Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see remark, filed 10/15/2025, with respect to the rejection(s) of claims 1-20 under 35 U.S.C. 103 as being unpatentable over Reider et al (US 2020/0186227) in view of Bai et al (US 2023/0353264) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of by Files et al (US 2022/0123797) (hereinafter Files).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 8 and 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Files et al (US 2022/0123797).
Regarding claim 1, Files discloses a base station (BS) (see Files, Fig. 1, p. [0018], e.g., an information handling system 100 can be a base station transceiver) comprising:
a transceiver configured to receive a set of input metrics (see Files, p. [0053], e.g., The information handling system 100 includes an antenna selection machine learning module 140, and p. [0054], e.g., the antenna selection machine learning module 140 receives as input the location, orientation, and configuration data of the information handling system 100); and
a processor operably coupled to the transceiver (e.g., base station transceiver), the processor configured to:
calculate, based on the set of input metrics, a transmit antenna selection (TAS) throughput prediction (see Files, p. [0054], e.g., the antenna selection machine learning module 140 may execute any machine learning classifier or other deep learning supervised learning system to provide a recommendation as to which of the antenna systems 132 to be used based on the location, orientation, and configuration data of the information handling system 100 and the historical antenna use profiles); and
generate, based on the TAS throughput prediction, a predicted TAS throughput result (see Files, p. [0054], e.g., the antenna selection machine learning module 140 may provide a prioritized list of antenna recommendations to be used to communicatively couple the information handling system to a network).
Regarding claim 8, Files discloses a method of operating a base station (BS) (see Files, Fig. 1, p. [0018], e.g., a base station transceiver), the method comprising: receiving a set of input metrics (see Files, p. [0053], e.g., The information handling system 100 includes an antenna selection machine learning module 140, and p. [0054], e.g., the antenna selection machine learning module 140 receives as input the location, orientation, and configuration data of the information handling system 100); calculating, based on the set of input metrics, a transmit antenna selection (TAS) throughput prediction (see Files, p. [0054], e.g., the antenna selection machine learning module 140 may execute any machine learning classifier or other deep learning supervised learning system to provide a recommendation as to which of the antenna systems 132 to be used based on the location, orientation, and configuration data of the information handling system 100 and the historical antenna use profiles); and generating, based on the TAS throughput prediction, a predicted TAS throughput result (see Files, p. [0054], e.g., the antenna selection machine learning module 140 may provide a prioritized list of antenna recommendations to be used to communicatively couple the information handling system to a network).
Regarding claim 15, Files discloses a non-transitory computer readable medium embodying a computer program, the computer program comprising program code that, when executed by a processor of a device (see Files, Fig. 1, p. [0018]), causes the device to: receive a set of input metrics (see Files, p. [0053], e.g., The information handling system 100 includes an antenna selection machine learning module 140, and p. [0054], e.g., the antenna selection machine learning module 140 receives as input the location, orientation, and configuration data of the information handling system 100); calculate, based on the set of input metrics, a transmit antenna selection (TAS) throughput prediction (see Files, p. [0054], e.g., the antenna selection machine learning module 140 may execute any machine learning classifier or other deep learning supervised learning system to provide a recommendation as to which of the antenna systems 132 to be used based on the location, orientation, and configuration data of the information handling system 100 and the historical antenna use profiles); and generate, based on the TAS throughput prediction, a predicted TAS throughput result (see Files, p. [0054], e.g., the antenna selection machine learning module 140 may provide a prioritized list of antenna recommendations to be used to communicatively couple the information handling system to a network).
Claim Rejections - 35 USC § 103
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.
Claims 2-7, 9-14, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Files in view of Reider et al (US 2020/0186227) (hereinafter Reider).
Regarding claim 2, Files discloses the base station of claim 1, wherein: the TAS throughput prediction is a single model throughput prediction (see Files, Fig. 1, p. [0054], e.g., the antenna selection machine learning module 140).
Files does not expressly disclose the base station of claim 1, wherein: the set of input metrics comprises at least one metric derived from a sounding reference signal (SRS), and the TAS throughput prediction is calculated based on an uplink (UL) signal to interference and noise ratio (SINR) value, a beamforming loss (BFloss), a first parameter identified from an offline model training procedure, and a second parameter identified from the offline model training procedure.
Reider discloses the above recited limitations (see Reider, p. [0075], p. [0093-0096], e.g., the training module then collects the channel quality measurements, p. [0094], e.g., the training module then sends the machine learning model update to the prediction module, and p. [0096], e.g., the prediction module then determines the best beam).
It would have been obvious to a person of ordinary skilled in the art before the effective filing date of the claimed invention to incorporate Reider’s teachings into Files. The suggestion/motivation would have been to optimize the selection among wireless antenna options on an information handling system connected to a network as suggested by Reider.
Regarding claim 3, the combined teaching of Files and Reider disclose the base station of claim 2, wherein: the set of input metrics further comprises at least one metric derived from a channel state information (CSI) report, and the TAS throughput prediction is calculated based on mapping a reported channel quality indicator (CQI) to a signal to interference and noise ratio (SINR) value (see Reider, Fig. 1, p. [0051], [0092], e.g., the 5G radio unit provides all channel quality measurements to the measurement collector 102).
Regarding claim 4, the combined teaching of Files and Reider disclose the base station of claim 1, wherein: the TAS throughput prediction is a multiple model throughput prediction (see Reider, p. [0031], e.g., the system is further configured to create different machine learning models, and Fig. 11, p. [0119], e.g., different ML models), and the set of input metrics comprises at least one metric derived from a sounding reference signal (SRS) and at least one metric derived from a channel state information (CSI) report (see Reider, p. [0050], e.g., channel quality measurements).
Regarding claim 5, the combined teaching of Files and Reider disclose the base station of claim 4, wherein the processor is further configured to select a TAS throughput prediction model based on a maximum uplink (UL) signal to noise ratio (SNR) across all receive (RX) ports, wherein the TAS throughput prediction is calculated based on the selected TAS throughput prediction model (see Reider, p. [0031], e.g., the system is further configured to create different machine learning models for different corresponding, respective cell sites of the radio access network, and select one of the machine learning models based on a location of the user equipment in one of the cell sites or a set of cells to predict the beam with the highest channel quality among the plurality of beams).
Regarding claim 6, the combined teaching of Files and Reider disclose the base station of claim 4, wherein the processor is further configured to select a TAS throughput prediction model based on an uplink (UL) signal to noise ratio (SNR) of a specific SRS port, wherein the TAS throughput prediction is calculated based on the selected TAS throughput prediction model (see Reider, p. [0031], e.g., the system is further configured to select one of the machine learning models based on a set of cells to predict the beam with the highest channel quality among the plurality of beams).
Regarding claim 7, the combined teaching of Files and Reider disclose the base station of claim 4, wherein the processor is further configured to select a TAS throughput prediction model based on a UE mobility profile, wherein the TAS throughput prediction is calculated based on the selected TAS throughput prediction model (see Reider, p. [0031], e.g., the system is further configured to select one of the machine learning models based on a location of the user equipment in one of the cell sites).
Regarding claim 9, the combined teaching of Files and Reider disclose the method of claim 8, wherein: the TAS throughput prediction is a single model throughput prediction (see Reider, p [0094], e.g., machine learning model), the set of input metrics comprises at least one metric derived from a sounding reference signal (SRS), and the TAS throughput prediction is calculated based on an uplink (UL) signal to interference and noise ratio (SINR) value, a beamforming loss (BFloss) (see Reider, p. [0075], p. [0093-0096], e.g., the training module then collects the channel quality measurements, p. [0094], e.g., the training module then sends the machine learning model update to the prediction module, and p. [0096], e.g., the prediction module then determines the best beam), a first parameter identified from an offline model training procedure, and a second parameter identified from the offline model training procedure.
Regarding claim 10, the combined teaching of Files and Reider disclose the method of claim 9, wherein: the set of input metrics further comprises at least one metric derived from a channel state information (CSI) report, and the TAS throughput prediction is calculated based on mapping a reported channel quality indicator (CQI) to a signal to interference and noise ratio (SINR) value (see Reider, Fig. 1, p. [0051], [0092], e.g., the 5G radio unit provides all channel quality measurements to the measurement collector 102).
Regarding claim 11, the combined teaching of Files and Reider disclose the method of claim 8, wherein: the TAS throughput prediction is a multiple model throughput prediction (see Reider, p. [0031], e.g., the system is further configured to create different machine learning models, and Fig. 11, p. [0119], e.g., different ML models), and the set of input metrics comprises at least one metric derived from a sounding reference signal (SRS) and at least one metric derived from a channel state information (CSI) report (see Reider, p. [0050], e.g., channel quality measurements).
Regarding claim 12, the combined teaching of Files and Reider disclose the method of claim 11, further comprising: selecting a TAS throughput prediction model based on a maximum uplink (UL) signal to noise ratio (SNR) across all receive (RX) ports, wherein the TAS throughput prediction is calculated based on the selected TAS throughput prediction model (see Reider, p. [0031], e.g., the system is further configured to create different machine learning models for different corresponding, respective cell sites of the radio access network, and select one of the machine learning models based on a location of the user equipment in one of the cell sites or a set of cells to predict the beam with the highest channel quality among the plurality of beams).
Regarding claim 13, the combined teaching of Files and Reider disclose the method of claim 11, further comprising: selecting a TAS throughput prediction model based on an uplink (UL) signal to noise ratio (SNR) of a specific SRS port, wherein the TAS throughput prediction is calculated based on the selected TAS throughput prediction model (see Reider, p. [0031], e.g., the system is further configured to select one of the machine learning models based on a set of cells to predict the beam with the highest channel quality among the plurality of beams).
Regarding claim 14, the combined teaching of Files and Reider disclose the method of claim 11, further comprising: selecting a TAS throughput prediction model based on a UE mobility profile, wherein the TAS throughput prediction is calculated based on the selected TAS throughput prediction model (see Reider, p. [0031], e.g., the system is further configured to select one of the machine learning models based on a location of the user equipment in one of the cell sites).
Regarding claim 16, the combined teaching of Files and Reider disclose the non-transitory computer readable medium of claim 15, wherein: the TAS throughput prediction is a single model throughput prediction (see Reider, p [0094], e.g., machine learning model), the set of input metrics comprises at least one metric derived from a sounding reference signal (SRS), and the TAS throughput prediction is calculated based on an uplink (UL) signal to interference and noise ratio (SINR) value, a beamforming loss (BFloss) (see Reider, p. [0075], p. [0093-0096], e.g., the training module then collects the channel quality measurements, p. [0094], e.g., the training module then sends the machine learning model update to the prediction module, and p. [0096], e.g., the prediction module then determines the best beam), a first parameter identified from an offline model training procedure, and a second parameter identified from the offline model training procedure.
Regarding claim 17, the combined teaching of Files and Reider disclose the non-transitory computer readable medium of claim 16, wherein: the set of input metrics further comprises at least one metric derived from a channel state information (CSI) report, and the TAS throughput prediction is calculated based on mapping a reported channel quality indicator (CQI) to a signal to interference and noise ratio (SINR) value (see Reider, Fig. 1, p. [0051], [0092], e.g., the 5G radio unit provides all channel quality measurements to the measurement collector 102).
Regarding claim 18, the combined teaching of Files and Reider disclose the non-transitory computer readable medium of claim 15, wherein: the TAS throughput prediction is a multiple model throughput prediction, the set of input metrics comprises at least one metric derived from a sounding reference signal (SRS) and at least one metric derived from a channel state information (CSI) (see Reider, p. [0031], e.g., the system is further configured to create different machine learning models for different corresponding, respective cell sites of the radio access network, and select one of the machine learning models based on a location of the user equipment in one of the cell sites or a set of cells to predict the beam with the highest channel quality among the plurality of beams), and the computer program further comprises program code that, when executed by the processor, causes the device to select a TAS throughput prediction model based on a maximum uplink (UL) signal to noise ratio (SNR) across all receive (RX) ports, wherein the TAS throughput prediction is calculated based on the selected TAS throughput prediction model (see Reider, p. [0031], e.g., the system is further configured to create different machine learning models for different corresponding, respective cell sites of the radio access network, and select one of the machine learning models based on a location of the user equipment in one of the cell sites or a set of cells to predict the beam with the highest channel quality among the plurality of beams).
Regarding claim 19, the combined teaching of Files and Reider disclose the non-transitory computer readable medium of claim 15, wherein: the TAS throughput prediction is a multiple model throughput prediction (see Reider, p. [0031], e.g., the system is further configured to create different machine learning models, and Fig. 11, p. [0119], e.g., different ML models), the set of input metrics comprises at least one metric derived from a sounding reference signal (SRS) and at least one metric derived from a channel state information (CSI), and the computer program further comprises program code that, when executed by the processor, causes the device to select a TAS throughput prediction model based on an uplink (UL) signal to noise ratio (SNR) of a specific SRS port, wherein the TAS throughput prediction is calculated based on the selected TAS throughput prediction model (see Reider, p. [0031], e.g., the system is further configured to create different machine learning models for different corresponding, respective cell sites of the radio access network, and select one of the machine learning models based on a location of the user equipment in one of the cell sites or a set of cells to predict the beam with the highest channel quality among the plurality of beams).
Regarding claim 20, the combined teaching of Files and Reider disclose the non-transitory computer readable medium of claim 15, wherein: the TAS throughput prediction is a multiple model throughput prediction (see Reider, p. [0031], e.g., the system is further configured to create different machine learning models, and Fig. 11, p. [0119], e.g., different ML models), the set of input metrics comprises at least one metric derived from a sounding reference signal (SRS) and at least one metric derived from a channel state information (CSI), and the computer program further comprises program code that, when executed by the processor, causes the device to selecting a TAS throughput prediction model based on a UE mobility profile, wherein the TAS throughput prediction is calculated based on the selected TAS throughput prediction model (see Reider, p. [0031], e.g., the system is further configured to select one of the machine learning models based on a location of the user equipment in one of the cell sites).
Conclusion
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/MINH TRANG T NGUYEN/Primary Examiner, Art Unit 2477