Detailed Action
1. This Office Action is in response to the Applicant’s preliminary amendment filed on 7/12/2023. In virtue of this communication, claims 1-20 are currently pending in this Office Action.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
3. Claim 1 is objected to because of the following informalities: there is a typographic error and “Network equipment” shall be read as “A network equipment”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
4. 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 of this title, 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.
5. 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.
6. Claims 1-4, 7, 8, and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pefkianakis et al. Pub. No.: US 2018/0124694 A1 in view of Gunasekara et al. Pub. No.: US 2019/0037418 A1 and Whinnett et al. Pub. No.: US 2024/0162955 A1.
Claim 1
Pefkianakis discloses Network equipment (wireless controller 104 in fig. 1 & 6), comprising:
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a processor (processing resource 126 in fig. 6); and
a memory (memory resource 128 in fig. 6) that stores executable instructions that, when executed by the processor (memory resource 128 storing instructions 130-136 in fig. 6 to be executed by the processor to perform steps in fig. 1-5), facilitate performance of operations, the operations comprising:
based on a first dataset (SINR based on CSI in 118 of fig. 3 & 5) comprising user equipment profile (CSI profile in par. 0029 and traffic profile in par. 0030) data associated with a user equipment (wireless client 110 in fig. 105), multiple-input, multiple-output (MU-MIMO in par. 0010 and see par. 0049 for client’s profile for MU-MIMO groups) configuration data associated with an access point (AP 108 in fig. 1-5, CSI profile in par. 0029, traffic profile in par. 0030, bandwidth profile in par. 0038), and measurement data associated with the access point (step 118 in fig.5, SINR value between a wireless client and each AP based on CSI), determining candidate signal-plus interference to noise ratio data for the user equipment (SINR in 118 in fig. 5 and par. 0028 and see SINR equation in par. 0041-0042); and
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modifying the candidate signal-to-interference-plus-noise ratio data into an estimated feasible upper threshold limit signal-to-interference-plus-noise ratio value for the user equipment (see steps 120-122 of fig. 5 and SINR equation in par. 0041; SINR is generally used a measure of through put, see par. 0054 for a client’s K best throughput PHY rate).
Although Pefkianakis does not explicitly disclose: “based on a second dataset comprising measurement data from the user equipment and trajectory data of the user equipment, an estimated feasible upper threshold limit signal-to-interference-plus-noise ratio value for the user equipment; and determining allocated power data for the access point with respect to the user equipment based on the estimated feasible upper limit signal-to-interference-plus-noise ratio value”, the claim limitations are considered obvious by the following rationales.
Firstly, to consider the obviousness of the claim limitations “based on a second dataset comprising measurement data from the user equipment and trajectory data of the user equipment, an estimated feasible upper threshold limit signal-to-interference-plus-noise ratio value for the user equipment”, initially, recall that Pefkianakis discloses CSI profile (par. 0045 & 0048, measured CSI), traffic profile, bandwidth profile, multiplath profile (par. 0038) and client’s profile (par. 0049). These profiles are measured data and hence, the profiles mentioned above in Pefkianakis would be reasonably interpreted as a first data set and a second data set (see fig. 5 of Pefkianakis). Furthermore, Pefkianakis describe how to measure or identify a client k’s best throughput PHY rate (par. 0054). If these teachings from Pefkianakis are compared to the addressing claim limitations, the claim limitation “trajectory data of the user equipment” may be arguable. In accordance with MPEP 2111, in light of specification, trajectory data could be reasonably interpreted for real time power allocation, network performance, past measurement data of UE. In particular, Gunasekara teaches network performance data (fig. 5), adjusting transmit power based on measured parameters (fig. 5a) and last connection data (fig. 5b) and beamforming transmit power with threshold (fig. 6a-b).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify wireless access point selection based on signal-to-interference-plus noise ration SINR value of Pefkianakis by providing client-based dynamic control of connections as taught in Gunasekara. Such a modification would have provided user mobile device and ad-hoc user wireless services to allow Wi-Fi or other WLAN RAT so that the consistent connection could have avoided service interruption as suggested in par. 0011-0013 in Gunasekara.
Secondly, to address the obviousness of the claim limitation “determining allocated power data for the access point with respect to the user equipment based on the estimated feasible upper limit signal-to-interference-plus-noise ratio value”, recall that Pefkianakis describe how to measure or identify a client k’s best throughput PHY rate (par. 0054). In addition, Gunasekara discloses assigning beamforming transmit power to WLAN and client ED threshold (fig. 6a-b). Indeed, these combined teachings from the prior art could have rendered the addressing claim limitations obvious. To advance the prosecution, further evidence is provided herein. In particular, Whinnett teaches training AI/ML model based on the received measurement to predict beamforming performance for MIMO modes (fig. 9-11, beamforming is power allocation to antenna, see par. 0080, 0136 & 0143) that corresponds to gNB power and processing resource load for SNR range, UE mobility and computation complexity (par. 0026, see par. 0037 for transmission parameter: power level).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify wireless access point selection based on signal-to-interference-plus noise ration SINR value of Pefkianakis in view of Gunasekara by providing beamforming for MIMO modes in O-RAN system as taught in Whinnett to obtain the claimed invention as specified in the claim. Such a modification would have provided RAN intelligent controller RIC to adjust beamforming related power so that a network performance of range, throughput and capacity could have been enhanced as suggested in par.0003-0005 in Whinnett.
Claim 2
Pefkianakis, in view of Gunasekara and Whinnett, discloses the network equipment of claim 1, wherein the determining of the candidate signal-plus interference to noise ratio data for the user equipment comprises (Pefkianakis, controller 104 in fig. 1 and fig. 6) determining the candidate signal-plus interference to noise ratio data for a user equipment group comprising the user equipment (Pefkianakis, 118-120 in fig. 5 and par.0054 a client’s best throughput and the throughput of a group; Gunasekara, SINR in par. 0181-0184; accordingly, the combined prior art renders the claim obvious).
Claim 3
Pefkianakis, in view of Gunasekara and Whinnett, discloses the network equipment of claim 1, wherein the first dataset further comprises at least one of:
constraint data representative of a constraint, or digital twin data representative of a digital twin (Pefkianakis, in fig. 5, SINR is based on CSI or constraint with CSI data in step 118, in step 120, SINR value is constraint with channel bandwidth and see constrains for equations in par. 0049, 0051-0057; accordingly, the combined prior art could have read on the claim unless claim specifically recites what are required to be the constraint data such as power or SINR ratio).
Claim 4
Pefkianakis, in view of Gunasekara and Whinnett, discloses the network equipment of claim 1, wherein the measurement data comprises at least one of:
path loss data representative of a path loss associated with the access point, channel quality indicator data representative of a channel quality associated with the access point (Pefkianakis, CSI in 118 of fig. 5), zero power reference data representative of a zero power reference associated with the access point, or quality of service identifier data representative of a quality of service associated with the access point (Pefkianakis, SINR in 120 of fig. 5; hence, the combined prior art meets the claim requirement as claim recites the alternatives).
Claim 7
Pefkianakis, in view of Gunasekara and Whinnett, discloses the network equipment of claim 1, wherein the modifying of the candidate signal-to-interference-plus-noise ratio data is further based on at least one of:
prior allocated power history data representative of past allocations of power (Pefkianakis, SINR in fig. 5; Gunasekara, history data in 562 and power allocation in 570 in fig. 5b, fig. 6a-b for assigning Tx power and beamforming; for these reasons, the combined prior art meets the claim condition as claim recites the alternative limitations), prior user equipment trajectory history data representative of past trajectories of previously connected user equipment, or prior user equipment measurement data representative of past measurements applicable to the previously connected user equipment.
Claim 8
Pefkianakis, in view of Gunasekara and Whinnett, discloses the network equipment of claim 1, wherein the modifying of the candidate signal-to-interference-plus-noise ratio data trained model is performed by an application of a near-real time radio access network controller (Pefkianakis, MAPS in par. 0025 and wireless controller in fig. 1 & 5; Gunasekara, coexistence controller 210 in fig. 6a-b; Whinnett, near-RT RIC 714 in fig. 7, an artificial intelligence/machine learning in fig. 9-11; accordingly, the combined prior art renders the claim obvious).
Claim 11
Pefkianakis, in view of Gunasekara and Whinnett, discloses the network equipment of claim 1, wherein the modifying of the candidate signal-to-interference-plus-noise ratio data is performed by an application of a distributed unit (Pefkianakis, fig. 5; Whinnett, AL/ML in fig. 9-11 and O-DU in par. 0115 for O-DU in fig. 11; and thus, the combined prior art reads on the claim).
Claim 12
Pefkianakis discloses a method (fig. 1-7), comprising:
obtaining, using a first model of a system (controller in fig. 1, 4 & 6) comprising a processor (processing resource 126 in fig. 6), a first dataset (SINR value based on CSI in 118 of fig. 5) comprising user equipment profile data (CSI profile in par. 0029 and traffic profile in par. 0030) associated with a user equipment (client 110 in fig. 1), multiple-input, multiple-output configuration data (MU-MIMO in par. 0010 and see par. 0049 for client’s profile for MU-MIMO groups) associated with an access point, and first measurement data associated with the access point (AP 108 in fig. 1-5, CSI profile in par. 0029, traffic profile in par. 0030, bandwidth profile in par. 0038);
determining, using the first model of the system, candidate signal-to-interference-plus-noise ratio data (SINR in 118 in fig. 5 and par. 0028 and see SINR equation in par. 0041-0042);
modifying, by the system, the candidate signal-to-interference-plus-noise ratio data into an estimated feasible upper threshold limit signal-to-interference-plus-noise ratio value for the user equipment (see steps 120-122 of fig. 5 and SINR equation in par. 0041; SINR is generally used a measure of through put, see par. 0054 for a client’s K best throughput PHY rate).
Although Pefkianakis does not explicitly disclose: “the modifying being based on second measurement data from the user equipment and trajectory data of the user equipment; and determining, using a second model of the system, real time power allocation coefficient data for the access point with respect to the user equipment based on a second dataset comprising the estimated feasible upper limit signal-to-interference-plus-noise ratio value”, the claim limitations are considered obvious by the following rationales.
Firstly, to consider the obviousness of the claim limitations “the modifying being based on second measurement data from the user equipment and trajectory data of the user equipment; a second dataset comprising the estimated feasible upper limit signal-to-interference-plus-noise ratio value”, initially, recall that Pefkianakis discloses CSI profile (par. 0045 & 0048, measured CSI), traffic profile, bandwidth profile, multiplath profile (par. 0038) and client’s profile (par. 0049). These profiles are measured data and hence, the profiles mentioned above in Pefkianakis would be reasonably interpreted as a first data set and a second data set (see fig. 5 of Pefkianakis). Furthermore, Pefkianakis describe how to measure or identify a client k’s best throughput PHY rate (par. 0054). If these teachings from Pefkianakis are compared to the addressing claim limitations, the claim limitation “trajectory data of the user equipment” may be arguable. In accordance with MPEP 2111, in light of specification, trajectory data could be reasonably interpreted for real time power allocation, network performance, past measurement data of UE. In particular, Gunasekara teaches network performance data (fig. 5), adjusting transmit power based on measured parameters (fig. 5a) and last connection data (fig. 5b) and beamforming transmit power with threshold (fig. 6a-b).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify wireless access point selection based on signal-to-interference-plus noise ration SINR value of Pefkianakis by providing client-based dynamic control of connections as taught in Gunasekara. Such a modification would have provided user mobile device and ad-hoc user wireless services to allow Wi-Fi or other WLAN RAT so that the consistent connection could have avoided service interruption as suggested in par. 0011-0013 in Gunasekara.
Secondly, to address the obviousness of the claim limitation “determining, using a second model of the system, real time power allocation coefficient data for the access point with respect to the user equipment”, recall that Pefkianakis describes how to measure or identify a client k’s best throughput PHY rate (par. 0054). In addition, Gunasekara discloses assigning beamforming transmit power to WLAN and client ED threshold (fig. 6a-b). Indeed, these combined teachings from the prior art could have rendered the addressing claim limitations obvious. To advance the prosecution, further evidence is provided herein. In particular, Whinnett teaches training AI/ML model based on the received measurement to predict beamforming performance for MIMO modes (fig. 9-11, beamforming is power allocation to antenna, see par. 0080, 0136 & 0143; see Non-RT RIC and Near-RT RIC in fig. 1-8) that corresponds to gNB power and processing resource load for SNR range, UE mobility and computation complexity (par. 0026, see par. 0037 for transmission parameter: power level).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify wireless access point selection based on signal-to-interference-plus noise ration SINR value of Pefkianakis in view of Gunasekara by providing beamforming for MIMO modes in O-RAN system as taught in Whinnett to obtain the claimed invention as specified in the claim. Such a modification would have provided RAN intelligent controller RIC to adjust beamforming related power so that a network performance of range, throughput and capacity could have been enhanced as suggested in par.0003-0005 in Whinnett.
Claim 13
Pefkianakis, in view of Gunasekara and Whinnett, discloses the method of claim 12, wherein the first model is incorporated into a non-real time radio access network intelligent controller (Pefkianakis, controller 104 in fig. 1 and fig. 6; Whinnett, NON-RT RIC 712 in fig. 7), and wherein the determining of the candidate signal-to-interference-plus-noise ratio data comprises inputting the first dataset to the first model (Pefkianakis, 118-120 in fig. 5 and par.0054 a client’s best throughput and the throughput of a group; Gunasekara, SINR in par. 0181-0184; accordingly, the combined prior art renders the claim obvious).
Claim 14
Pefkianakis, in view of Gunasekara and Whinnett, discloses the method of claim 13, wherein the first model is coupled to a near-real time radio access network intelligent controller (Pefkianakis, MAPS in par. 0025 and wireless controller in fig. 1 & 5; Whinnett, the near real-time RIC in par. 0004 and Near-RT RIC 714 in fig. 7), and further comprising communicating, by the system, the candidate signal-to-interference-plus-noise ratio data to the near-real time radio access network intelligent controller (Pefkianakis, SINR in steps 118-120 in fig. 5; Whinnett, Near-RT RIC 714 in fig. 7), wherein the modifying of the candidate signal-to-interference-plus-noise ratio data into the estimated feasible upper threshold limit signal-to-interference-plus-noise ratio value is performed by an application of the near-real time radio access network intelligent controller (Pefkianakis, SINR in steps 118-120 in fig. 5 and identifying k’s best throughput PHY rate and the throughput of the group in par. 0054 & 0057; Whinnett, Near-RT RIC 714 in fig. 7 and AI/ML in fig. 9-11; accordingly, one of ordinary skill in the art would have expected the combined prior art to perform equally well to the claim).
Claim 15
Pefkianakis, in view of Gunasekara and Whinnett, discloses the method of claim 14, wherein the near-real time radio access network intelligent controller is coupled to a distributed unit comprising the second model (Pefkianakis, MAPS in par. 0025 and wireless controller in fig. 1 & 5; Whinnett, Near-Real Time RIC 814 in fig. 8) , and further comprising communicating, by the system, the estimated feasible upper threshold limit signal-to-interference-plus-noise ratio value from the near-real time radio access network intelligent controller to the distributed unit (Pefkianakis, SINR in steps 118-120 in fig. 5 and identifying k’s best throughput PHY rate and the throughput of the group in par. 0054 & 0057; Whinnett, Near-RT RIC 814 in fig. 8 connecting to O-DU 815 via E2 interface, see AI/ML in fig. 9-11; and thus, the combined prior art renders the claim obvious).
Claim 16
Pefkianakis discloses a non-transitory machine-readable medium (fig. 1-5 for measuring SINR based on CSI and channel bandwidth), comprising executable instructions (instructions 128-136 in fig. 6) that, when executed by a processor (processing resource 126 in fig. 6), facilitate performance of operations (performing operations in fig. 1-5), the operations comprising:
determining, using a first model (controller in fig. 1, 4 & 6, and MAPs in par. 0025), candidate signal-plus interference to noise ratio data for a user equipment (client 110 in fig. 1; SINR value based on CSI in 118 of fig. 5; CSI profile in par. 0029 and traffic profile in par. 0030);
communicating the candidate signal-plus interference to noise ratio data for a user equipment (SINR in 118 in fig. 5 and par. 0028 and see SINR equation in par. 0041-0042);
determining, based on the candidate signal-plus interference to noise ratio data (fig. 5 for SINR based on CSI in step 118 and channel bandwidth in step 120), an estimated feasible upper threshold limit signal-to-interference-plus-noise ratio value for the user equipment (SINR in 118 in fig. 5 and par. 0028 and see SINR equation in par. 0041-0042; see par. 0054 for a client’s K best throughput PHY rate); and
communicating the estimated feasible upper threshold limit signal-to-interference-plus-noise ratio value for the user equipment (see steps 120-122 of fig. 5 and SINR equation in par. 0041; SINR is generally used a measure of through put, see par. 0054 for a client’s K best throughput PHY rate).
Although Pefkianakis does not explicitly disclose: “distributing a power control engine across a first layer comprising a non-real time radio access network intelligent controller, a second layer comprising a near-real time radio access network intelligent controller, and a third layer comprising a distributed unit; a first model of the first layer; the first layer to the second layer; the second layer to the third layer; a second model of the third layer; performing, by the third layer, synchronization signal block beam sweeping based on the sparse candidate probing beam sweep measurement subgroup; and determining, based on the estimated feasible upper limit signal-to-interference-plus-noise ratio value using a second model of the third layer, allocated power data for an access point with respect to the user equipment”, the claim limitations are considered obvious by the following rationales.
Firstly, to consider the obviousness of the claim limitation “performing, synchronization signal block beam sweeping based on the sparse candidate probing beam sweep measurement subgroup”, initially, recall that Pefkianakis discloses measuring CSI profile (par. 0045 & 0048, measured CSI), traffic profile, bandwidth profile, multiplath profile (par. 0038) and client’s profile (par. 0049). In pertinent art, it’s intrinsic feature to measure a synchronization signal. In particular, Gunasekara teaches network performance data (fig. 5), adjusting transmit power based on measured parameters (fig. 5a) and the measuring the primary synchronization signal PSS (par. 0219).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify wireless access point selection based on signal-to-interference-plus noise ration SINR value of Pefkianakis by providing client-based dynamic control of connections as taught in Gunasekara. Such a modification would have provided user mobile device and ad-hoc user wireless services to allow Wi-Fi or other WLAN RAT so that the consistent connection could have avoided service interruption as suggested in par. 0011-0013 in Gunasekara.
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Secondly, the claim limitations “distributing a power control engine across a first layer comprising a non-real time radio access network intelligent controller, a second layer comprising a near-real time radio access network intelligent controller, and a third layer comprising a distributed unit; a first model of the first layer; the first layer to the second layer; the second layer to the third layer; a second model of the third layer” are considered obvious by the rationales found in Whinnett. Recall that Pefkianakis describes how to measure or identify a client k’s best throughput PHY rate (par. 0054). In addition, Gunasekara discloses assigning beamforming transmit power to WLAN and client ED threshold (fig. 6a-b). In particular, Whinnett teaches a non-real time radio access network intelligent controller (NON-RT in fig. 1-8), a near-time radio access network intelligent controller (NEAR-RT RIC in fig. 1-8), a distributed unit (O-DU in fig. 1-8) and a first model and a second model could be reasonably interpreted to as depicted in fig. 2 & 7-8.
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Lastly, to address the obviousness of the claim limitations “determining, allocated power data for an access point with respect to the user equipment”, recall that Pefkianakis describes how to measure or identify a client k’s best throughput PHY rate (par. 0054). In addition, Gunasekara discloses assigning beamforming transmit power to WLAN and client ED threshold (fig. 6a-b). In particular, Whinnett teaches training AI/ML model based on the received measurement to predict beamforming performance for MIMO modes (fig. 9-11, beamforming is power allocation to antenna, see par. 0080, 0136 & 0143) that corresponds to gNB power and processing resource load for SNR range, UE mobility and computation complexity (par. 0026, see par. 0037 for transmission parameter: power level).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify wireless access point selection based on signal-to-interference-plus noise ration SINR value of Pefkianakis in view of Gunasekara by providing beamforming for MIMO modes in O-RAN system as taught in Whinnett to obtain the claimed invention as specified in the claim. Such a modification would have provided RAN intelligent controller RIC to adjust beamforming related power so that a network performance of range, throughput and capacity could have been enhanced as suggested in par.0003-0005 in Whinnett.
Claim 17
Pefkianakis, in view of Gunasekara and Whinnett, discloses the non-transitory machine-readable medium of claim 16, wherein the operations further comprise communicating the candidate signal-plus interference to noise ratio data from the first layer to the third layer (Pefkianakis, 118-120 in fig. 5 and par.0054 a client’s best throughput and the throughput of a group; Gunasekara, SINR in par. 0181-0184; Whinnett, layers in fig. 2 & 8; accordingly, the combined prior art renders the claim obvious).
Claim 18
Pefkianakis, in view of Gunasekara and Whinnett, discloses the non-transitory machine-readable medium of claim 16, wherein the determining of the candidate signal-plus interference to noise ratio data for a user equipment comprises inputting user equipment profile data associated with the user equipment (Pefkianakis, CSI profile in par. 0029, traffic profile in par. 0030, multipath and bandwidth profiles in par. 0038), multiple-input, multiple-output configuration data associated with an access point (Whinnett, par. 0027 for MIMO mode and corresponding SINR, UE mobility data), and measurement data associated with the access point to the first model (Pefkianakis, fig. 9-11; for these reasons, the combined prior art reads on the claim).
Claim 19
Pefkianakis, in view of Gunasekara and Whinnett, discloses the non-transitory machine-readable medium of claim 16, wherein the determining of the estimated feasible upper threshold limit signal-to-interference-plus-noise ratio value for the user equipment (Pefkianakis, SINR in fig. 5 and thresholds in par. 0055 & 0057) comprises inputting trajectory data of the user equipment into a third model running as an application on the near-real time radio access network intelligent controller (Pefkianakis, fig. 7-11, AI/ML model could be used to predict the data Near-RT RIC of fig. 7-8; accordingly, the combined prior art would have been expected to perform equally well to the claim).
Claim 20
Pefkianakis, in view of Gunasekara and Whinnett, discloses the non-transitory machine-readable medium of claim 16, wherein the determining of the allocated power data comprises inputting, to the second model, location and speed data of the user equipment (Pefkianakis, speeds in par. 0016 & 0025, location in par. 0020; Whinnett, AI/ML in fig. 9-11), and inputting, to the second model, a low threshold signal-to-interference-plus-noise ratio value corresponding to the candidate signal-plus interference to noise ratio data (Pefkianakis, SINR in steps in 118-120 in fig. 5 and thresholds for throughput in par. 0054 & 0057; Whinnett, AI/ML in fig. 9-11; accordingly, one of ordinary skill in the art would have expected the combined prior art to performed equally well to the claim, see MPEP 2143, Exemplary Rationale F).
Allowable Subject Matter
7. Claims 5-6 and 9-10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Contact Information
8. Any inquiry concerning this communication or earlier communications from the
examiner should be directed to SAN HTUN whose telephone number is (571)270-3190. The examiner can normally be reached Monday - Thursday 7 AM - 5 PM.
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/SAN HTUN/
Primary Examiner, Art Unit 2643