AI/ML energy consumption parameter for deriving the energy saving strategy considering the AI/ML energy consumption parameter, to consider information related to at least one of network performance or user targets.
Bellamkonda discloses:
when processing the AI/ML energy consumption parameter for deriving the energy saving strategy considering the AI/ML energy consumption parameter, to consider information related to at least one of network performance or user targets (Bellamkonda;
Bellamkonda expressly discloses applying AI/ML techniques to optimize parameter settings where the parameters include energy-consumption related parameters and are evaluated together with other metric/attributes that affect system operation. For example
Bellamkonda also expressly contemplates network performance metrics such as QoS and KPIs a spart of the parameter set being evaluated by the AI/ML process. These QoS/KPI-type metrics are the very signals used to assess system/network performance , and when Bellamkonda’s AI/ML framework determines parameter importance and performs optimization/actions selection using those metrics together with energy-consumption -related parameters, the system is considering network performance within the same processing loop. Bellamkonda discloses use of AI/ML techniques, determining an importance of parameters, which shows the AI/ML process is not passively listing values but actively using them in decision -making . Bellamkonda discloses energy consumption related parameters, thereby supplying the claimed AI/ML energy consumption parameter context. Bellamkonda disclose using such information in selecting or determining actions/parameter settings and optimizing based on those parameters/metrics. Thus Bellamkonda teaches an AI/ML process that actively uses energy – consumption related parameters together with other operational information to driver optimization decision, which reads on processing the AI/ML energy consumption parameter for deriving an energy saving strategy while considering information related to at least one of network performance or user targets.
[0015] As described herein, one or more scores, metrics, etc. (referred to herein simply as “scores” for the sake of brevity) may be determined (e.g., using AI/ML techniques and/or other suitable techniques) based on service coverage (e.g., range or area of wireless service), service quality (e.g., Signal-to-Interference-and-Noise-Ratio (“SINR”), Channel Quality Indicator (“CQI”), latency, throughput, etc.), energy consumption metrics (e.g., measure of energy consumed over time), mobility metrics (e.g., quantity or proportion of UEs involved in a handover process), and/or other suitable metrics or information associated with base stations or other equipment associated with the RANs. In some embodiments, the scores may reflect an overall optimization score, which may reflect a holistic measure of how well a given sector is optimized.
[0055] As further shown, sector model 203 may be associated with one or more configuration frameworks 201. For example, GOS 105 may use AI/ML techniques or other suitable techniques to determine that particular KPIs, attributes, etc. are more important for sectors 101 with particular attributes than for sectors with different attributes. As an example, GOS 105 may determine that sectors 101 having a first set of attributes should have mobility-related parameters prioritized (e.g., KPI category 403-2) and that energy consumption-related parameters (e.g., KPI category 403-3) are less of a priority, and may determine that sectors 101 having a second set of attributes should have coverage/quality-related parameters prioritized (e.g., KPI category 403-1). In this example, GOS 105 may determine that the first sector 101 is associated with a first configuration framework 201 and that the second sector 101 is associated with a second configuration framework 201.
[0057] GOS 105 may further generate, maintain, refine, etc. (e.g., using one or more AI/ML techniques or other suitable techniques) one or more associations between respective configuration frameworks 201 and one or more sets of actions/parameters 205. For example, each configuration framework 201 may be associated with one or more sets of actions/parameters 205, as each particular set of actions/parameters 205 may have been determined (e.g., based on real-world results and/or simulated results) as increasing an overall optimization score of one or more sectors 101 that match sector model 203, where such overall optimization score is computed based on configuration framework 201. As noted above, actions/parameters 205 may include modifying QoS parameters, modifying beamforming and/or other antenna parameters, modifying energy consumption parameters, modifying handover parameters, or other suitable actions.
[0073] Process 900 may also include determining (at 908) a particular sector model 203 based on the received metrics, KPIs, attributes, etc. For example, as discussed above, GOS 105 may use one or more AI/ML techniques to determine an association, correlation, or the like between the received metrics, KPIs, attributes, etc. and metrics, KPIs, attributes, etc. associated with sector model 203. For example, GOS 105 may select a particular sector model 203 from a set of candidate sector models 203, and/or may generate a new sector model 203 based on the received metrics, KPIs, attributes, etc.
[0075] Process 900 may additionally include identifying (at 912) a set of actions 205 to perform based on the selected particular configuration framework 201. For example, as discussed above, GOS 105 may select actions/parameters 205 based on respective framework-action affinity scores 513 between configuration framework 201 and a set of candidate actions/parameters 205, and/or may otherwise select or generate actions/parameters 205 based on sector model 203. As noted above, actions/parameters 205 may include QoS actions or parameters, antenna actions or parameters, energy consumption actions or parameters, and/or other suitable actions and/or parameters.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bellamkonda with 3GPP,k the motivation being the combined solution provides for implementing a known technique resulting in increased efficiency and improve optimization of AI/ML based decision making by incorporating energy consumption-related parameters together with network performance information within a unified optimization framework.
Regarding claim 2, 3GPP in view of Styles and in further view of Bellamkonda discloses the apparatus according to claim 1, wherein the Al/ML energy consumption parameter further comprises:
an AI/ML energy consumption metric (3GPP; see e.g. Section 5.8.1 “... the performance metrics which the ML entity optimizes through its actions ...”) ,
wherein Al/ML energy consumption parameter is related to at least one phase in a lifecycle of an Al/ML entity including a data collection phase, a model training phase, a testing phase and an inference phase (3GPP; see e.g. Section 5.9 AI/ML updated management
“The ML entity needs to be update timely to ensure of the performance of inference and analysis”
The Examiner notes ML inherently provides for a data collection phase, a model training phase, a testing phase, and an inference phase
See e.g. Section 5.9.2.1; See e.g. Section 5.10.2.3; See e.g. Section 5.16.2.1).
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Styles parameter and profile associated with machine learning schemes. The motivation being the combined solution provides for implementing a known technique resulting in increased efficiencies of managing system performance.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bellamkonda with 3GPP, the motivation being the combined solution provides for implementing a known technique resulting in increased efficiency and improve optimization of AI/ML based decision making by incorporating energy consumption-related parameters together with network performance information within a unified optimization framework.
Regarding claim 4, 3GPP in view of Styles and in further view of Bellamkonda discloses The apparatus according to claim 2, wherein the Al/ML energy consumption metric is defined per Al/ML capability including a definition of an object or object types for optimization or control, configuration parameters on an object or object types, and network metrics being optimized, and per Al/ML scope indicating a validity range (3GPP;
See e.g. Section 5.8.1 “... the object or object types for which the ML entity can undertake optimization or control”)
See e.g. Section 5.7.2.2 “ ... validity scope ...”
See e.g. Section 5.12.2.1
The examiner notes a validity range is inherently present based on the scope
Regarding claim 5, 3GPP in view of Styles and in further view of Bellamkonda discloses the apparatus according to claim 2, wherein the Al/ML energy consumption metric is defined per Al/ML entity (3GPP;
See e.g. Section 5.17.2.1 “... quantitative metric ... ML entity performance by evaluating the effect on ML entity ...”)
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Styles parameter and profile associated with machine learning schemes. The motivation being the combined solution provides for implementing a known technique resulting in increased efficiencies of managing system performance.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bellamkonda with 3GPP, the motivation being the combined solution provides for implementing a known technique resulting in increased efficiency and improve optimization of AI/ML based decision making by incorporating energy consumption-related parameters together with network performance information within a unified optimization framework.
Regarding claim 6, 3GPP in view of Styles and in further view of Bellamkonda discloses the apparatus according to claim 2, wherein the Al/ML energy consumption metric is defined per managed element or managed entity each comprising one or more Al/ML entities (3GPP; The metrics may be based on a managed environment (3GPP;
See e.g. Section 5.4.2.2 “ ... operator may wish to control and manage ...ML entity ...”);
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Styles parameter and profile associated with machine learning schemes. The motivation being the combined solution provides for implementing a known technique resulting in increased efficiencies of managing system performance.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bellamkonda with 3GPP, the motivation being the combined solution provides for implementing a known technique resulting in increased efficiency and improve optimization of AI/ML based decision making by incorporating energy consumption-related parameters together with network performance information within a unified optimization framework.
Regarding claim 7, 3GPP in view of Styles and in further view of Bellamkonda disclose the apparatus according to claim 2, Styles does not expressly disclose wherein the AI/ML energy consumption metric is defined separately for AI/ML related signaling concerning at least one of transmission of data to be processed by an AI/ML model, or transmission of data comprising an AI/ML model.
The above feature is considered to be Applicant Admitted Prior Art and it would have been obvious to one of ordinary skill in the art to implement this feature. The motivation being the combined solution provides for increased efficiencies in energy consumption optimization..
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bellamkonda with 3GPP,the motivation being the combined solution provides for implementing a known technique resulting in increased efficiency and improve optimization of AI/ML based decision making by incorporating energy consumption-related parameters together with network performance information within a unified optimization framework.
.
Regarding claim 8., 3GPP in view of Styles and in further view of Bellamkonda discloses The apparatus according to claim 2, wherein the Al/ML energy consumption metric is defined according to a requested energy consumption requirement provided from the consumer to the producer (3GPP; Per independent claim 1).
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Styles parameter and profile associated with machine learning schemes. The motivation being the combined solution provides for implementing a known technique resulting in increased efficiencies of managing system performance.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bellamkonda with 3GPP, the motivation being the combined solution provides for implementing a known technique resulting in increased efficiency and improve optimization of AI/ML based decision making by incorporating energy consumption-related parameters together with network performance information within a unified optimization framework.
Regarding claim 10, 3GPP in view of Styles and in further view of Bellamkonda disclose The apparatus according to claim 1, to decide on at least one of a recommendation regarding activation/deactivation of the AI/ML entity a recommendation regarding switching over towards another AI/ML based solution based on performance or energy consumption, a recommendation regarding switching over towards a less energy demanding solution being different to an AI/ML based solution, a recommendation is derived regarding switching over towards a more energy demanding solution in order to improve performance of AI/ML solution, and a recommendation regarding a scheduling of a re-training of AI/ML entity (Because the combined system apples A”/ML techniques to evaluate performance and energy-related metrics (Bellamkonda) and operates within an AI/ML framework that includes training-related functionality and signaling (3GPPP), the control of the AI/ML entity necessarily includes decision regarding its training and updating. Accordingly, when the system determines actions for the AI/ML entity based on those evaluated conditions, it would have been obvious for those actions to include a recommendation regarding scheduling of re-training of the AI/ML entity as part of managing and improving the model based on the same evaluated performance and energy conditions as training is part of machine learning. Furthermore execution of training by a processor necessarily occurs under instruction controlled timing/order)
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bellamkonda with 3GPP, the motivation being the combined solution provides for implementing a known technique resulting in increased efficiency and improve optimization of AI/ML based decision making by incorporating energy consumption-related parameters together with network performance information within a unified optimization framework.
Regarding claim 11, 3GPP in view of Styles and in further view of Bellamkonda disclose The apparatus according to claim 10,
wherein the instructions further cause the apparatus when processing the AI/ML energy consumption parameter for deriving the energy saving strategy considering the AI/ML energy consumption parameter, to consider information related to network performance in a tradeoff between an improvement in network performance and an energy consumption when applying the AI/ML entity, or to consider information related to a performance of the AI/ML entity in a tradeoff between AI/ML entity performance improvement and an energy consumption when improving the AI/ML entity (The combined solution because 3GPP already discloses AI/ML processing using parameters and performance metrics to optimize outcomes and Bellamkonda discloses evaluating energy consumption related parameters together with QoS/KPI metrics to select andn prioritize actions based on an optimization score. Thus the combined system necessarily performs a tradeoff between network performance (e.g. QoS/KPIs) and energy consumption when determining actions for the AI/ML entity, which reads on considering information related to network performance in a tradeoff with energy consumption when applying or improving the AI/ML entity)
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bellamkonda with 3GPP, the motivation being the combined solution provides for implementing a known technique resulting in increased efficiency and improve optimization of AI/ML based decision making by incorporating energy consumption-related parameters together with network performance information within a unified optimization framework.
Regarding claim 12, 3GPP in view of Styles and in further view of Bellamkonda discloses The apparatus according to claim 1, wherein the instructions further cause the apparatus to send, to the AI/ML management service producer, at least one of a result of the processing the AI/ML energy consumption parameter for deriving the energy saving strategy or configuration data for configuring the energy saving strategy at the AI/ML management service producer (The combined solution because 3GPP already discloses exchanging AI/ML capability data and results between entities, and Bellamkonda discloses generating results from processing energy consumption parameters for optimization, such that transmitting those results to a management entity to utilize results for configuring and optimizing the system)
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bellamkonda with 3GPP, the motivation being the combined solution provides for implementing a known technique resulting in increased efficiency and improve optimization of AI/ML based decision making by incorporating energy consumption-related parameters together with network performance information within a unified optimization framework.
Regarding claim 13, claim 13 comprises the same and/or similar subject matter as claim 1 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 14, claim 14 comprises the same and/or similar subject matter as claim 2 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 16, claim 16 comprises the same and/or similar subject matter as claim 4 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 17, claim 17 comprises the same and/or similar subject matter as claim 5 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 18, claim 18 comprises the same and/or similar subject matter as claim 6 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 19, claim 19 comprises the same and/or similar subject matter as claim 7 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 20, claim 20 comprises the same and/or similar subject matter as claim 8 and is considered an obvious variation; therefore it is rejected under the same rationale.
Claims 3 and 15 are rejected under 35 USC 103 as being unpatentable over 3GPP in view of Styles and in further view of Bellamkonda and in further view of Bellamkonda and in further view of Thotan (US 2014/0324240)
Regarding claim 3, 3GPP in view of Styles and in further view of Bellamkonda discloses the apparatus according to claim 2, 3GPP does not expressly disclose wherein the Al/ML energy consumption metric is expressed in a form of numerator / denominator, wherein the numerator indicates one of
a complexity related value indicating a computational complexity of an Al/ML model, an energy consumption related value indicating an energy consumption of an Al/ML model under predefined operation conditions, or an environmental related value indicating a carbon emission when operating an Al/ML model, and
the denominator indicates one of or a combination of per sample, per entire training process, per Al/ML related performance gain, per network KPI gain, per amount of saved energy, or per inference.
However in analogous art Thotan discloses:
wherein the Al/ML energy consumption metric is expressed in a form of numerator / denominator, wherein the numerator indicates one of
a complexity related value indicating a computational complexity of an Al/ML model,
the denominator indicates one of or a combination of per sample (Thotan;
see e.g. [0121] “ ... HMM ... computational complexity per data sample)
Therefore it would have been prima facie obvious before the effective filing date of the claimed invention to incorporate Thotan’s metric. The motivation being the combined solution provides for implanting a known technique providing for optimization of resources within the ML environment.
Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Styles parameter and profile associated with machine learning schemes. The motivation being the combined solution provides for implementing a known technique resulting in increased efficiencies of managing system performance.
Regarding claim 15, claim 15 comprises the same and/or similar subject matter as claim 3 and is considered an obvious variation; therefore it is rejected under the same rationale.
Claim 9 is rejected under 35 USC 103 as being unpatentable over 3GPP in view of Styles and in further view of Bellamkonda and in further view of Khalil et al. “Energy Efficiency in 5G Communications – Conventional to Machine Learning Approaches”, April 2020
Regarding claim 9, 3GPP in view of Styles and in further view of Bellamkonda disclose The apparatus according to claim 2, 3GPP does not expressly disclose wherein the AI/ML energy consumption profile indicates an expected energy consumption during at least one phase of the lifecycle of an AI/ML entity and includes at least one of an indication of a complexity of the AI/ML entity, an indication of energy consumption of the AI/ML entity when operated under predefined operation conditions, an indication of an alternative energy saving solution based on an AI/ML model, an amount of data required for inference, an amount of data required for model training purposes, an indication of a required periodicity for re-training of the AI/ML entity, an indication of a requirement for data signaling regarding model training purposes.
However in analogous art Khalil discloses:
an indication of energy consumption of the AI/ML entity when operated under predefined operation conditions (Khalil; Khalil monitors energy efficiency within a predefine environment;
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Therefore it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Khalils’s scheme. The motivation being implanting a known technique provides for increased efficiencies in networking transmissions.
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 TODD L. BARKER whose telephone number is (571) 270 0257. The Examiner can normally be reached on Monday through Friday, 7:30am to 5:00pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor Vivek Srivastava can be reached on (571) 272 7304.
/TODD L BARKER/Primary Examiner, Art Unit 2449