CTNF 19/051,071 CTNF 102056 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. 07-34-01 Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention . Regarding claims 1-20, the claims use the phrase “throughput prediction” and “throughput predication” interchangeably. Please see claims; 1 lines 7 and 10, 3 line 1, 9 line 2, 10 line 1, 11 lines 6 and 9, 13 line 1, 19 line 2, 20 line 1. It is unclear and indefinite whether there is a difference in meaning between prediction and predication. Predication may be defined as “affirming one thing of another; affirmation; assertion’. Prediction on the other hand may be defined as “the act of foretelling; also, that which is foretold; prophecy.” These clearly have distinct meanings and should be clarified by the applicant. Therefore, due to the different meanings of the terms “throughput prediction” and “throughput predication” in the claims, it is unclear and indefinite and fails to particularly point out and distinctly claim the subject matter of the application. Claims that are not mentioned in the identified claims, are “rejected based on their dependencies” hence in sum, all pending claims of claims 1-20 are rejected herein. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-2, 8-12, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bellamkonda et al. US-20160248496-A1 (hereinafter, Bellamkonda) in view of Kittipong et al. WO-2019064163-A1 (hereinafter, Kittipong) . Regarding claim 1, Bellamkonda teaches a base station (BS) in a wireless network (“the mobile device 500 may establish an IP connection 1003 through either its subscribed wide area network, through a wireless local area network, or etc.” see paragraph 60) , comprising: a memory; and a processor coupled to the memory (“may accordingly send information to, or receive information from, a processor 510. In addition to the processor 510, the mobile device 100 components include, but are not limited to, transceivers 502, antenna selection and tuning logic 503, antennas 507, location detection logic 509 (such as, but not limited to, a GPS receiver), display and user interface” (Paragraph 45 lines 6 – 11, also note paragraph 48). Receive, from a user equipment (UE), a set of input metrics (“The CQI information may be sent as feedback to the transmitting base station for further adjustments of the transmitted streams. In accordance with the embodiments, the mobile device 100 includes a correlation estimator 109 that is operatively coupled to the transceivers 105 to obtain signal quality metrics 108 including signal-to-noise-ratio (SNR) and CQI information” (Paragraph 27 lines 4 - 10), this is identical to the claim language, as UE is sending CQI and SNR to a base station. Bellamkonda teaches, perform a transmit antenna selection (TAS) throughput prediction using a linear model, (“the exact quantification can be facilitated by linear interpolation between the points. This can be used in operation block 311 to provide feedback to the antenna tuning and selection logic 103. The method may continue to loop to operation block 303 and make adjustments as needed until the channel is terminated in decision block 315” (see paragraph 38 lines 18-23). However, Bellamkonda does not explicitly teach claim tied to SNR to CQI mappings. Kittipong teaches , in a similar field of endeavor, SNR to CQI mappings (“the UE supporting multiple CQI processes may have multiple sets of SNR-to-CQI mappings for different BLER targets” (note page 10 lines 13-14, also see example in table 1). Kittipong further teaches limiting interference/noise when transmitting various forms of data (“Network node 120 may configure wireless device 110 with the number of CQI processes and associated BLER values (e.g., 10% and 0.1%). An advantage is that network node 120 may set the MCS” - Modulation and Coding Scheme “more appropriately for low BLER” - Block Error Ratio “targets and extends the range of supported code rates beyond the default values for robust transmissions”, (see page 9 lines 17 to 21). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to incorporate CQI and SNR mapping with selecting an optimal antenna using a method of antenna selection into the system of Bellamkonda by using the quality metrics that are already being collected and adding the SNR/CQI information for the purpose of limiting noise (“Network node 120 may configure wireless device 110 with the number of CQI processes and associated BLER values (e.g., 10% and 0.1%). An advantage is that network node 120 may set the MCS” - Modulation and Coding Scheme “more appropriately for low BLER” - Block Error Ratio “targets and extends the range of supported code rates beyond the default values for robust transmissions”, (see page 9 lines 17 to 21). Regarding claim 2 , as applied to claim 1. Bellamkonda further teaches wherein the set of input metrics is associated with at least one of a CQI, a rank indicator, a number of layers, a modulation and coding scheme, a beamforming loss, an uplink sounding reference signal (SRS) signal to noise ratio (SNR), a downlink SNR, or a hybrid automatic repeat request (HARQ) acknowledgement and negative acknowledgement (“a method of transceivers 105 to obtain signal quality metrics 108 including signal-to-noise-ratio (SNR) and CQI information” (paragraph 27 lines 4 - 10). Regarding claim 8, as applied to claim 1. Kittipong further teaches , wherein the mapping from CQI to SNR is variable based on a cell to which the BS belongs, a UE with which the BS communicates, or a configuration of the BS (“the UE supporting multiple CQI processes may have multiple sets of SNR-to-CQI mappings for different BLER targets” (see example in Table 1 also page 10 lines 13-14). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to add the SNR-to-CQI mappings to the metrics collected from the UE, which it is assumed that it would be (“reasonable for a UE supporting different services targeting very low BLER, such as ultra-reliable low-latency communications (URLLC) and normal BLER such as mobile broadband (MBB). The mappings may also be available for different transmission modes and are UE-dependent” (page 10 lines 14-17). Regarding claim 9, as applied to claim 1. Bellamkonda teaches, the processor is further configured to offset the TAS throughput predication, this is seen when the antenna selection and tuning logic (“may switch to the appropriate MIMO diversity antenna pair as an initial setting (i.e. prior to determination of the actual SNR condition . ” (paragraph 42 lines 13-16) by a pre-defined parameter (“may predict that a “low” or “high” SNR condition will result and may send a flag (“low” SNR predict flag or “high” SNR predict flag) to the antenna selection and tuning logic 410” (paragraph 42 lines 9-12) . Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to combine TAS throughput predication with a pre-defined parameter such as a metric such as low or high SNR condition to select the optimal antenna to establish a connection with. It is evident that it is beneficial for a server (which by using broadest reasonable interpretation can include a processor) to “predict antenna correlation for a given mobile device based on statistics data and to suggest antenna adjustments to subscribed mobile devices in order to improve antenna correlation and to help the mobile device obtain better throughput” (paragraph 61 lines 18-22). Regarding claim 10, as applied to claim 1. Bellamkonda further teaches the TAS throughput predication is performed based on a parameter that changes based on uplink SNR or based on UE speed. This capability can be achieved such that (“the antenna selection logic may be a combination of RF hardware for implementation of switching between MIMO diversity antennas, and software or firmware executed by a processor that makes the decision regarding when to switch to a given antenna, etc.” (see paragraph 55 lines 5-12.) For the benefit of improving “MIMO antenna performance under given conditions. In one embodiment, a correlation estimator is operative to determine an approximation of instantaneous antenna correlation values” (paragraph 15 lines 4-8). Regarding claim 11, Bellamkonda teaches computer-implemented method for communication by a base station (BS) in a wireless network (“the mobile device 500 may establish an IP connection 1003 through either its subscribed wide area network, through a wireless local area network, or etc.” see paragraph 60) , comprising: receiving, from a user equipment (UE), a set of input metrics; (“The CQI information may be sent as feedback to the transmitting base station for further adjustments of the transmitted streams. In accordance with the embodiments, the mobile device 100 includes a correlation estimator 109 that is operatively coupled to the transceivers 105 to obtain signal quality metrics 108 including signal-to-noise-ratio (SNR) and CQI information” (Paragraph 27 lines 4 - 10), this is identical to the claim language, as UE is sending CQI and SNR to a base station. Bellamkonda teaches, performing, from the set of input metrics, a mapping from a channel quality indicator (CQI) to signal to noise ratio (SNR); performing a transmit antenna selection (TAS) throughput prediction using a linear model (“the exact quantification can be facilitated by linear interpolation between the points. This can be used in operation block 311 to provide feedback to the antenna tuning and selection logic 103. The method may continue to loop to operation block 303 and make adjustments as needed until the channel is terminated in decision block 315” (see paragraph 38 lines 18-23). that uses the mapping from the CQI to SNR; and selecting a TAS mode as a multiple input multiple output (MIMO) mode based on the TAS throughput prediction. However, Bellamkonda does not explicitly teach claim tied to SNR to CQI mappings. Kittipong teaches , in a similar field of endeavor, SNR to CQI mappings (“the UE supporting multiple CQI processes may have multiple sets of SNR-to-CQI mappings for different BLER targets” (note page 10 lines 13-14, also see example in table 1). Kittipong further teaches limiting interference/noise when transmitting various forms of data (“Network node 120 may configure wireless device 110 with the number of CQI processes and associated BLER values (e.g., 10% and 0.1%). An advantage is that network node 120 may set the MCS” - Modulation and Coding Scheme “more appropriately for low BLER” - Block Error Ratio “targets and extends the range of supported code rates beyond the default values for robust transmissions”, (see page 9 lines 17 to 21). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to incorporate CQI and SNR mapping with selecting an optimal antenna using a method of antenna selection into the system of Bellamkonda by using the quality metrics that are already being collected and adding the SNR/CQI information for the purpose of limiting noise (“network node 120 may configure wireless device 110 with the number of CQI processes and associated BLER values (e.g., 10% and 0.1%). An advantage is that network node 120 may set the MCS” - Modulation and Coding Scheme “more appropriately for low BLER” - Block Error Ratio “targets and extends the range of supported code rates beyond the default values for robust transmissions”, (see page 9 lines 17 to 21). Regarding claim 12, as applied to claim 11. Bellamkonda further teaches the computer-implemented method of claim 11, wherein the set of input metrics is associated with at least one of a CQI, a rank indicator, a number of layers, a modulation and coding scheme, a beamforming loss, an uplink sounding reference signal (SRS) signal to noise ratio (SNR), a downlink SNR, or a hybrid automatic repeat request (HARQ) acknowledgement and negative acknowledgement (“a method of transceivers 105 to obtain signal quality metrics 108 including signal-to-noise-ratio (SNR) and CQI information” (paragraph 27 lines 4 - 10). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to combine the different set of metrics to ensure a more reliable signal connection. Which would help (“improve antenna correlation for the existing transmission being received” (paragraph 27 lines 26-27) . Regarding claim 18, as applied to claim 11. Kittipong teaches the computer-implemented method of claim 11, wherein the mapping from CQI to SNR is variable based on a cell to which the BS belongs, a UE with which the BS communicates, or a configuration of the BS (“the UE supporting multiple CQI processes may have multiple sets of SNR-to-CQI mappings for different BLER targets” (see example in Table 1 also page 10 lines 13-14). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to add the SNR-to-CQI mappings to the metrics collected from the UE, which it is assumed that it would be (“reasonable for a UE supporting different services targeting very low BLER, such as ultra-reliable low-latency communications (URLLC) and normal BLER such as mobile broadband (MBB). The mappings may also be available for different transmission modes and are UE-dependent” (page 10 lines 14-17). Regarding claim 19, as applied to claim 11. Bellamkonda further teaches, offsetting the TAS throughput predication, this is seen when the antenna selection and tuning logic (“may switch to the appropriate MIMO diversity antenna pair as an initial setting (i.e. prior to determination of the actual SNR condition . ” (paragraph 42 lines 13-16) by a pre-defined parameter (“may predict that a “low” or “high” SNR condition will result and may send a flag (“low” SNR predict flag or “high” SNR predict flag) to the antenna selection and tuning logic 410” (paragraph 42 lines 9-12) . Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to combine TAS throughput predication with a pre-defined parameter or metric such as low or high SNR condition to select the optimal antenna when establishing a connection. It is evident that it is beneficial for a server (which by using broadest reasonable interpretation can include a processor) to “predict antenna correlation for a given mobile device based on statistics data and to suggest antenna adjustments to subscribed mobile devices in order to improve antenna correlation and to help the mobile device obtain better throughput” (paragraph 61 lines 18-22). Regarding claim 20, as applied to claim 11. Bellamkonda teaches the TAS throughput predication is performed based on a parameter that changes based on uplink SNR or based on UE speed. (“The antenna selection logic may be a combination of RF hardware for implementation of switching between MIMO diversity antennas, and software or firmware executed by a processor that makes the decision regarding when to switch to a given antenna, etc.” see paragraph 55 lines 5-12) . Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to use the methodology of adapting TAS throughput based on changes to uplink SNR/UE speed. For the benefit of improving (“MIMO antenna performance under given conditions. In one embodiment, a correlation estimator is operative to determine an approximation of instantaneous antenna correlation values” (paragraph 15 lines 4-8) . 07-21-aia AIA Claim s 3-7, and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bellamkonda et al. US-20160248496-A1 (hereinafter, Bellamkonda) in view of Kittipong et al. WO-2019064163-A1 (hereinafter, Kittipong) and Chung et al. US-20130286877-A1 (hereinafter, Chung) . Regarding claim 3, as applied to claim 1. Bellamkonda further teaches wherein the TAS (transmit antenna selection, which would be similar to antenna tuning and selection logic using broadest reasonable interpretation) throughput predication is approximated by a linear function (“the exact quantification can be facilitated by linear interpolation between the points. This can be used in operation block 311 to provide feedback to the antenna tuning and selection logic 103” (paragraph 39 lines 18-23), based on the mapping from CQI to SNR, a beamforming loss (“wireless signal 130 may comprise one or more beams. Particular beams may be beamformed in a particular direction” (page 8 lines 11-12) , Bellamkonda in view of Kittipong teaches all, as applied to claim 1. However, Bellamkonda in view of Kittipong does not explicitly teach and an SRS to generate a spectral efficiency. Chung teaches an SRS to generate a spectral efficiency (“maximize the cell capacity by adjusting the coding rate of channel decoder to control the capability of error protection, and by selecting the suitable modulation scheme to achieve the spectral efficiency”). In addition, Sounding Reference Signal (SRS) would be identical to the method mentioned previously, as SRS is a signal that helps a base station estimate uplink channel. Further, Kittipong teaches, the UE supporting multiple CQI processes may have multiple sets of SNR-to-CQI mappings for different BLER targets (see example in Table 1). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to incorporate linear mapping of CQI to SNR, as well as beamforming methodologies, which would reduce noise. This would consist of (“a method of receiving channel quality information for use in a network node of a wireless communication network comprises sending a measurement configuration to a wireless device” (page 4 lines 1-3). Regarding claim 4, Bellamkonda in view of Kittipong teaches all, as applied to claim 3. However, Bellamkonda in view of Kittipong does not explicitly teach wherein the linear function is bounded by a lower bound and an upper bound on the spectral efficiency per layer that is supported by the BS. Chung teaches wherein the linear function is bounded by a lower bound and an upper bound on the spectral efficiency per layer that is supported by the BS. (“The base station can schedule proper data transmission for the terminals to fit channel capacity of each terminal, and maximize the cell capacity by adjusting the coding rate of channel decoder to control the capability of error protection, and by selecting the suitable modulation scheme to achieve the spectral efficiency” (paragraph 4 lines 14-19). Also see, (“in an embodiment, each of the mapping function g(i,.) is a linear function defined over thresholds” (paragraph 35 lines 21-22) Also, (“the two predetermined ranges are respect proximities of a bottom bound and an upper bound of BLER. (paragraph 12 lines 1-6). If the BLER is lower, the communication is more reliable, which would be advantageous when limiting noise. Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to incorporate the MIMO methodology with suitable modulation scheme to achieve the spectral efficiency per layer (where one layer is carrying the data) to limit signal interference. It is essential that (“the base station can schedule proper data transmission for the terminals to fit channel capacity of each terminal, and maximize the cell capacity by adjusting the coding rate of channel decoder to control the capability of error protection, and by selecting the suitable modulation scheme to achieve the spectral efficiency.” (paragraph 4 lines 14-19). Regarding claim 5, Bellamkonda in view of Kittipong teaches all, as applied to claim 3. However, Bellamkonda in view of Kittipong does not explicitly teach wherein the linear function is bounded by a lower bound and an upper bound on a total spectral efficiency that is supported by the BS . Chung teaches wherein the linear function is bounded by a lower bound and an upper bound (“predetermined ranges are respect proximities of a bottom bound and an upper bound of BLER” – Block Error Rate ) on a total spectral efficiency - (where the sum of all active layers is being considered) that is supported by the BS. This is done (“by selecting the suitable modulation scheme to achieve the spectral efficiency” (paragraph 4 lines 14-19). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to incorporate the MIMO methodology with suitable modulation scheme to achieve total spectral efficiency (where the sum of all active layers is being considered) to reduce noise. (“The two predetermined ranges are respect proximities of a bottom bound and an upper bound of BLER.” (paragraph 12 lines 1-6). If the BLER is lower, the communication is more reliable, which would be advantageous when limiting noise. Regarding claim 6, Bellamkonda in view of Kittipong teaches all, as applied to claim 1. However, Bellamkonda in view of Kittipong does not explicitly teach wherein the processor is further configured to select a particular linear function from a plurality of linear functions based on at least one of a number of UEs, a network load, or a power consumption. Chung teaches the processor ( “terminal 14 can include a memory (volatile or nonvolatile) which records codes, and a processor which executes the codes to implement the flow 100 and/or 200” (paragraph 89 lines 7-9). is further configured to select a particular linear function from a plurality of linear functions (“ the mapping of reported CQI and SIR is also a linear relation under a static channel” (paragraph 34 lines 10-11 based on at least one of a number of UEs, a network load, or a power consumption. This can be seen using (“power scheduling information of the base station ”) and also considering that “a s a terminal (UE) operates, it collects power scheduling information which reflects how a serving base station schedules power for different indicator levels CQI. By analyzing power scheduling information, If the terminal finds that the base station schedules additional power for a given indicator level CQI(i)” (see paragraph 16 lines 4-12) . Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to use metrics such as power scheduling information when attempting to connect to a base station. It is a lso clear that (“an efficient way to maximize the cell capacity is to allocate higher data rate to terminals with better channel qualities. (paragraph 4 lines 7- 9 ). Regarding claim 7, Bellamkonda in view of Kittipong teaches all, as applied to claim 1. However, Bellamkonda in view of Kittipong does not explicitly teach the processor is further configured to select a particular linear function from a plurality of linear functions for a particular UE based on a traffic type or a quality of service (QoS) requirement. Chung teaches, the processor is further configured to select a particular linear function (“ the mapping of reported CQI and SIR is also a linear relation under a static channel” (paragraph 34 lines 10-11) from a plurality of linear functions for a particular UE based on a traffic type or a quality of service (QoS) requirement. (“an objective of the invention is providing a method for reporting CQI by a transmitter of a communication system” (paragraph 18 lines 1-3). Which contributes to QoS by collecting CQI metrics and further limiting noise. Also see (“the adaptation module 26 can be implemented by hardware, firmware and/or software. For example, the terminal 14 can include a memory (volatile or nonvolatile) which records codes, and a processor which executes the codes ” (paragraph 89 lines 2-9). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to combine a processor and having it configured to use a linear function with UE based on a traffic type or a quality of service (QoS) requirement. It is a clear that (“an efficient way to maximize the cell capacity is to allocate higher data rate to terminals with better channel qualities. (paragraph 4 lines 7-13). Regarding claim 13 , as applied to claim 11. Bellamkonda further teaches, the computer-implemented method of claim 11, wherein the TAS (transmit antenna selection, which would be similar to antenna tuning and selection logic using broadest reasonable interpretation) throughput predication is approximated by a linear function (“the exact quantification can be facilitated by linear interpolation between the points. This can be used in operation block 311 to provide feedback to the antenna tuning and selection logic 103” (paragraph 39 lines 18-23), based on the mapping from CQI to SNR, a beamforming loss (“wireless signal 130 may comprise one or more beams. Particular beams may be beamformed in a particular direction” (page 8 lines 11-12) , (Bellamkonda in view of Kittipong teaches all, as applied to claim 11. However, Bellamkonda in view of Kittipong does not explicitly teach and an SRS to generate a spectral efficiency. Chung teaches an SRS to generate a spectral efficiency (“maximize the cell capacity by adjusting the coding rate of channel decoder to control the capability of error protection, and by selecting the suitable modulation scheme to achieve the spectral efficiency”). In addition, Sounding Reference Signal (SRS) would be identical to the method mentioned previously, as SRS is a signal that helps a base station estimate uplink channel. Further, Kittipong teaches, the UE supporting multiple CQI processes may have multiple sets of SNR-to-CQI mappings for different BLER targets (see example in Table 1). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to incorporate linear mapping of CQI to SNR, as well as beamforming methodologies, which would reduce noise. This would consist of (“a method of receiving channel quality information for use in a network node of a wireless communication network comprises sending a measurement configuration to a wireless device” (page 4 lines 1-3). Regarding claim 14, as applied to claim 13. Chung teaches wherein the linear function is bounded by a lower bound and an upper bound on the spectral efficiency per layer that is supported by the BS (“the base station can schedule proper data transmission for the terminals to fit channel capacity of each terminal, and maximize the cell capacity by adjusting the coding rate of channel decoder to control the capability of error protection, and by selecting the suitable modulation scheme to achieve the spectral efficiency” (paragraph 4 lines 14-19). Also see, (“in an embodiment, each of the mapping function g(i,.) is a linear function defined over thresholds” (paragraph 35 lines 21-22) Also see, (“the two predetermined ranges are respect proximities of a bottom bound and an upper bound of BLER. (paragraph 12 lines 1-6). If the BLER is lower, the communication is more reliable, which would be advantageous when limiting noise. Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to incorporate the MIMO methodology with suitable modulation scheme to achieve the spectral efficiency per layer (where one layer is carrying the data) to limit signal interference. It is essential that (“the base station can schedule proper data transmission for the terminals to fit channel capacity of each terminal, and maximize the cell capacity by adjusting the coding rate of channel decoder to control the capability of error protection, and by selecting the suitable modulation scheme to achieve the spectral efficiency.” (paragraph 4 lines 14-19). Regarding claim 15, as applied to claim 13. Chung teaches wherein the linear function is bounded by a lower bound and an upper bound (“predetermined ranges are respect proximities of a bottom bound and an upper bound of BLER” – Block Error Rate ) on a total spectral efficiency - (where the sum of all active layers is being considered) that is supported by the BS. This is done (“by selecting the suitable modulation scheme to achieve the spectral efficiency” (paragraph 4 lines 14-19). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to incorporate the MIMO methodology with suitable modulation scheme to achieve total spectral efficiency (where the sum of all active layers is being considered) to reduce noise. (“The two predetermined ranges are respect proximities of a bottom bound and an upper bound of BLER.” (paragraph 12 lines 1-6). If the BLER is lower, the communication is more reliable, which would be advantageous when limiting noise. Regarding claim 16, as applied to claim 11. Chung teaches selecting a particular linear function from a plurality of linear functions based on at least one of a number of UEs, a network load, or a power consumption. Chung teaches the processor ( “terminal 14 can include a memory (volatile or nonvolatile) which records codes, and a processor which executes the codes to implement the flow 100 and/or 200” (paragraph 89 lines 7-9). is further configured to select a particular linear function from a plurality of linear functions (“ the mapping of reported CQI and SIR is also a linear relation under a static channel” (paragraph 34 lines 10-11 based on at least one of a number of UEs, a network load, or a power consumption. This can be seen using (“power scheduling information of the base station ”) and also considering that “a s a terminal (UE) operates, it collects power scheduling information which reflects how a serving base station schedules power for different indicator levels CQI. By analyzing power scheduling information, If the terminal finds that the base station schedules additional power for a given indicator level CQI(i)” (see paragraph 16 lines 4-12) . Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to use metrics such as power scheduling information when attempting to connect to a base station. It is a lso clear that (“an efficient way to maximize the cell capacity is to allocate higher data rate to terminals with better channel qualities. (paragraph 4 lines 7- 9 ). Regarding claim 17, as applied to claim 11. Chung teaches selecting a particular linear function from a plurality of linear functions for a particular UE based on a traffic type or a quality of service (QoS) requirement . Chung teaches, the processor is further configured to select a particular linear function (“the mapping of reported CQI and SIR is also a linear relation under a static channel” (paragraph 34 lines 10-11) from a plurality of linear functions for a particular UE based on a traffic type or a quality of service (QoS) requirement. (“an objective of the invention is providing a method for reporting CQI by a transmitter of a communication system ” (paragraph 18 lines 1-3). Which contributes to QoS by collecting CQI metrics and further limiting noise. Also see (“the adaptation module 26 can be implemented by hardware, firmware and/or software. For example, the terminal 14 can include a memory (volatile or nonvolatile) which records codes, and a processor which executes the codes ” (paragraph 89 lines 2-9). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing the application to combine a processor and having it configured to use a linear function with UE based on traffic type or a quality of service (QoS) requirement. It is a clear that (“an efficient way to maximize the cell capacity is to allocate higher data rate to terminals with better channel qualities. (paragraph 4 lines 7-13). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to WALID MIA whose telephone number is (571)270-0783. The examiner can normally be reached Monday - Friday, 8 a.m. - 5 p.m. ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sam Ahn can be reached at (571) 272-3044. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /WALID MIA/Examiner, Art Unit 2633 /SAM K AHN/Supervisory Patent Examiner, Art Unit 2633 Application/Control Number: 19/051,071 Page 2 Art Unit: 2633 Application/Control Number: 19/051,071 Page 3 Art Unit: 2633 Application/Control Number: 19/051,071 Page 4 Art Unit: 2633 Application/Control Number: 19/051,071 Page 5 Art Unit: 2633 Application/Control Number: 19/051,071 Page 6 Art Unit: 2633 Application/Control Number: 19/051,071 Page 7 Art Unit: 2633 Application/Control Number: 19/051,071 Page 8 Art Unit: 2633 Application/Control Number: 19/051,071 Page 9 Art Unit: 2633 Application/Control Number: 19/051,071 Page 10 Art Unit: 2633 Application/Control Number: 19/051,071 Page 11 Art Unit: 2633 Application/Control Number: 19/051,071 Page 12 Art Unit: 2633 Application/Control Number: 19/051,071 Page 13 Art Unit: 2633 Application/Control Number: 19/051,071 Page 14 Art Unit: 2633 Application/Control Number: 19/051,071 Page 15 Art Unit: 2633 Application/Control Number: 19/051,071 Page 16 Art Unit: 2633 Application/Control Number: 19/051,071 Page 17 Art Unit: 2633
Prosecution Insights