DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Response to Argument
Applicant's arguments filed 03/09/26 have been fully considered but they are not persuasive.
Applicant, on page 13 of the remark, argues that Hedge does not disclose at least "output[ing] a first message requesting a machine learning model corresponding to a spatial reuse procedure," as recited in independent claim 1. However, the Examiner respectfully disagrees.
Hegde discloses that the machine learning computing system 150 includes a plurality of machine learning models, and each machine learning model of the plurality of machine learning models is directed to a specific sub-area of a service area, a specific frequency band, and/or a specific operator (Fig. 1A and [0075].) Each respective machine learning model 152 is directed to a specific operator and uses only traffic data for that specific operator as an input. The machine learning model 152 determines a predicted radio resource usage based on the traffic data. The system is configured to determine whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse based on the predicted radio resource usage (please see paragraph 74). In other words, the machine learning computing system sends a request message including the time data and traffic data to a particular machine learning model for determining whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse (spatial reused procedure). For the reason above, the Examiner contends that the reference shows all limitations in the claim.
Applicant, on page 13 of the remark, argues that Hedge does not mention any "spatial reuse procedure" and thus Hedge cannot be fairly read to describe any "machine learning model corresponding to a spatial reuse procedure" let alone "output[ing] a first message requesting a machine learning model corresponding to a spatial reuse procedure," as recited in independent claim 1. Thus, Hedge does not disclose the aforementioned features of independent claim 1. However, the Examiner respectfully disagrees.
Hedge discloses that the machine learning computing system sends a request message including the time data and traffic data to a particular machine learning model for determining whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse (spatial reused procedure). For the reason above, the Examiner contends that the reference shows all limitations in the claim.
Applicant, on page 13 of the remark, argues that Hedge does not disclose "obtain[ing], a spatial reuse information request message" much less "output[ting], based at least in part on obtaining the spatial reuse information request message, a spatial reuse information response message," as recited in amended independent claim 30. For example, the machine learning computing system merely configured "to receive traffic data for the base station," as in Hedge, does not disclose any "outputting" of "a spatial reuse information response message" let alone outputting the response message "based at least in part on obtaining a spatial reuse information request message," as recited in amended independent claim 30. Moreover, receiving traffic data for the base station, as in Hedge, provides no indication of "obtain[ing], a spatial reuse information request message," as recited in amended independent claim 30. Thus, Hedge does not disclose the aforementioned features of amended independent claim 30. However, the Examiner respectfully disagrees.
As mentioned above, Hegde discloses that the machine learning computing system 150 includes a plurality of machine learning models, and each machine learning model of the plurality of machine learning models is directed to a specific sub-area of a service area, a specific frequency band, and/or a specific operator (Fig. 1A and [0075].) Each respective machine learning model 152 is directed to a specific operator and uses only traffic data for that specific operator as an input. The machine learning model 152 determines a predicted radio resource usage based on the traffic data. The system is configured to determine whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse based on the predicted radio resource usage (please see paragraph 74). In other words, when the specific operator determines whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse, it transmits the request message including the time data and traffic data to the machine learning computing system 150. The machine learning computing system 150 requests a particular machine learning model 152 for determining the predicted radio resource usage and downlink frequency reuse or uplink frequency reuse. After determining, the particular machine learning model 152 sends a result back to the machine learning computing system 150. For the reason above, the Examiner contends that the reference shows all limitations in the claim.
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 person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 7-9, 11-13, 16-21, 24-26, 28-34 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hegde (U.S. 20250063429).
For claim 1, Hegde discloses an apparatus for wireless communications at a device, comprising: at least one memory; and at least one processor communicatively coupled with the at least one memory, the at least one processor operable to cause the device to:
output a first message requesting a machine learning model corresponding to a spatial reuse procedure (at least Fig. 1A, [0038], [0042]-[0045], [0060], [0066] and [0075]. Multiple machine learning models 152 directed to specific subsets of the traffic data are utilized by the machine learning computing system 150. In some such examples, each respective machine learning model 152 is directed to a specific sub-area of the cell or a specific frequency band/band class used in the cell and uses only traffic data for that specific sub-area as an input. In other such examples, each respective machine learning model 152 is directed to a specific operator and uses only traffic data for that specific operator as an input.);
obtain, in accordance with outputting the first message, a second message indicating information corresponding to the machine learning model, the information comprising a model structure corresponding to the machine learning model, an identifier indicating the machine learning model, one or more inputs corresponding to the machine learning model, one or more outputs corresponding to the machine learning model, or any combination thereof (at least [0060] and [0066]. A machine learning computing system configured to: receive time data and traffic data; and determine a predicted radio resource usage of the base station based on the time data and the traffic data; wherein the system is configured to adjust operation of at least one radio unit of the one or more radio units based on the predicted radio resource usage of the base station.);
generate one or more spatial reuse parameter values corresponding to the one or more outputs according to the machine learning model and the one or more inputs (at least [0035] and [0061]. The predicted radio resource usage 156 output by the machine learning model 152 includes one or more target quantized signature vector (QSV) sets to be utilized by the base station to meet real-time needs of the network. The output of the machine learning model 152 is an integer that corresponds to a particular combination of groups of RUs to be used by the base station.); and
output one or more frames according to the spatial reuse procedure in accordance with the one or more spatial reuse parameter values (at least [029]-[0030], [0036], [0047], [0061], [0072] and [0074]. The method 400 includes adjusting the operation of one or more radio units (RUs) based on the predicted radio resource usage (block 406). In some examples, adjusting the operation of a RU based on the predicted radio resource usage includes turning the RU completely on or completely off. In some examples, adjusting the operation of a RU based on the predicted radio resource usage includes disabling one or more frequency bands or services utilized at the RU. The system is configured to determine whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse based on the predicted radio resource usage.)
For claim 2, Hegde discloses the apparatus of claim 1, wherein the at least one processor is further operable to cause the device to: obtain, from one or more neighbor devices, one or more values corresponding to the one or more inputs; and input the one or more values into the machine learning model, wherein generating the one or more spatial reuse parameter values is based at least in part on the inputting (at least [0028], [0034], [0037], [0059] and [0067]. The traffic data includes a number of UEs in the cell, traffic density in the cell, types of UEs (based on capability) in the cell, and/or identification of primary RUs used to communicate with UEs in the cell. The machine learning model determines the predicted radio resource usage based on the time data and the traffic data. The predicted radio resource usage 156 output by the machine learning model 152 includes one or more target quantized signature vector (QSV) sets to be utilized by the base station to meet real-time needs of the network. The output of the machine learning model 152 is an integer that corresponds to a particular combination of groups of RUs to be used by the base station).
For claim 3, Hegde discloses the apparatus of claim 1, wherein the at least one processor is further operable to cause the device to: obtain an indication of one or more values corresponding to the one or more inputs, the one or more values corresponding to one or more neighbor devices; and input the one or more values into the machine learning model, wherein generating the one or more spatial reuse parameter values is based at least in part on the inputting (at least [0028], [0034], [0037], [0059] and [0067]. The traffic data includes a number of UEs in the cell, traffic density in the cell, types of UEs (based on capability) in the cell, and/or identification of primary RUs used to communicate with UEs in the cell. The machine learning model determines the predicted radio resource usage based on the time data and the traffic data. The predicted radio resource usage 156 output by the machine learning model 152 includes one or more target quantized signature vector (QSV) sets to be utilized by the base station to meet real-time needs of the network. The output of the machine learning model 152 is an integer that corresponds to a particular combination of groups of RUs to be used by the base station.)
For claim 4, Hegde discloses the apparatus of claim 1, wherein the at least one processor is further operable to cause the device to: input one or more local values corresponding to the one or inputs into the machine learning model, wherein generating the one or more spatial reuse parameter values is based at least in part on the inputting (at least [0028], [0034], [0037], [0059] and [0067]. The traffic data includes a number of UEs in the cell, traffic density in the cell, types of UEs (based on capability) in the cell, and/or identification of primary RUs used to communicate with UEs in the cell. The machine learning model determines the predicted radio resource usage based on the time data and the traffic data. The predicted radio resource usage 156 output by the machine learning model 152 includes one or more target quantized signature vector (QSV) sets to be utilized by the base station to meet real-time needs of the network. The output of the machine learning model 152 is an integer that corresponds to a particular combination of groups of RUs to be used by the base station.)
For claim 7, Hegde discloses the apparatus of claim 1, wherein the at least one processor is further operable to cause the device to: obtain, from a second device, an indication of a time period during which the one or more outputs of the machine learning model are applicable to the spatial reuse procedure, wherein outputting the one or more frames occurs during the indicated time period (at least [0004] and [0028], [0034], [0037], [0059] and [0067]. The traffic data includes a number of UEs in the cell, traffic density in the cell, types of UEs (based on capability) in the cell, and/or identification of primary RUs used to communicate with UEs in the cell. The machine learning model determines the predicted radio resource usage based on the time data and the traffic data. The predicted radio resource usage 156 output by the machine learning model 152 includes one or more target quantized signature vector (QSV) sets to be utilized by the base station to meet real-time needs of the network. The output of the machine learning model 152 is an integer that corresponds to a particular combination of groups of RUs to be used by the base station).
For claim 8, Hegde discloses the apparatus of claim 1, wherein the at least one processor is further operable to cause the device to: compare the one or more spatial reuse parameter values with a set of spatial reuse parameter values that is different than the one or more spatial reuse parameter values; and select the one or more spatial reuse parameter values based on the comparing, wherein outputting the one or more frames is based at least in part on the selecting (at least [0039]-[0040]. The number of independent variables of the machine learning model 152 can be selected during training based on the desired level of accuracy and computational load demands for the machine learning model 152. In theory, a greater number of independent variables for the time data and the traffic data can provide a more accurate prediction of the radio resource usage of the base station assuming that the machine learning model 152 is sufficiently trained. However, the computational load demands and the time required for training increase when using a higher number of independent variables.)
For claim 9, Hegde discloses the apparatus of claim 1, wherein the at least one processor is further operable to cause the device to: obtain an indication of one or more threshold parameter values, wherein outputting the one or more frames is based at least in part on the one or more spatial reuse parameter values satisfying the one or more threshold parameter values (at least [0029]. The one or more components of the system are configured to power on at least one RU 106 that was previously powered off based on the predicted radio resource usage 156 of the base station. In some examples, the one or more components of the system are configured to power off at least one RU 106 based on the predicted radio resource usage 156 of the base station. In some examples, the one or more components are configured to adjust a minimum and/or maximum threshold signal-to-interference ratio or a minimum and/or maximum QSV based on the predicted radio resource usage 156.)
For claim 11, Hegde discloses the apparatus of claim 1, wherein the at least one processor is further operable to cause the device to: obtain an indication of a second machine learning model corresponding to the spatial reuse procedure, a first set of conditions associated with the machine learning model, and a second set of conditions associated with the second machine learning model; and select, for the spatial reuse procedure, the machine learning model based at least in part on one or more current conditions satisfying the one or more conditions associated with the machine learning model, wherein the generating is based at least in part on the selecting (at least [0039]-[0040] and [0075]. The machine learning computing system is configured to utilize the time data and the traffic data as inputs to a plurality of machine learning models, wherein each machine learning model of the plurality of machine learning models is directed to a specific sub-area of a service area, a specific frequency band, and/or a specific operator. The number of independent variables of the machine learning model 152 can be selected during training based on the desired level of accuracy and computational load demands for the machine learning model 152. In theory, a greater number of independent variables for the time data and the traffic data can provide a more accurate prediction of the radio resource usage of the base station assuming that the machine learning model 152 is sufficiently trained. However, the computational load demands and the time required for training increase when using a higher number of independent variables.)
For claim 12, Hegde discloses the apparatus of claim 1, wherein the one or more inputs comprise one or more local observations, one or more obtained input values, one or more system inferences, or any combination thereof (at least [0027]-[0028]. The machine learning computing system 150 also includes one or more interfaces 154 configured to receive traffic data for the base station. The one or more interfaces 154 configured to receive traffic data can be the same interface(s) 154 or different interface(s) 154 compared to the one or more interfaces 154 configured to receive time data. The traffic data can include, for example, a number of UEs in the cell, traffic density in the cell, types of UEs (based on capability) in the cell, and/or identification of primary RUs used to communicate with UEs in the cell.)
For claim 13, Hegde discloses the apparatus of claim 12, wherein the one or more local observations comprise a quantity of stations observed by the device satisfying a threshold signal strength, an average channel quality metric over a threshold time period, a distance to an access point, a distance to a threshold quantity of interfering outputting devices, an average interference level during a clear channel assessment, a success rate for previous transmissions at a candidate out of basic service set preamble detection level, a transmit power over a threshold time period, a quantity of interruptions during a threshold quantity of previous transmissions, or any combination thereof (at least [0028], [0038] and [0047]. The machine learning computing system 150 also includes one or more interfaces 154 configured to receive traffic data for the base station. The one or more interfaces 154 configured to receive traffic data can be the same interface(s) 154 or different interface(s) 154 compared to the one or more interfaces 154 configured to receive time data. The traffic data can include, for example, a number of UEs in the cell, traffic density in the cell, types of UEs (based on capability) in the cell, and/or identification of primary RUs used to communicate with UEs in the cell.)
For claim 16, Hegde discloses the apparatus of claim 1, wherein the one or more outputs comprise an overlapping basic service set preamble detection threshold level, a transmit power, a parametrized spatial reuse value, an action to be taken by the device, an estimated performance metric corresponding to a candidate spatial reuse parameter value or a candidate transmit power value, or any combination thereof (at least [0030]. The predicted radio resource usage 156 is output to a component of the system (for example, the BBU entity 102), and the component of the system generates and provides control signals to the RUs 106 for changing operation. For example, if the predicted radio resource usage 156 indicates that a particular RU 106 or group of RUs 106 will not be needed in the immediate future, then that particular RU 106 or group of RUs 106 is powered down or otherwise put into a low power mode.)
For claim 17, Hegde discloses the apparatus of claim 1, wherein the machine learning model is configured to process the one or more inputs according to the spatial reuse procedure and output the one or more outputs (at least [0030]. The machine learning computing system 150 is configured to provide control signals (for example, via controller 158) to the RUs 106 either directly or indirectly via a BBU entity 102. In other examples, the predicted radio resource usage 156 is output to a component of the system (for example, the BBU entity 102), and the component of the system generates and provides control signals to the RUs 106 for changing operation. For example, if the predicted radio resource usage 156 indicates that a particular RU 106 or group of RUs 106 will not be needed in the immediate future, then that particular RU 106 or group of RUs 106 is powered down or otherwise put into a low power mode.)
For claims 18-21, the claims have features similar to claims 1-3. Therefore, the claims are also rejected for the same reasons in claims 1-3.
For claims 24-26, the claims have features similar to claims 7-9. Therefore, the claims are also rejected for the same reasons in claims 7-9.
For claims 28-29, the claims have features similar to claims 11-12. Therefore, the claims are also rejected for the same reasons in claims 11-12.
For claim 30, Hedge discloses an apparatus for wireless communications at a device, comprising: at least one memory; and at least one processor communicatively coupled with the at least one memory, the at least one processor operable to cause the device to: obtain, a spatial reuse information request message; output, based at least in part on obtaining the spatial reuse information request message, a spatial reuse information response message; obtain, based at least in part on outputting a spatial reuse information response message, a first message comprising spatial reuse information corresponding to one or more inputs of a machine learning model associated with a spatial reuse procedure (Hegde discloses that the machine learning computing system 150 includes a plurality of machine learning models, and each machine learning model of the plurality of machine learning models is directed to a specific sub-area of a service area, a specific frequency band, and/or a specific operator (Fig. 1A and [0075].) Each respective machine learning model 152 is directed to a specific operator and uses only traffic data for that specific operator as an input. The machine learning model 152 determines a predicted radio resource usage based on the traffic data. The system is configured to determine whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse based on the predicted radio resource usage (please see paragraph 74). In other words, when the specific operator determines whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse, it transmits the request message including the time data and traffic data to the machine learning computing system 150. The machine learning computing system 150 requests a particular machine learning model 152 for determining the predicted radio resource usage and downlink frequency reuse or uplink frequency reuse. After determining, the particular machine learning model 152 sends a result back to the machine learning computing system 150.);
input the one or more inputs into the machine learning model based at least in part on the spatial reuse information (at least [0060] and [0066]. A machine learning computing system configured to: receive time data and traffic data; and determine a predicted radio resource usage of the base station based on the time data and the traffic data; wherein the system is configured to adjust operation of at least one radio unit of the one or more radio units based on the predicted radio resource usage of the base station.);
generate one or more spatial reuse parameter values corresponding to one or more outputs according to the machine learning model and the one or more inputs at least [0035] and [0061]. The predicted radio resource usage 156 output by the machine learning model 152 includes one or more target quantized signature vector (QSV) sets to be utilized by the base station to meet real-time needs of the network. The output of the machine learning model 152 is an integer that corresponds to a particular combination of groups of RUs to be used by the base station.); and
output one or more frames according to the spatial reuse procedure in accordance with the one or more spatial reuse parameter values (at least [029]-[0030], [0036], [0047], [0061], [0072] and [0074]. The method 400 includes adjusting the operation of one or more radio units (RUs) based on the predicted radio resource usage (block 406). In some examples, adjusting the operation of a RU based on the predicted radio resource usage includes turning the RU completely on or completely off. In some examples, adjusting the operation of a RU based on the predicted radio resource usage includes disabling one or more frequency bands or services utilized at the RU. The system is configured to determine whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse based on the predicted radio resource usage.)
31. (Currently Amended) The apparatus of claim 30, wherein the spatial reuse information request message, a the spatial reuse information response message comprises one or more local observations, one or more local measurements, or a combination thereof (Hegde discloses that the machine learning computing system 150 includes a plurality of machine learning models, and each machine learning model of the plurality of machine learning models is directed to a specific sub-area of a service area, a specific frequency band, and/or a specific operator (Fig. 1A and [0075].) Each respective machine learning model 152 is directed to a specific operator and uses only traffic data for that specific operator as an input. The machine learning model 152 determines a predicted radio resource usage based on the traffic data. The system is configured to determine whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse based on the predicted radio resource usage (please see paragraph 74). In other words, when the specific operator determines whether or not to utilize a radio unit for downlink frequency reuse or uplink frequency reuse, it transmits the request message including the time data and traffic data to the machine learning computing system 150. The machine learning computing system 150 requests a particular machine learning model 152 for determining the predicted radio resource usage and downlink frequency reuse or uplink frequency reuse. After determining, the particular machine learning model 152 sends a result back to the machine learning computing system 150.)
For claims 32-33, the claims have features similar to claims 30-31. Therefore, the claims are also rejected for the same reasons in claims 30-31.
For claim 34, Hegde discloses the apparatus of claim 32, wherein the at least one processor is further operable to cause the device to: output, to a second device, a second spatial reuse information request message; and obtain, from the second device based at least in part on outputting the spatial reuse information request message, a second spatial reuse information response message comprising one or more local observations associated with a plurality of stations associated with the second device, one or more local measurements associated with the plurality of stations associated with the second device, or a combination thereof, wherein outputting the message comprising the spatial reuse information is based at least in part on obtaining the second spatial reuse information response message (at least [0028], [0034], [0037], [0059] and [0067]. The method 400 begins with receiving time data and traffic data (block 402). In some examples, the time data includes the current time of day, the current day of the week, and/or whether the current day is a holiday. In some examples, the traffic data includes a number of UEs in the cell, traffic density in the cell, types of UEs (based on capability) in the cell, and/or identification of primary RUs used to communicate with UEs in the cell.)
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 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.
Claims 5 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Hegde (U.S. 20250063429). in view of Wang et al. (U.S. 20220353803).
For claim 5, Hegde does not disclose the apparatus of claim 1, wherein the at least one processor is further operable to cause the device to: obtain a third message indicating an availability of the machine learning model corresponding to the spatial reuse procedure, wherein outputting the first message is based at least in part on obtaining the third message.
In the same field of endeavor, Wang et al. disclose obtain a third message indicating an availability of the machine learning model corresponding to the spatial reuse procedure, wherein outputting the first message is based at least in part on obtaining the third message (at least [0079]. The machine-learning architecture selection rules 608 include instructions that enable the UE 110 to derive a requested quality-of-service level from given performance requirements of the application. The machine learning architecture selection rules 608 can also include instructions enabling the UE 110, the base station 120, or the entity of the 5GC 150 to select an appropriate machine-learning architecture based on the available machine-learning architectures 606.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Hegde as taught by Wang et al. for purpose of a providing quality-of-service level requested by an application.
For claim 22, the claim has features similar to claim 5. Therefore, the claim is also rejected for the same reasons in claim 5.
Claims 6 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Hegde (U.S. 20250063429) in view of Lo et al. (U.S. 20220407745) and further in view of Bao et al. (U.S. 20210243633).
For claim 6, Hegde does not disclose the apparatus of claim 1, wherein the at least one processor is further operable to cause the device to: obtain a third message comprising an instruction to disable the machine learning model; disable the machine learning model based at least in part on obtaining the third message; and output one or more additional frames according to a second spatial reuse procedure based at least in part on a set of spatial reuse parameters that is different than the one or more spatial reuse parameter values.
In the same field of endeavor, Lo et al. disclose obtain a third message comprising an instruction to disable the machine learning model; disable the machine learning model based at least in part on obtaining the third message (at least [0006]. Configuration information from the base station indicates one or more of enabling or disabling of machine learning adaptation of the reference signal pattern, a machine learning model used for machine learning adaptation of the reference signal pattern, updated model parameters for the machine learning model, or whether model parameters received from the user equipment will be used for machine learning adaptation of the reference signal pattern.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Hegde as taught by Lo et al. for purpose of enabling or disabling of ML adaptation of the RS pattern.
In the same field of endeavor, Bao et al. disclose output one or more additional frames according to a second spatial reuse procedure based at least in part on a set of spatial reuse parameters that is different than the one or more spatial reuse parameter values (at least [0155]. The base station 505 may determine an updated customized function block based on the additional parameter reported by the UE 515. At 570, the base station 505 may transmit the updated customized function block to the UE 515).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Hegde as taught by Bao et al. for purpose of processing one or more signals at the UE based on the received customized function block.
For claim 23, the claim has features similar to claim 6. Therefore, the claim is also rejected for the same reasons in claim 6.
Claims 10 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Hegde (U.S. 20250063429) in view of Thomas et al. (U.S. 20220272707).
For claim 10, Hegde does not disclose the apparatus of claim 1, wherein the at least one processor is further operable to cause the device to: obtain control signaling that enables tuning of the machine learning model; tune one or more parameters of the machine learning model to generate an updated machine learning model according to local data generated by the device or obtained from one or more neighbor devices based at least in part on the control signaling that enables the tuning; and generate one or more additional spatial reuse parameter values corresponding to one or more outputs of the updated machine learning model.
In the same field of endeavor, Thomas et al. disclose the at least one processor is further operable to cause the device to: obtain control signaling that enables tuning of the machine learning model; tune one or more parameters of the machine learning model to generate an updated machine learning model according to local data generated by the device or obtained from one or more neighbor devices based at least in part on the control signaling that enables the tuning; and generate one or more additional spatial reuse parameter values corresponding to one or more outputs of the updated machine learning model (at least [0018]. Base station 110 may provide the uplink data and the tuning factors as an input to the machine learning model. The machine learning model may process the uplink data and the tuning factors to generate an output. The output may indicate the total score and a confidence score that reflects a measure of confidence that the total score is accurate.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Hegde as taught by Thomas et al. for purpose of a increasing accuracy.
For claim 27, the claim has features similar to claim 10. Therefore, the claim is also rejected for the same reasons in claim 20.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hegde (U.S. 20250063429) in view of Kucera et al. (U.S. 11477666).
For claim 14, Hegde does not disclose the apparatus of claim 1, wherein the one or more obtained input values comprise an observed interference power corresponding to a reference basic service set, a quantity of errors to stations corresponding to the reference basic service set, a distance to a reference access point, a binary indication of whether a reference basic service set is generating interference, or any combination thereof.
In the same field of endeavor, Kucera et al. disclose the one or more obtained input values comprise an observed interference power corresponding to a reference basic service set, a quantity of errors to stations corresponding to the reference basic service set, a distance to a reference access point, a binary indication of whether a reference basic service set is generating interference, or any combination thereof (at least claim 1 and 12. A trained network controller of a heterogeneous network, the trained network controller including, a memory storing computer-readable instructions, at least one processor configured to execute the computer-readable instructions to, obtain input network measurements comprising at least one of channel gains, measurements of received power, measurements of interference, power-related information, an available bandwidth, connectivity, user equipment (UE) and base station associations, quality of service (QoS) parameters, congestion and load information, and generate output network parameters based on the input network measurements; wherein the network controller comprises a Deep Feed Forward neural network comprising a plurality of individual Feed-Forward neural networks each of which receives the input network measurements and outputs an individual bit of output data, wherein said individual bit is a schedulable element representing an actual resource element in an available signal bandwidth.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Hegde as taught by Kucera et al. for purpose of improving quality of service to the UE.
For claim 15, Hegde does not disclose the apparatus of claim 1, wherein the one or more system inferences comprise a candidate transmit power, a candidate parametrized spatial reuse value, a candidate overlapping basic service set preamble detection threshold level, a candidate action to be taken by the device, or any combination thereof.
In the same field of endeavor, Kucera et al. disclose the one or more system inferences comprise a candidate transmit power, a candidate parametrized spatial reuse value, a candidate overlapping basic service set preamble detection threshold level, a candidate action to be taken by the device, or any combination thereof. (at least claim 1 and 12. A trained network controller of a heterogeneous network, the trained network controller including, a memory storing computer-readable instructions, at least one processor configured to execute the computer-readable instructions to, obtain input network measurements comprising at least one of channel gains, measurements of received power, measurements of interference, power-related information, an available bandwidth, connectivity, user equipment (UE) and base station associations, quality of service (QoS) parameters, congestion and load information, and generate output network parameters based on the input network measurements; wherein the network controller comprises a Deep Feed Forward neural network comprising a plurality of individual Feed-Forward neural networks each of which receives the input network measurements and outputs an individual bit of output data, wherein said individual bit is a schedulable element representing an actual resource element in an available signal bandwidth.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Hegde as taught by Kucera et al. for purpose of improving quality of service to the UE.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAI PHUONG whose telephone number is 571-272-7896. The examiner can normally be reached on Monday-Friday, 8am-5pm.
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, Kathy Wang-Hurst can be reached on 571-270-5371. The fax phone number for the organization where this application or proceeding is assigned is 571-273-7687.
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/DAI PHUONG/Primary Examiner, Art Unit 2644
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