DETAILED ACTION
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN 2022089457, filed on 04/27/2022.
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 1-3, 5-7, 16-22, 24, 25, 27, 28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et. al (US 20210326726 A1), hereinafter Wang.
Regarding claim 1, Wang teaches,
An apparatus for wireless communication at a user equipment (UE), comprising:
at least one processor; and (Figure 3, label 302, paragraph 0076)
memory coupled to the at least one processor, the memory storing instructions executable by the at least one processor to cause the UE to: (Figure 3, label 304, paragraph 0076)
receive control signaling indicating a configuration for the UE to perform a machine learning-based inference for a characteristic of at least one resource, at least one communication beam, or at least one communication channel, wherein the characteristic is associated with one or more of a spatial domain, a time domain, or a frequency domain; (Figure 7, labels 722, 724, 726, paragraphs 0122-0124 – The UE receives a request to measure signals including RSRP using a machine learning-based network.
perform the machine learning-based inference for the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel in accordance with the configuration; (Figure 7, labels 722, 724, 726 paragraphs 0122-0124 – The UE performs measurements on the reference signals with the machine learning-based network).
obtain a measurement of the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel; and (Figure 7, labels 722, 724, 726 paragraphs 0122-0124 – The UE performs measurements on the reference signals including the RSRP).
transmit, in accordance with a triggering condition, an indication of a difference between the machine learning-based inference and the measurement of the characteristic for the at least one resource, the at least one communication beam, or the at least one communication channel. (Figure 7, label 728, paragraph 0125 – The UE transmits a report containing the prediction error (difference) between the data measured at the UE and the output of the machine learning-based network).
Regarding claim 2, Wang teaches,
The apparatus of claim 1, wherein to perform the machine learning-based inference for the characteristic and to obtain the measurement of the characteristic, the instructions are further executable by the at least one processor to cause the UE to:
identify, based at least in part on the machine learning-based inference, a first set of identifiers corresponding to one or more resources having a highest predicted measurement of the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel; and (paragraph 0072-0074 – The UE identifies a best performing SSB mean using the machine learning-based network from the first set of data.)
identify, based at least in part on obtaining the measurement of the characteristic, a second set of identifiers corresponding to one or more resources having a highest actual predicted measurement of the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel. (Paragraph 0073, 0074 – From time to time, the UE can submit additional data to determine both input and output data for the model, the output data including a prediction of a best predicted performing SSB beam which may be different from the previous prediction.
Regarding claim 3, Wang teaches,
The apparatus of claim 2, wherein to transmit the indication of a difference between the machine learning-based inference and the measurement of the characteristic, the instructions are further executable by the at least one processor to cause the UE to:
transmit one or more of:
the first set of identifiers, the second set of identifiers, an indication of a difference between the first set of identifiers and the second set of identifiers, or any combination thereof. (paragraph 0073 – The UE transmits feedback pair as sampled data to the BS.)
Regarding claim 5, Wang teaches,
The apparatus of claim 1, wherein the instructions to perform the machine learning-based inference for the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel are executable by the at least one processor to cause the UE to:
apply a machine learning model to at least one historic value of the characteristic to predict at least one later value of the characteristic, wherein the machine learning-based inference comprises the predicted at least one later value of the characteristic. (paragraph 0072 – The input to the machine learning can be 10 previous signal measurements and the measured signal strength in order to make a prediction of the best performing transmission beam).
Regarding claim 6, Wang teaches,
The apparatus of claim 5, wherein the instructions to receive the control signaling are executable by the at least one processor to cause the UE to:
receive an instruction to report the at least one historic value of the characteristic and the predicted at least one later value of the characteristic. (Paragraph 0072, covering historic values transmitted from the UE to the base station, Paragraph 0071– “The UEs 215 may be adapted to collect channel measurements with the UE PHY layer, pack the channel measurement data in the UE application layer, and communicate the channel measurement data with BS-side application layer of the BSs 205 and the server-side application layer of the cloud server 260. The BS-side application layer of the BSs 205 may be adapted to receive the channel measurement data from the UEs 215, pass the channel measurement data as input to one or more neural networks operating at one or more of the BSs 205 or the cloud server 260, forward propagate the neural network, pass the output of the neural network for a beam selection model, change one or more parameters of the beam selection model, as well as receiving neural network parameter updates from the cloud server 260.” Paragraph 0073 – The UE gathers data for additional later values of the signal strength to be considered a later value of the characteristic.)
Regarding claim 7, Wang teaches,
The apparatus of claim 6, wherein the instructions to transmit the indication of the difference between the machine learning-based inference and the measurement of the characteristic are executable by the at least one processor to cause the UE to:
transmit the at least one historic value of the characteristic and the predicted at least one later value of the characteristic in accordance with the received instruction. (Paragraph 0072, covering historic values transmitted from the UE to the base station, Paragraph 0071– “The UEs 215 may be adapted to collect channel measurements with the UE PHY layer, pack the channel measurement data in the UE application layer, and communicate the channel measurement data with BS-side application layer of the BSs 205 and the server-side application layer of the cloud server 260. The BS-side application layer of the BSs 205 may be adapted to receive the channel measurement data from the UEs 215, pass the channel measurement data as input to one or more neural networks operating at one or more of the BSs 205 or the cloud server 260, forward propagate the neural network, pass the output of the neural network for a beam selection model, change one or more parameters of the beam selection model, as well as receiving neural network parameter updates from the cloud server 260.” Paragraph 0072 – “The input to the neural network can be the previous 10 signal measurements of the BS 205a transmission beam monitored and the corresponding measured signal strength (e.g., RSRP) of the SSB. The output of the neural network may include a prediction of the best performing transmission beam from the BS 205a.”)
Regarding claim 16, Wang teaches,
The apparatus of claim 1, wherein the instructions are further executable by the at least one processor to cause the UE to:
receive an indication of the triggering condition. (paragraph 0037 – The UE receives a predetermined threshold from the base station).
Regarding claim 17, Wang teaches,
The apparatus of claim 1, wherein the triggering condition occurs when the difference between the machine learning-based inference and the measurement of the characteristic satisfies a threshold. (paragraph 0037 – The UE receives a predetermined threshold from the base station that indicates whether the output of the machine learning model corresponds to an error if it does not meet a threshold compared to the measurement).
Regarding claim 18, Wang teaches,
The apparatus of claim 1, wherein the instructions are further executable by the at least one processor to cause the UE to:
transmit a capability report indicating one or more of a capability of the UE to perform the machine learning-based inference, a capability of the UE to perform a measurement of the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel, or a capability of the UE to transmit the indication of the difference between the machine learning-based inference and the measurement of the characteristic. (paragraph 0039 – The transceiver is configured to report the difference between the machine learning-based inference and the measurement of the characteristic.)
Regarding claim 19, Wang teaches,
The apparatus of claim 1, wherein the characteristic comprises one or more of a signal strength associate with the at least one communication channel or the at least one communication beam, a change in signal strength associated with the at least one communication channel or the at least one communication beam, an explicit channel characteristic associated with the at least one resource, the at least one communication beam, or the at least one communication channel, an angular characteristic associated with the at least one resource, the at least one communication beam, or the at least one communication channel, a location of the UE during communication over the at least one resource, the at least one communication beam, or the at least one communication channel, a set of one or more UE receive beams used to communicate over the at least one resource, the at least one communication beam, or the at least one communication channel, a bandwidth part identifier associated with communicating over the at least one resource, the at least one communication beam, or the at least one communication channel, a serving cell identifier associated with communicating over the at least one resource, the at least one communication beam, or the at least one communication channel, a central frequency associated with communicating over the at least one resource, the at least one communication beam, or the at least one communication channel, or a numerology associated with communicating over the at least one resource, the at least one communication beam, or the at least one communication channel. (paragraph 0072 – The report includes historical signal strength along with the 10 signal measurements.)
Regarding claim 20, Wang teaches,
The apparatus of claim 1, wherein the characteristic is defined with respect to a set of one or more reference signal resource sets or one or more synchronization block resource sets. (paragraph 0081 – In addition to the downlink signal containing a RSRP value it may include a synchronization block (SSB). Figure 7, label 712, Paragraph 0103 – The first configuration may include SSB configured for the UE.)
Regarding claim 21, Wang teaches,
The apparatus of claim 1, wherein the indication of the difference between the machine learning-based inference and the measurement of the characteristic is transmitted via one or more of an application layer protocol, a radio resource control layer, or a medium access control layer, the indication comprising physical layer information associated with the machine learning-based inference. (paragraph 0039 – “In some instances, the transceiver configured to receive the request may be further configured to receive the request in a RRC signal.” Paragraph 0031 – discussion of interaction with physical layer and other information).
Regarding claim 22, Wang teaches,
The apparatus of claim 1, wherein the indication of the difference between the machine learning-based inference and the measurement of the characteristic is transmitted in a channel state information report via a physical layer uplink control information transmission. (paragraph 0116 – “In some aspects, the BS 610 may request to receive sampled data that corresponds exclusively to the prediction error. In some examples, the report may include at least one of RSRP or CQI. In some aspects, the report is multiplexed between at least one of RSRP or CQI or other UL control information (UCI) when configured PUCCH resources of the RSRP, or CQI, or the other UCI overlap. In some aspects, the BS 620 transmits a CSI report.”
Regarding claim 24, Wang teaches,
An apparatus for wireless communication at a network entity, comprising:
at least one processor; and (Figure 4, label 402, paragraph 0091)
memory coupled with the at least one processor, the memory storing instructions executable by the at least one processor to cause the network entity to: (Figure 4, label 404, paragraph 0091)
transmit control signaling indicating a configuration for a user equipment (UE) to perform a machine learning-based inference for a characteristic of at least one resource, at least one communication beam, or at least one communication channel; and (Figure 7, labels 722, 724, 726, paragraphs 0122-0124 – The UE receives a request to measure signals including RSRP with a machine learning-based network from the base station.
receive, in accordance with a triggering condition, an indication of a difference between the machine learning-based inference and a measurement of the characteristic for the at least one resource, the at least one communication beam, or the at least one communication channel at the UE. (Figure 7, label 728, paragraph 0125 – The UE transmits a report to the base station containing the prediction error (difference) between the data measured at the UE and the output of the machine learning-based network).
Regarding claim 25, Wang teaches,
The apparatus of claim 24, wherein the indication of the difference between the machine learning-based inference and the measurement of the characteristic comprises one or more of:
a first set of identifiers corresponding to one or more resources having a highest predicted measurement of the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel, a second set of identifiers corresponding to one or more resources having a highest actual predicted measurement of the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel, an indication of a difference between the first set of identifiers and the second set of identifiers, or any combination thereof. (paragraph 0072-0074 – The UE identifies a best performing SSB mean using the machine learning-based network from the first set of data. Paragraph 0073, 0074 – From time to time, the UE can submit additional data to determine both input and output data for the model, the output data including a prediction of a best predicted performing SSB beam which may be different from the previous prediction.)
Regarding claim 27, Wang teaches,
The apparatus of claim 24, wherein the machine learning-based inference comprises at least one predicted later value of the characteristic based at least in part on at least one historic value of the characteristic, and wherein the instructions to transmit the control signaling are executable by the at least one processor to cause the network entity to:
transmit an instruction to report the at least one historic value of the characteristic and the predicted at least one later value of the characteristic. (Paragraph 0072, covering historic values transmitted from the UE to the base station, Paragraph 0071– “The UEs 215 may be adapted to collect channel measurements with the UE PHY layer, pack the channel measurement data in the UE application layer, and communicate the channel measurement data with BS-side application layer of the BSs 205 and the server-side application layer of the cloud server 260. The BS-side application layer of the BSs 205 may be adapted to receive the channel measurement data from the UEs 215, pass the channel measurement data as input to one or more neural networks operating at one or more of the BSs 205 or the cloud server 260, forward propagate the neural network, pass the output of the neural network for a beam selection model, change one or more parameters of the beam selection model, as well as receiving neural network parameter updates from the cloud server 260.” Paragraph 0073 – The UE gathers data for additional later values of the signal strength to be considered a later value of the characteristic.)
Regarding claim 28, Wang teaches,
The apparatus of claim 27, wherein the instructions to receive the indication of the difference between the machine learning-based inference and the measurement of the characteristic are executable by the at least one processor to cause the network entity to:
receive the at least one historic value of the characteristic and the predicted at least one later value of the characteristic in accordance with the transmitted instruction. (Paragraph 0072, covering historic values transmitted from the UE to the base station, Paragraph 0071– “The UEs 215 may be adapted to collect channel measurements with the UE PHY layer, pack the channel measurement data in the UE application layer, and communicate the channel measurement data with BS-side application layer of the BSs 205 and the server-side application layer of the cloud server 260. The BS-side application layer of the BSs 205 may be adapted to receive the channel measurement data from the UEs 215, pass the channel measurement data as input to one or more neural networks operating at one or more of the BSs 205 or the cloud server 260, forward propagate the neural network, pass the output of the neural network for a beam selection model, change one or more parameters of the beam selection model, as well as receiving neural network parameter updates from the cloud server 260.” Paragraph 0072 – “The input to the neural network can be the previous 10 signal measurements of the BS 205a transmission beam monitored and the corresponding measured signal strength (e.g., RSRP) of the SSB. The output of the neural network may include a prediction of the best performing transmission beam from the BS 205a.”)
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.
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.
Claims 4, 26 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Zhou et al. (US 20160014778 A1), hereinafter Zhou.
Regarding claim 4, Wang fails to teach,
The apparatus of claim 1, wherein to obtain the measurement of the characteristic, the instructions are further executable by the at least one processor to cause the UE to:
obtain a virtual measurement of a virtual resource associated with the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel, wherein the virtual resource is a non-transmitted resource.
Zhou teaches,
The apparatus of claim 1, wherein to obtain the measurement of the characteristic, the instructions are further executable by the at least one processor to cause the UE to:
obtain a virtual measurement of a virtual resource associated with the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel, wherein the virtual resource is a non-transmitted resource. (paragraph 0030, paragraph 0362, figure 2, labels 201, 202, 203 – The UE performs virtual measurements of a virtual bandwidth that includes measurements of RSRP to include a system information block and synchronization signal.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the virtual measurement teachings of Zhou. The purpose of doing so is to reduce signal overhead and complexity (paragraphs 0003, 0004 of Zhou).
Regarding claim 26, Wang fails to teach,
The apparatus of claim 24, wherein the measurement of the characteristic is a virtual measurement of a virtual resource associated with the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel, wherein the virtual resource is a non-transmitted resource.
Zhou teaches,
The apparatus of claim 24, wherein the measurement of the characteristic is a virtual measurement of a virtual resource associated with the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel, wherein the virtual resource is a non-transmitted resource. (paragraph 0030, paragraph 0362, figure 2, labels 201, 202, 203 – The UE performs virtual measurements of a virtual bandwidth that includes measurements of RSRP to include a system information block and synchronization signal.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the virtual measurement teachings of Zhou. The purpose of doing so is to reduce signal overhead and complexity (paragraphs 0003, 0004 of Zhou).
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Lunardi et al. (US 20250119482 A1), hereinafter Lunardi.
Regarding claim 8, Wang fails to teach,
The apparatus of claim 5, wherein the at least one historic value of the characteristic comprises a time series of a plurality of historic values of the characteristic.
Lunardi teaches,
The apparatus of claim 5, wherein the at least one historic value of the characteristic comprises a time series of a plurality of historic values of the characteristic. (paragraphs 0026-0031 – The historic data consists historical data associated with an AI/ML model that may include quality of service data associated with a UE, radio measurements, load metrics or other characteristics and areas of interest of a UE. Paragraph 0026 – The data contains an indication of the time period of collection of the data.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the historical data teachings of Lunardi. The purpose of doing so is to provide training and inference data for the model to use (paragraphs 0022-0024).
Regarding claim 9, Wang fails to teach,
The apparatus of claim 8, wherein the instructions are further executable by the at least one processor to cause the UE to:
receive an indication of a length of the time series of the plurality of historic values of the characteristic.
Lunardi teaches,
The apparatus of claim 8, wherein the instructions are further executable by the at least one processor to cause the UE to:
receive an indication of a length of the time series of the plurality of historic values of the characteristic. (Paragraph 0026 – The data contains an indication of the time period of collection of the data and size of the data.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the historical data teachings of Lunardi. The purpose of doing so is to provide training and inference data for the model to use (paragraphs 0022-0024).
Regarding claim 10, Wang fails to teach,
The apparatus of claim 8, wherein the instructions to transmit the indication of the difference between the machine learning-based inference and the measurement of the characteristic are executable by the at least one processor to cause the UE to:
transmit an indication of a length of the time series of the plurality of historic values of the characteristic.
Lunardi teaches,
The apparatus of claim 8, wherein the instructions to transmit the indication of the difference between the machine learning-based inference and the measurement of the characteristic are executable by the at least one processor to cause the UE to:
transmit an indication of a length of the time series of the plurality of historic values of the characteristic. (Paragraph 0026 – The data contains an indication of the time period of collection of the data. This data is indicated from a first network to a second network. Paragraph 0031 – The criteria for sending the historical data containing the measurements).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the historical data teachings of Lunardi. The purpose of doing so is to provide training and inference data for the model to use (paragraphs 0022-0024).
Claims 11-15, 29, 30 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Bai et al. (US 20220124634 A1), hereinafter Bai.
Regarding claim 11, Wang fails to teach,
The apparatus of claim 1, wherein the instructions to perform the machine learning-based inference for the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel are executable by the at least one processor to cause the UE to:
apply a machine learning model to at least one first value of the characteristic associated with a first set one or more reference signal resource identifiers or synchronization signal block resource identifiers to predict at least one second value of the characteristic associated with a second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers, wherein the machine learning-based inference comprises the predicted at least one second value of the characteristic.
Bai teaches,
The apparatus of claim 1, wherein the instructions to perform the machine learning-based inference for the characteristic of the at least one resource, the at least one communication beam, or the at least one communication channel are executable by the at least one processor to cause the UE to:
apply a machine learning model to at least one first value of the characteristic associated with a first set one or more reference signal resource identifiers or synchronization signal block resource identifiers to predict at least one second value of the characteristic associated with a second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers, wherein the machine learning-based inference comprises the predicted at least one second value of the characteristic. (paragraph 0035 – The measurement of the first beam is used to predict a pathloss of a second beam which can be implemented with a machine learning model and can be a RSRP).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the measuring and transmission of a second signal teachings of Bai. The purpose of doing so is to reduce the number of measurements by not measuring all beams and instead choosing a first beam to predict a pathloss of a second beam (paragraph 0034).
Regarding claim 12, Wang fails to teach,
The apparatus of claim 11, wherein the first set of some or more reference signal resource identifiers or synchronization signal block resource identifiers is spatially different than the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers.
Bai teaches,
The apparatus of claim 11, wherein the first set of some or more reference signal resource identifiers or synchronization signal block resource identifiers is spatially different than the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers. (paragraph 0035 – The first beam can be associated with a different PCI or TCI state than the second beam).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the measuring and transmission of a second signal teachings of Bai. The purpose of doing so is to reduce the number of measurements by not measuring all beams and instead choosing a first beam to predict a pathloss of a second beam (paragraph 0034).
Regarding claim 13, Wang fails to teach,
The apparatus of claim 11, wherein the first set of one or more reference signal resource identifiers or synchronization signal block resource identifiers and the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers are associated with different bandwidth parts or serving cells.
The apparatus of claim 11, wherein the first set of one or more reference signal resource identifiers or synchronization signal block resource identifiers and the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers are associated with different bandwidth parts or serving cells.
Bai teaches,
The apparatus of claim 11, wherein the first set of one or more reference signal resource identifiers or synchronization signal block resource identifiers and the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers are associated with different bandwidth parts or serving cells.
The apparatus of claim 11, wherein the first set of one or more reference signal resource identifiers or synchronization signal block resource identifiers and the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers are associated with different bandwidth parts or serving cells. (paragraph 0035 – The first beam can be associated with a different PCI or TCI state than the second beam).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the measuring and transmission of a second signal teachings of Bai. The purpose of doing so is to reduce the number of measurements by not measuring all beams and instead choosing a first beam to predict a pathloss of a second beam (paragraph 0034).
Regarding claim 14, Wang fails to teach,
The apparatus of claim 11, wherein the instructions to receive the control signaling are executable by the at least one processor to cause the UE to:
receive an instruction to report the at least one first value of the characteristic associated with the first set one or more reference signal resource identifiers or synchronization signal block resource identifiers and the at least one second value of the characteristic associated with the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers.
Bai teaches,
The apparatus of claim 11, wherein the instructions to receive the control signaling are executable by the at least one processor to cause the UE to:
receive an instruction to report the at least one first value of the characteristic associated with the first set one or more reference signal resource identifiers or synchronization signal block resource identifiers and the at least one second value of the characteristic associated with the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers. (Figure 7, labels t1, t4, paragraph 0094 – The UE receives a request for a pathloss measurement associated with a first beam. Paragraph 0112 – In response to a threshold based on the measurement of the first beam the UE receives an update requesting the transmission of a second transmission pathloss.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the measuring and transmission of a second signal teachings of Bai. The purpose of doing so is to reduce the number of measurements by not measuring all beams and instead choosing a first beam to predict a pathloss of a second beam (paragraph 0034).
Regarding claim 15, Wang fails to teach,
The apparatus of claim 14, wherein the instructions to transmit the indication of the difference between the machine learning-based inference and the measurement of the characteristic are executable by the at least one processor to cause the UE to:
transmit the at least one first value of the characteristic associated with the first set one or more reference signal resource identifiers or synchronization signal block resource identifiers and the at least one second value of the characteristic associated with the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers.
Bai teaches,
The apparatus of claim 14, wherein the instructions to transmit the indication of the difference between the machine learning-based inference and the measurement of the characteristic are executable by the at least one processor to cause the UE to:
transmit the at least one first value of the characteristic associated with the first set one or more reference signal resource identifiers or synchronization signal block resource identifiers and the at least one second value of the characteristic associated with the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers. (Figure 7, labels t1, t4, paragraph 0094 – The UE submits a channel measurement report request for the first reference signal which contains a pathloss measurement based on the request from a base station. Figure 9, paragraph 0112 – The UE transmits a report for the pathloss of the second beam measurement.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the measuring and transmission of a second signal teachings of Bai. The purpose of doing so is to reduce the number of measurements by not measuring all beams and instead choosing a first beam to predict a pathloss of a second beam (paragraph 0034).
Regarding claim 29, Wang fails to teach,
The apparatus of claim 24, wherein the instructions to transmit the control signaling are executable by the at least one processor to cause the network entity to:
transmit an instruction to report at least one first value of the characteristic associated with a first set of one or more reference signal resource identifiers or synchronization signal block resource identifiers and at least one predicted second value of the characteristic associated with a second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers, wherein the machine learning-based inference comprises the at least one predicted second value of the characteristic.
Bai teaches,
The apparatus of claim 24, wherein the instructions to transmit the control signaling are executable by the at least one processor to cause the network entity to:
transmit an instruction to report at least one first value of the characteristic associated with a first set of one or more reference signal resource identifiers or synchronization signal block resource identifiers and at least one predicted second value of the characteristic associated with a second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers, wherein the machine learning-based inference comprises the at least one predicted second value of the characteristic. (Figure 7, labels t1, t4, paragraph 0094 – The UE receives a request for a pathloss measurement associated with a first beam. Paragraph 0112 – In response to a threshold based on the measurement of the first beam the UE receives an update requesting the transmission of a second transmission pathloss.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the measuring and transmission of a second signal teachings of Bai. The purpose of doing so is to reduce the number of measurements by not measuring all beams and instead choosing a first beam to predict a pathloss of a second beam (paragraph 0034).
Regarding claim 30, Wang fails to teach,
The apparatus of claim 29, wherein the instructions to receive the indication of the difference between the machine learning-based inference and the measurement of the characteristic are executable by the at least one processor to cause the network entity to:
receive the at least one first value of the characteristic associated with the first set of one or more reference signal resource identifiers or synchronization signal block resource identifiers and the at least one predicted second value of the characteristic associated with the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers.
Bai teaches,
The apparatus of claim 29, wherein the instructions to receive the indication of the difference between the machine learning-based inference and the measurement of the characteristic are executable by the at least one processor to cause the network entity to:
receive the at least one first value of the characteristic associated with the first set of one or more reference signal resource identifiers or synchronization signal block resource identifiers and the at least one predicted second value of the characteristic associated with the second set of one or more reference signal resource identifiers or synchronization signal block resource identifiers. (Figure 7, labels t1, t4, paragraph 0094 – The UE submits a channel measurement report request for the first reference signal which contains a pathloss measurement based on the request from a base station. Figure 9, paragraph 0112 – The UE transmits a report for the pathloss of the second beam measurement.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the measuring and transmission of a second signal teachings of Bai. The purpose of doing so is to reduce the number of measurements by not measuring all beams and instead choosing a first beam to predict a pathloss of a second beam (paragraph 0034).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Zhang et al. (US 20250386225 A1), hereinafter Zhang.
Regarding claim 23, Wang fails to teach,
The apparatus of claim 1, wherein the instructions to transmit the indication of the difference between the machine learning-based inference and the measurement of the characteristic are executable by the at least one processor to cause the UE to:
transmit an indication of a state of one or more hidden layers of a machine learning model associated with the machine learning-based inference.
Zhang teaches,
The apparatus of claim 1, wherein the instructions to transmit the indication of the difference between the machine learning-based inference and the measurement of the characteristic are executable by the at least one processor to cause the UE to:
transmit an indication of a state of one or more hidden layers of a machine learning model associated with the machine learning-based inference. (paragraph 0126 – The UE transmits a maximum number of hidden layers and maximum number of nodes per layer to the gNB (base station) associated with a machine learning model.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the hidden layers of a machine learning model teachings of Zhang. The purpose of doing so is to facilitate selection of a model that is within the capability of a UE to use (paragraph 0126).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 form.
Butt et al. (US 20240385278 A1) – Figures 3, 4, 5, Using thresholds, performing inference at UE for ML model.
Malboubi et al. (US 20230016839 A1) – Figures 2E, 2F, 2G – Generally collecting data from a UE and optionally processing it using a machine learning model to make adjustments to the network configuration. Figure 2D, Paragraph 0041 – processing of historical data received by a UE and visualization.
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/RYAN CRIGLER/Examiner, Art Unit 2472
/NICHOLAS A JENSEN/Supervisory Patent Examiner, Art Unit 2472
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