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 .
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)(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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Da Silva et al (2023/0300654).
Regarding claim 1, Da Silva discloses a processor (see the UE 103 may be implemented through one or more processors in paragraph 092) comprising: one or more circuits (see figure 6a) to use one or more neural networks (see a Neural Network (NN) in paragraph 0182) to cause a prediction of a quality of one or more wireless signals to be transmitted, wherein the prediction is based, at least in part, on one or more reference signals (see The RLF predictions are used as input to SON function(s) i.e. an offline process to tune parameters, such as an A3 threshold for Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ) or Signal to Interference plus Noise Ratio (SINR) in paragraph 0019; the UE 103 perform predictions, based on an RS type like SSB and/or CSI-RS and/or DRMS in paragraph 0197).
Regarding claim 2, Da Silva discloses wherein the one or more circuits are to use the prediction of the quality of the one or more wireless signals to generate a measurement report (see the UE to report the following measurement information based on Channel State Information-Reference Signal (CSI-RS) resources: Measurement results per CSI-RS resource; Measurement results per cell based on CSI-RS resource(s); CSI-RS resource measurement identifiers in paragraph 0007; measurement reporting in paragraph 0031).
Regarding claim 3, Da Silva discloses wherein the one or more reference signals are one or more inputs to the one or more neural networks (see The network node 101 may take different input from the UE 103 to take a decision concerning the prediction model to provide the UE 103 and/or its configurations. For example, a network node 101, e.g., a BS or a cloud node, may receive the UEs′ 103 measurement reports and use them to train a Neural Network (NN) in paragraph 0182) and the prediction of the quality of the one or more wireless signals are one or more outputs of the one or more neural networks (see as output, the indication of whether a failure occurs or not at instant “t+X”. Thus, the NN may be able to predict if the failure occurs or not, “X” instants of time in advance in paragraph 0182).
Regarding claim 4, Da Silva discloses wherein the prediction of the quality of the one or more wireless signals are to be transmitted by a user equipment (see the UEs′ 103 measurement reports and use them to train a Neural Network (NN) in paragraph 0182).
Regarding claim 5, Da Silva discloses wherein the one or more reference signals include synchronization signal block (SSB) signals (see The predicted information may be based on measurements performed on downlink reference signal resources. The downlink reference signal resources may be for example at least one of SSB and CSI-RS in paragraph 0087).
Regarding claim 6, Da Silva discloses wherein the one or more reference signals include channel state information reference signals (CSI-RS) (see The predicted information may be based on measurements performed on downlink reference signal resources. The downlink reference signal resources may be for example at least one of SSB and CSI-RS in paragraph 0087).
Regarding claim 7, Da Silva discloses wherein the prediction of the quality of the one or more wireless signals includes at least one or more of a reference signal received power (RSRP), a reference signal received quality (RSRQ), and a signal-to-interference-plus-noise ratio (SINR) (see The RLF predictions are used as input to SON function(s) i.e. an offline process to tune parameters, such as an A3 threshold for Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ) or Signal to Interference plus Noise Ratio (SINR) in paragraph 0019; To train the NN, one may use as input to the NN signal measurements, e.g., RSRP, RSRQ or in paragraph 0182).
Regarding claim 8, Da Silva discloses a system, comprising: one or more processors to use one or more neural networks (see a Neural Network (NN) in paragraph 0182) to cause a prediction of a quality of one or more wireless signals to be transmitted, wherein the prediction is based, at least in part, on one or more reference signals (see The RLF predictions are used as input to SON function(s) i.e. an offline process to tune parameters, such as an A3 threshold for Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ) or Signal to Interference plus Noise Ratio (SINR) in paragraph 0019; the UE 103 perform predictions, based on an RS type like SSB and/or CSI-RS and/or DRMS in paragraph 0197).
Regarding claim 9, Da Silva discloses wherein the one or more processors are to use the prediction of the quality of the one or more wireless signals to generate a measurement report (see the UE to report the following measurement information based on Channel State Information-Reference Signal (CSI-RS) resources: Measurement results per CSI-RS resource; Measurement results per cell based on CSI-RS resource(s); CSI-RS resource measurement identifiers in paragraph 0007; measurement reporting in paragraph 0031).
Regarding claim 10, Da Silva discloses wherein the one or more reference signals are one or more inputs to the one or more neural networks (see The network node 101 may take different input from the UE 103 to take a decision concerning the prediction model to provide the UE 103 and/or its configurations. For example, a network node 101, e.g., a BS or a cloud node, may receive the UEs′ 103 measurement reports and use them to train a Neural Network (NN) in paragraph 0182) and the prediction of the quality of the one or more wireless signals are one or more outputs of the one or more neural network (see as output, the indication of whether a failure occurs or not at instant “t+X”. Thus, the NN may be able to predict if the failure occurs or not, “X” instants of time in advance in paragraph 0182).
Regarding claim 11, Da Silva discloses wherein the prediction of the quality of the one or more wireless signals is transmitted by a radio link (see The network node 101 may be configured to communicate in the wireless communication network 100 with the UE 103 over a communication link 108, e.g., a radio link in paragraph 0058).
Regarding claim 12, Da Silva discloses wherein the prediction of the quality of the one or more wireless signals is filtered by a layer 3 filter (see The input parameters may be either instantaneous values or filtered values, e.g. with L3 filter parameters configured by RRC in paragraphs 0197, 0223).
Regarding claim 13, Da Silva discloses wherein the prediction of the quality of the one or more wireless signals is further based on a triggering event condition (see When the event is triggered for at least one cell, a measurement report is transmitted in paragraph 0002).
Regarding claim 14, Da Silva discloses wherein the triggering event condition further includes an entering condition of the event and a leaving condition of the event (see the UE may be configured by the network to perform Radio Resource Management (RRM) measurements, typically called RRM/L3 measurements, and report them periodically or based on the triggering of configured events in paragraph 0003; the UE 103 performs predictions of information related to failures. The information may comprise at least one of the following: an indication that a failure may be declared, an indication of the reason why a failure may be declared, such as due to potential physical layer problems, potential expiry of timer T310, potential Media Access Control (MAC) protocol problems due to a possibly reach of the maximum number of preamble transmission attempts, potential failure problems due to a possibly reach of the maximum number of retransmissions, predictions of further details concerning failure declaration such as predictions of the occurrence(s) of Out-of-Sync (OOS) events or In-Sync (IS) events, and predictions of the SINR measurement used as input to determine an OSS event or IS event, etc in paragraphs 0012-0016).
Regarding claim 15, Da Silva discloses a method, comprising: using one or more neural networks (see a Neural Network (NN) in paragraph 0182) to cause a prediction of a quality of one or more wireless signals to be transmitted, wherein the prediction is based, at least in part, on one or more reference signals (see The RLF predictions are used as input to SON function(s) i.e. an offline process to tune parameters, such as an A3 threshold for Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ) or Signal to Interference plus Noise Ratio (SINR) in paragraph 0019; the UE 103 perform predictions, based on an RS type like SSB and/or CSI-RS and/or DRMS in paragraph 0197).
Regarding claim 16, Da Silva discloses using the prediction of the quality of the one or more wireless signals to generate a measurement report (see the UE to report the following measurement information based on Channel State Information-Reference Signal (CSI-RS) resources: Measurement results per CSI-RS resource; Measurement results per cell based on CSI-RS resource(s); CSI-RS resource measurement identifiers in paragraph 0007; measurement reporting in paragraph 0031).
Regarding claim 17, Da Silva discloses wherein the one or more reference signals are one or more inputs to the one or more neural networks (see The network node 101 may take different input from the UE 103 to take a decision concerning the prediction model to provide the UE 103 and/or its configurations. For example, a network node 101, e.g., a BS or a cloud node, may receive the UEs′ 103 measurement reports and use them to train a Neural Network (NN) in paragraph 0182) and the prediction of the quality of the one or more wireless signals are one or more outputs of the one or more neural networks (see as output, the indication of whether a failure occurs or not at instant “t+X”. Thus, the NN may be able to predict if the failure occurs or not, “X” instants of time in advance in paragraph 0182).
Regarding claim 18, Da Silva discloses generating a second prediction of the quality of the one or more wireless signals after a defined time interval (see The network node 101 may transmit a configuration of a prediction report to the UE 103 configuring at least one of: what information to predict; what to include in the prediction report; an entry condition triggering the prediction report; a period (a defined time interval) for reporting; and an associated configuration of a measurement report to apply for the prediction report in paragraphs 0066-0070).
Regarding claim 19, Da Silva discloses wherein the one or more reference signals are periodically transmitted (see a period for reporting in paragraph 0069; predictions of information related to failure may be comprised in the report before it is transmitted, periodically in paragraph 0244).
Regarding claim 20, Da Silva discloses transmitting, by a user device, the prediction of the quality of the one or more wireless signals to a base station in a fifth generation (5G) network (see Fifth Generation (5G) New Radio (NR) in paragraphs 0002, 0053).
Conclusion
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/BRIAN D NGUYEN/Primary Examiner, Art Unit 2475