DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to communication filed on 07/29/2024. Claims 1-30 are pending for examination.
Examiner’s Note
2. 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 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.
Claim Rejections - 35 USC § 103
3. 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
4. Claim 1, 2, 12, 16, 18, 20, 21, 25, 29 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Mu (US 2025/0038918 A1) in view of O’Shea (US 2023/0284048 A1).
Regarding claim 1 and 29, Mu teaches a method for wireless communication at a user equipment (UE), comprising:
receiving signaling (information) identifying a configuration of a plurality of machine learning (artificial intelligence, AI) models for channel characteristic prediction(estimation) ( [0161], Fig. 8, Step S81-- configuration information for indicating number of AI models corresponding to DMRS pattern is sent{by base station—[0160]}; where the number of the AI models corresponding to the DMRS pattern is for instructing the UE to determine the AI model to perform channel estimation according to the number of AI models corresponding to the DMRS pattern;)(Hence the UE receives information identifying configuration of AI models for channel characteristic estimation/prediction.), wherein the channel characteristic prediction(estimation) for each machine learning (artificial intelligence, AI) model of the plurality of machine learning(artificial intelligence, AI) models is based at least in part on a respective(corresponding—[0003]) reference signal(DMRS) resource of a plurality of reference signal(DMRS) resources ([0161], configuration information for indicating number of AI models corresponding to DMRS pattern is sent{by base station—[0160]}; where the number of the AI models corresponding to the DMRS pattern is for instructing the UE to determine the AI model to perform channel estimation{see[0006]} according to the number of AI models corresponding to the DMRS pattern/resource{see [0056]};)(Hence channel characteristic estimation for each AI model based on respective DMRS resource of DMRS resources.);
Mu teaches artificial intelligence, AI as Machine learning, ML.
Mu does not teach obtaining an input to one or more machine learning models of the plurality of machine learning models; and processing the input using at least one machine learning model of the plurality of machine learning models to obtain the channel characteristic prediction of the at least one machine learning model.
However, in an analogous art, O’Shea teaches
obtaining an input (104—Fig. 1) to one or more machine learning(ML) models of the plurality of machine learning(ML) models (Fig. 1, 10; [0055], Input signal data 104 from RAN 130 is obtained by MLSS 102 and processed using one or more machine learning (ML) networks, to obtain metadata 114 characterizing the input signal data 104. ) (Hence obtaining an input to a ML model.); and
processing the input using at least one machine learning(ML) model of the plurality of machine learning(ML) models to obtain the channel characteristic prediction of the at least one machine learning(ML) model (Fig. 1, 10; [0055], Input signal data 104 from RAN 130 is obtained by MLSS 102 and processed using one or more machine learning (ML) networks, to obtain metadata 114; wherein [0061] & [0118], the metadata includes one or more other properties of the signal (for example, channel statistics/prediction such as signal strength {RSRP/RSSI/SINR—see [0105]}).)(Hence processing input using the ML model to obtain channel characteristic prediction of the ML model) .
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of O’Shea and apply them on the teaching of Mu to provide method enables controlling the operation of the RAN based on the metadata, so that the RF signal transmitted using the machine learning network is analyzed to obtain the information about a network environment in an efficient manner (O’Shea; [0039]).
Regarding claim 2, Mu further teaches further comprising:
receiving signaling indicating the at least one machine learning (artificial intelligence, AI) model ([0071], UE receives AI model indication information configured by base station to accurately indicate one AI model to be used by the UE when the number of the AI models corresponding to the DMRS pattern is a plurality. ); and
selecting the at least one machine learning (artificial intelligence, AI) model based at least in part on the signaling ( [0071], UE receives AI model indication information configured by base station to accurately indicate one AI model to be used by the UE when the number of the AI models corresponding to the DMRS pattern is a plurality. UE is enabled to determine {select—[0073]} a suitable AI model for channel estimation. ).
Regarding claims 12 and 25, Mu does not teach wherein the input for each machine learning model of the one or more machine learning models comprises a time series of reference signal receive power vectors associated with the respective reference signal resource of each machine learning model, a bitmap indicating one or more indices of one or more respective strongest reference signal resources based at least in part on a reference signal receive power vector of the time series of reference signal receive power vectors, or any combination thereof.
However, in an analogous art, O’Shea teaches wherein the input for each machine learning model of the one or more machine learning models comprises a time series of reference signal receive power(RSRP) vectors associated with the respective reference signal(RS) resource of each machine learning model(Fig. 1, 10; [0055], Input signal data 104 from RAN 130 is obtained by MLSS 102 and processed using one or more machine learning (ML) networks, to obtain metadata 114; wherein [0105], In some cases the inputs to these models is embeddings of the MLSS metadata. The metadata includes Reference Signal Received Power (RSRP){i.e. that is associated with RS },), a bitmap indicating one or more indices of one or more respective strongest reference signal resources based at least in part on a reference signal receive power vector of the time series of reference signal receive power vectors, or any combination thereof.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of O’Shea and apply them on the teaching of Mu to provide method enables controlling the operation of the RAN based on the metadata, so that the RF signal transmitted using the machine learning network is analyzed to obtain the information about a network environment in an efficient manner (O’Shea; [0039]).
Regarding claims 16, Mu does not teach wherein the at least one machine learning model predicts one or more channel characteristics of the respective reference signal resource, an angle of departure for downlink precoding associated with the respective reference signal resource, a linear combination of one or more measurements associated with the respective reference signal resource, or any combination thereof.
However, in an analogous art, O’Shea teaches wherein the at least one machine learning model predicts one or more channel characteristics (RSRP) of the respective reference signal(RS) resource (Fig. 1, 10; [0055], Input signal data 104 from RAN 130 is obtained by MLSS 102 and processed using one or more machine learning (ML) networks, to obtain metadata 114; wherein [0061] & [0118], the metadata includes one or more other properties of the signal (for example, channel statistics/prediction such as signal strength {RSRP/RSSI/SINR—see [0105]}), an angle of departure for downlink precoding associated with the respective reference signal resource, a linear combination of one or more measurements associated with the respective reference signal resource, or any combination thereof.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of O’Shea and apply them on the teaching of Mu to provide method enables controlling the operation of the RAN based on the metadata, so that the RF signal transmitted using the machine learning network is analyzed to obtain the information about a network environment in an efficient manner (O’Shea; [0039]).
Regarding claim 18, Mu does not teach wherein the channel characteristic prediction comprises a reference signal receive power prediction, a signal-to- interference-plus-noise ratio prediction, a rank indicator prediction, a precoding matrix indicator prediction, a layer indicator prediction, a channel quality indicator prediction, or a combination thereof.
However, in an analogous art, O’Shea teaches wherein the channel characteristic prediction comprises a reference signal receive power prediction, a signal-to- interference-plus-noise ratio prediction, a rank indicator prediction, a precoding matrix indicator prediction, a layer indicator prediction, a channel quality indicator prediction, or a combination thereof ( [0061] & [0118], the metadata includes one or more other properties of the signal (for example, channel statistics/prediction such as signal strength {RSRP/RSSI/SINR/CQI—see [0105]}).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of O’Shea and apply them on the teaching of Mu to provide method enables controlling the operation of the RAN based on the metadata, so that the RF signal transmitted using the machine learning network is analyzed to obtain the information about a network environment in an efficient manner (O’Shea; [0039]).
Regarding claim 20 and 30, Mu teaches a method for wireless communication at a network entity, comprising:
transmitting signaling(information) identifying a configuration of a plurality of machine learning(artificial intelligence, AI) models for channel characteristic prediction(estimation) ( [0161], Fig. 8, Step S81-- configuration information for indicating number of AI models corresponding to DMRS pattern is sent{by base station—[0160]}; where the number of the AI models corresponding to the DMRS pattern is for instructing the UE to determine the AI model to perform channel estimation according to the number of AI models corresponding to the DMRS pattern;)(Hence the base station transmits information identifying configuration of AI models for channel characteristic estimation/prediction.), wherein the channel characteristic prediction(estimation) for each machine learning(artificial intelligence, AI) model of the plurality of machine learning(artificial intelligence, AI) models is based at least in part on a reference signal(DMRS) resource of a plurality of reference signal(DMRS) resources ([0161], configuration information for indicating number of AI models corresponding to DMRS pattern is sent{by base station—[0160]}; where the number of the AI models corresponding to the DMRS pattern is for instructing the UE to determine the AI model to perform channel estimation{see[0006]} according to the number of AI models corresponding to the DMRS pattern/resource{see [0056]};)(Hence channel characteristic estimation for each AI model based on respective DMRS resource of DMRS resources.);
Mu teaches artificial intelligence, AI as Machine learning, ML.
Mu does not teach obtaining an input to the plurality of machine learning models based at least in part on performing one or more measurements associated with the plurality of reference signal resources; and outputting the input comprising the one or more measurements.
However, in an analogous art, O’Shea teaches obtaining an input(104-Fig. 1) to the plurality of machine learning(ML) models based at least in part on performing one or more measurements associated with the plurality of reference signal resources (Fig. 1, 10; [0055], Input signal data 104 from RAN 130 is obtained by MLSS 102 and processed using one or more machine learning (ML) networks, to obtain metadata 114 characterizing the input signal data 104. Wherein [0105], In some cases, the inputs to these models may be embeddings of the MLSS metadata. The metadata can includes combined KPI data {RSRP, RSRQ—[0117]—i.e. measurements associated with RSs.}) (Hence obtaining input to ML models based on performing measurements of the RSs.); and
outputting the input ([0055], Fig. 1- Input signal data 104 from the RAN 130 to MLSS 102.) comprising the one or more measurements ([0105], In some cases, the inputs to these models may be embeddings of the MLSS metadata. The metadata can includes combined KPI data {RSRP, RSRQ—[0117]—i.e. measurements associated with RSs.})(Hence the 130 outputs the input comprising the RS measurements.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of O’Shea and apply them on the teaching of Mu to provide method enables controlling the operation of the RAN based on the metadata, so that the RF signal transmitted using the machine learning network is analyzed to obtain the information about a network environment in an efficient manner (O’Shea; [0039]).
Regarding claim 21, Mu does not teach further comprising: outputting an indication of one or more machine learning models of the plurality of machine learning models for processing the input.
However, in an analogous art, O’Shea teaches further comprising: outputting an indication of one or more machine learning models of the plurality of machine learning models for processing the input ([0055]; [0059]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of O’Shea and apply them on the teaching of Mu to provide method enables controlling the operation of the RAN based on the metadata, so that the RF signal transmitted using the machine learning network is analyzed to obtain the information about a network environment in an efficient manner (O’Shea; [0039]).
5. Claim 3-7, 11, 14, 24 and 27 is rejected under 35 U.S.C. 103 as being unpatentable over Mu (US 2025/0038918 A1) in view of O’Shea (US 2023/0284048 A1), further in view of Kovacs (US 2023/0198640 A1).
Regarding claim 3, Mu- O’Shea does not teach further comprising: selecting the at least one machine learning model based at least in part on the channel characteristic prediction of the at least one machine learning model having a likelihood of being used to determine a reference signal resource measurement cycle above a threshold.
However, in an analogous art, Kovacs teaches further comprising:
selecting the at least one machine learning model based at least in part on the channel characteristic (CSI) prediction of the at least one machine learning model having a likelihood of being used to determine a reference signal resource measurement (RSRP) cycle above a threshold (high) ([0050]; [0051]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Kovacs and apply them on the teaching of Mu- O’Shea to provide artificial intelligence (AI)/machine learning (ML) techniques is used to assist or improve various aspects of network operation or network performance(Kovacs; [0029]).
Regarding claim 4, Mu- O’Shea does not teach further comprising: determining the likelihood of being used to determine the reference signal resource measurement cycle for each machine learning model of the plurality of machine learning models based at least in part on applying a separate machine learning model.
However, in an analogous art, Kovacs teaches further comprising:
determining the likelihood of being used to determine the reference signal resource measurement (RSRP) cycle for each machine learning (ML) model (214—Fig. 3B) of the plurality of machine learning (ML) models based at least in part on applying a separate machine learning (ML) model ([0059]; Fig. 3B, 316- a retraining of ML model 212 to be performed; then 210 and 214.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Kovacs and apply them on the teaching of Mu- O’Shea to provide artificial intelligence (AI)/machine learning (ML) techniques is used to assist or improve various aspects of network operation or network performance(Kovacs; [0029]).
Regarding claim 5, Mu- O’Shea does not teach wherein the threshold is a probability value or a binary output.
However, in an analogous art, Kovacs teaches wherein the threshold is a probability value ( [0054], a threshold of y a threshold of y ms) or a binary output.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Kovacs and apply them on the teaching of Mu- O’Shea to provide artificial intelligence (AI)/machine learning (ML) techniques is used to assist or improve various aspects of network operation or network performance(Kovacs; [0029]).
Regarding claim 6, Mu- O’Shea does not teach further comprising: selecting the at least one machine learning model based at least in part on the channel characteristic prediction of the at least one machine learning model having a greatest reference signal receive power vector of the one or more machine learning models.
However, in an analogous art, Kovacs teaches further comprising:
selecting the at least one machine learning (ML) model based at least in part on the channel characteristic (CSI) prediction of the at least one machine learning (ML) model having a greatest (high) reference signal receive power (RSRP) vector of the one or more machine learning (ML) models ([0050]; [0051]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Kovacs and apply them on the teaching of Mu- O’Shea to provide artificial intelligence (AI)/machine learning (ML) techniques is used to assist or improve various aspects of network operation or network performance(Kovacs; [0029]).
Regarding claim 7, Mu further teaches further comprising:
receiving an indication of the one or more machine learning (artificial intelligence, AI) models from a network entity (base station) ([0071], UE receives AI model indication information configured by base station to accurately indicate one AI model to be used by the UE when the number of the AI models corresponding to the DMRS pattern is a plurality.).
Regarding claims 11 and 24, Mu- O’Shea does not teach further comprising: transmitting a report comprising one or more target metrics associated with the channel characteristic prediction; and receiving the input to the one or more machine learning models based at least in part on the report.
However, in an analogous art, Kovacs teaches further comprising:
transmitting a report comprising one or more target metrics(value) associated with the channel characteristic(RSRP) prediction ( [0059] ML model training 210 performs training of the ML model 212, e.g., based on measured RSRP values 312 reported by UEx to the CU. Hence the UE transmits report includes RSRP value.); and
receiving the input to the one or more machine learning (ML) models (212) based at least in part on the report ([0059] ML model training 210 performs training of the ML model 212, e.g., based on measured RSRP values 312 reported by UEx to the CU. Hence the 212 receives input based on the transmitted report from the UE.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Kovacs and apply them on the teaching of Mu- O’Shea to provide artificial intelligence (AI)/machine learning (ML) techniques is used to assist or improve various aspects of network operation or network performance(Kovacs; [0029]).
Regarding claims 14 and 27, Mu- O’Shea does not teach wherein the channel characteristic prediction comprises an indication of one or more likelihoods that a reference signal resource measurement cycle will change for one or more respective threshold number of times.
However, in an analogous art, Kovacs teaches wherein the channel characteristic prediction comprises an indication of one or more likelihoods that a reference signal resource measurement cycle will change for one or more respective threshold number of times ([0050]; [0051]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Kovacs and apply them on the teaching of Mu- O’Shea to provide artificial intelligence (AI)/machine learning (ML) techniques is used to assist or improve various aspects of network operation or network performance(Kovacs; [0029]).
6. Claims 8-10, 22 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Mu (US 2025/0038918 A1) in view of O’Shea (US 2023/0284048 A1), further in view of Tang (US 2024/0422073 A1).
Regarding claims 8 and 22, Mu- O’Shea does not teach further comprising: receiving first signaling indicating one or more common layers corresponding to a common set of weights for the plurality of machine learning models, one or more individual layers corresponding to an individual set of weights for the plurality of machine learning models, or any combination thereof.
However, in an analogous art, Tang teaches further comprising:
receiving first signaling indicating one or more common layers corresponding to a common set of weights for the plurality of machine learning ([0053]) models ([0054]), one or more individual layers corresponding to an individual set of weights for the plurality of machine learning models, or any combination thereof.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Tang and apply them on the teaching of Mu- O’Shea to provide the relative to using the same priority handling for all parameters of an AI/ML model, lower priority or importance transmission according to adaptive QoS handling helps to reduce communication overhead significantly, while maintaining training performance (Tang; [0123]).
Regarding claim 9, Mu- O’Shea does not teach further comprising: updating the one or more individual layers corresponding to the individual set of weights for the plurality of machine learning models based at least in part on training the plurality of machine learning models according to federated learning.
However, in an analogous art, Tang teaches further comprising:
updating the one or more individual layers corresponding to the individual set of weights for the plurality of machine learning (ML-[0053]) models ([0054]) based at least in part on training the plurality of machine learning(ML) models according to federated learning ([0052]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Tang and apply them on the teaching of Mu- O’Shea to provide the relative to using the same priority handling for all parameters of an AI/ML model, lower priority or importance transmission according to adaptive QoS handling helps to reduce communication overhead significantly, while maintaining training performance (Tang; [0123]).
Regarding claims 10 and 23, Mu- O’Shea does not teach further comprising: receiving second signaling indicating for the UE to train the plurality of machine learning models, wherein the updating is based at least in part on the second signaling.
However, in an analogous art, Tang teaches further comprising:
receiving second signaling indicating for the UE to train the plurality of machine learning models([0063], a second transmission configuration{ The network device then applies the received transmission configuration(s), for DL training to determine how parameters are to be transmitted by the network device to the UE—[0062]} to selectively transmit only another subset of the full set of model parameters, for layers 3 to 7.), wherein the updating([0054]) is based at least in part on the second signaling([0063]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Tang and apply them on the teaching of Mu- O’Shea to provide the relative to using the same priority handling for all parameters of an AI/ML model, lower priority or importance transmission according to adaptive QoS handling helps to reduce communication overhead significantly, while maintaining training performance (Tang; [0123]).
7. Claims 13, 19, 26 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Mu (US 2025/0038918 A1) in view of O’Shea (US 2023/0284048 A1), further in view of Lee_1 (US 2021/0014809 A1).
Regarding claims 13 and 26, Mu- O’Shea does not teach wherein the channel characteristic prediction comprises a probability or a binary output indicating that a first index of the respective reference signal resource with a strongest reference signal receive power is different from a second index of an additional reference signal resource associated with a strongest reference signal receive power for the input for a duration comprising a time between when the respective reference signal resource and the additional reference signal resource are measured.
However, in an analogous art, Lee_1 teaches wherein the channel characteristic prediction comprises a probability or a binary output indicating that a first index of the respective reference signal resource(each SSB) with a strongest reference signal receive power(RSRP) is different from a second index of an additional reference signal resource(each SSB) associated with a strongest reference signal receive power (RSRP) for the input for a duration comprising a time between when the respective reference signal resource(each SSB) and the additional reference signal resource(SSB) are measured ([0072]; [0073]; obvious the output indicating.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Lee_1 and apply them on the teaching of Mu- O’Shea to provide a method enables reducing and/or eliminating excessive processing delay of communication devices such that speed of signal processing and operating performance of the communication devices are improved (Lee_1; [0123]).
Regarding claims 19 and 28, Mu- O’Shea does not teach wherein the plurality of reference signal resources comprise a synchronization signal block resource, a channel state information-reference signal resource, or any combination thereof.
However, in an analogous art, Lee_1 teaches wherein the plurality of reference signal resources comprise a synchronization signal block resource (SSB) ([0072]; [0073]), a channel state information-reference signal resource, or any combination thereof.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Lee_1 and apply them on the teaching of Mu- O’Shea to provide a method enables reducing and/or eliminating excessive processing delay of communication devices such that speed of signal processing and operating performance of the communication devices are improved (Lee_1; [0123]).
8. Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Mu (US 2025/0038918 A1) in view of O’Shea (US 2023/0284048 A1), further in view of Lee_2 (US 2021/0006989 A1).
Regarding claim 15, Mu- O’Shea does not teach wherein the at least one machine learning model predicts one or more future channel characteristics based at least in part on one or more current channel characteristic measurements, one or more previous channel characteristic measurements, or any combination thereof associated with the respective reference signal resource.
However, in an analogous art, Lee_2 teaches wherein the at least one machine learning model (620) predicts one or more future channel characteristics (630) based at least in part on one or more current channel characteristic measurements (611) (See Fig. 6; [0124]), one or more previous channel characteristic measurements, or any combination thereof associated with the respective reference signal resource.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Lee_2 and apply them on the teaching of Mu- O’Shea to provide a learning algorithm (e.g., DNN), and improving link adaptation performance by using a channel forecast value. Further, the method for improving learning accuracy and learning efficiency for the learning algorithm (Lee_2; [0083]).
9. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Mu (US 2025/0038918 A1) in view of O’Shea (US 2023/0284048 A1), further in view of Zirwas (US 12381762 B2).
Regarding claim 17, Mu- O’Shea does not teach wherein the at least one machine learning model predicts one or more channel characteristics for a first frequency range based at least in part on measuring one or more channel characteristics for a second frequency range.
However, in an analogous art, Zirwas teaches wherein the at least one machine learning model predicts one or more channel characteristics for a first frequency range based at least in part on measuring one or more channel characteristics for a second frequency range (Abstract).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claim invention to take the teaching of Zirwas and apply them on the teaching of Mu- O’Shea to provide an improved estimate of channel information for all chains in the mixed resolution system that incorporates information from both the high-resolution RF chains and the low-resolution RF chains. (Zirwas; Col 8, line 12-16)
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEHEDI S ALEY whose telephone number is (571)270-0439. The examiner can normally be reached Mon, Thus, Fri: 9-5.
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/MEHEDI S ALEY/Examiner, Art Unit 2415
/JEFFREY M RUTKOWSKI/Supervisory Patent Examiner, Art Unit 2415
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