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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/29/2024, 06/25/2026 and 07/01/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claim 1 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jeon (US 2024/0113794, relying on the Provisional Applications 63/411,216 and 63/412,147).
Regarding Claim 1, Jeon teaches a method of a user equipment (UE), comprising:
sending, to a network, a notification message identifying one or more measurement prediction models at the UE ([0143] The procedure begins with 1610, a UE sends to the network 130 its capabilities related to CSI prediction including supported models, either AI/ML-based or non-AI/ML-based, supported neural network types/complexities, and the support of model transfer, switch, update, training, and fallback; [0152] the UE 116 capability information includes an indication on the list of supported AI/ML models for CSI prediction. For example, the UE 116 may have multiple site-specifically trained models. As an example, it can be a sequence of Boolean indication for the support of CSI prediction models trained for different channel environments including; [0154] The UE 116 capability signaling may include an indication on the support of model switch, transfer, or reconfiguration. The model switch capability relates to the capability that the UE 116 can switch CSI prediction model among multiple models that the UE 116 supports);
receiving, from the network, an activation message identifying a first measurement prediction model for use from the one or more measurement prediction models at the UE ([0108] in 910, a network provides parameters and/or model for CSI prediction to a UE. In 920, the network 130 then provides reference signals for channel measurement and for a UE to perform channel measurement; [0111] a UE is provided from the network 130 parameters and/or models for future CSI prediction. The parameters that can be provided by the network 130 to the UE 116 includes those related to CSI measurement window, CSI prediction window or set of future instances for prediction, CSI report format, and prediction model including but not limited to AI/ML-based CSI prediction, Extended Kalman Filter (EKF)-based prediction; [0143] In 1620, a UE is then provided from the network 130 parameters and/or models for CSI prediction, either AI/ML-based or non-AI/ML-based, e.g., via model ID, model transfer, or model description, and feedback format. In 1630, the UE 116 is then provided from the network 130 information related to CSI prediction model monitoring including performance index to monitor, triggering events to send a report and/or transmission of any assistance information to the network 130; [0156] If a UE supports merely one CSI prediction model, the network 130 may indicate the UE 116 activation/deactivation of CSI prediction using a Boolean indication. If the UE 116 supports more than one CSI prediction models, the network 130 may indicate the UE 116 a chosen CSI prediction model using model ID. In this case, multiple models supported by the UE 116 can be registered to the network 130 during UE capability indication and assigned with unique IDs; [0157] The UE 116 may receive a CSI prediction model transferred from the network 130 or according to the model description received from the network 130 including model structure, and/or parameter values. The transferred model or provided model description shall remain within the indicated UE capability);
generating one or more actual measurements of one or more reference signals received at the UE from a cell of the network ([0108] In 920, the network 130 then provides reference signals for channel measurement and for a UE to perform channel measurement; [0111] In 1020, the UE 116 then measures reference signals transmitted from the network 130. The reference signals include CSI-RS as well as synchronization signal block (SSB), tracking reference signal (TRS), and/or any signal transmitted from the network 130 that the UE 116 can utilize to estimate the channel; [0143] In 1630, the UE 116 is then provided from the network 130 information related to CSI prediction model monitoring including performance index to monitor, triggering events to send a report and/or transmission of any assistance information to the network 130);
generating, using the first measurement prediction model, one or more predicted measurements based the one or more actual measurements ([0108] In 930, the UE 116 then performs CSI prediction for future instances; [0111] a UE is provided from the network 130 parameters and/or models for future CSI prediction. The parameters that can be provided by the network 130 to the UE 116 includes those related to CSI measurement window, CSI prediction window or set of future instances for prediction, CSI report format, and prediction model including but not limited to AI/ML-based CSI prediction, Extended Kalman Filter (EKF)-based prediction, etc. In 1020, the UE 116 then measures reference signals transmitted from the network 130. ... In 1030, the UE 116 then calculates the future CSI according to the parameters and/or models provided from the network 130; [0143] In 1630, the UE 116 is then provided from the network 130 information related to CSI prediction model monitoring including performance index to monitor, triggering events to send a report and/or transmission of any assistance information to the network 130. In 1640, the UE 116 then sends to the network 130 CSI prediction model monitoring report if triggering events are met and/or any assistance information); and
sending a first measurement report comprising the one or more predicted measurements to the network ([0108] In 940, the UE 116 then send the CSI report to the network; [0111] a UE is provided from the network 130 parameters and/or models for future CSI prediction. The parameters that can be provided by the network 130 to the UE 116 includes those related to CSI measurement window, CSI prediction window or set of future instances for prediction, CSI report format, and prediction model including but not limited to AI/ML-based CSI prediction, Extended Kalman Filter (EKF)-based prediction, etc. ... In 1040, the UE 116 sends to the network 130 the CSI report including the predicted future CSI feedback; [0143] In 1640, the UE 116 then sends to the network 130 CSI prediction model monitoring report if triggering events are met and/or any assistance information).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-7, 9-12 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Jeon in view of Lu et al. (US 2026/0082261).
Regarding Claim 2, Jeon does not teach the one or more predicted measurements comprise one or more predicted Layer 3 (L3) cell-level measurements.
In an analogous art, Lu teaches the one or more predicted measurements comprise one or more predicted Layer 3 (L3) cell-level measurements ([0149] the output of the prediction model is a predicted value of the layer 3 measurement quantity; [0150] the output of the prediction model is a predicted value of the layer 3 measurement quantity; [0163] the terminal may determine, based on the first information and predicted values of layer 3 measurement quantities of a plurality of cells at least one time point in a first time period, ... The plurality of cells may include a serving cell and/or a candidate cell of the terminal; [0169] the measurement quantity corresponding to the output of the first prediction model is a layer 3 measurement quantity; [0170] the measurement quantity corresponding to the output of the first prediction model is a layer 3 measurement quantity; [0180] the first prediction model is started for prediction, to determine the predicted values of the layer 3 measurement quantities of the plurality of cells at the at least one time point in the first time period; [0183] configuring the measurement quantity corresponding to the output of the first prediction model as the layer 3 measurement quantity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 3, Jeon does not teach the one or more actual measurements comprise one or more actual L3 cell-level measurements for the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by providing the one or more actual L3 cell-level measurements to the first measurement prediction model.
In an analogous art, Lu teaches the one or more actual measurements comprise one or more actual L3 cell-level measurements for the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by providing the one or more actual L3 cell-level measurements to the first measurement prediction model ([0150] Case 3: Refer to (c) in FIG. 7. The input of the prediction model is a historical measurement value and/or a current measurement value of the layer 3 measurement quantity, and the output of the prediction model is a predicted value of the layer 3 measurement quantity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 4, Jeon does not teach the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by: providing the one or more actual L1 beam-level measurements to the first measurement prediction model to generate one or more predicted L1 beam-level measurements; and performing linear averaging and L3 filtering over the one or more actual L1 beam-level measurements and the one or more predicted L1 beam level- measurements.
In an analogous art, Lu teaches the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by: providing the one or more actual L1 beam-level measurements to the first measurement prediction model to generate one or more predicted L1 beam-level measurements; and performing linear averaging and L3 filtering over the one or more actual L1 beam-level measurements and the one or more predicted L1 beam level- measurements ([0148] Refer to (a) in FIG. 7. The input of the prediction model is a historical measurement value of the layer 1 measurement quantity, and the output of the prediction model is a predicted value of the layer 1 measurement quantity. In this case, the terminal may process, for example, perform beam consolidation and layer 3 filtering on, the predicted value of the layer 1 measurement quantity, to obtain a predicted value of the layer 3 measurement quantity. An existing configuration may be reused for a correlation coefficient of the layer 3 filtering. ... the terminal may process the predicted value of the layer 1 measurement quantity, to obtain the predicted value of the layer 3 measurement quantity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 5, Jeon does not teach L3 filter coefficients used for the L3 filtering are generated by the measurement prediction model.
In an analogous art, Lu teaches L3 filter coefficients used for the L3 filtering are generated by the measurement prediction model ([0148] Refer to (a) in FIG. 7. The input of the prediction model is a historical measurement value of the layer 1 measurement quantity, and the output of the prediction model is a predicted value of the layer 1 measurement quantity. In this case, the terminal may process, for example, perform beam consolidation and layer 3 filtering on, the predicted value of the layer 1 measurement quantity, to obtain a predicted value of the layer 3 measurement quantity. An existing configuration may be reused for a correlation coefficient of the layer 3 filtering. ... the terminal may process the predicted value of the layer 1 measurement quantity, to obtain the predicted value of the layer 3 measurement quantity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 6, Jeon does not teach the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by: performing linear averaging over the one or more actual L1 beam-level measurements; and providing one or more actual linear averaging results of the linear averaging and a set of configured L3 filter coefficients to the first measurement prediction model.
In an analogous art, Lu teaches the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by: performing linear averaging over the one or more actual L1 beam-level measurements; and providing one or more actual linear averaging results of the linear averaging and a set of configured L3 filter coefficients to the first measurement prediction model ([0150] Case 3: Refer to (c) in FIG. 7. The input of the prediction model is a historical measurement value and/or a current measurement value of the layer 3 measurement quantity, and the output of the prediction model is a predicted value of the layer 3 measurement quantity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 7, Jeon does not teach the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by providing the one or more actual L1 beam level measurements and a set of configured L3 filter coefficients to the first measurement prediction model.
In an analogous art, Lu teaches the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by providing the one or more actual L1 beam level measurements and a set of configured L3 filter coefficients to the first measurement prediction model ([0149] Case 2: Refer to (b) in FIG. 7. The input of the prediction model is a historical measurement value and/or a current measurement value of the layer 1 measurement quantity, and the output of the prediction model is a predicted value of the layer 3 measurement quantity. In this case, beam consolidation and layer 3 filtering have been integrated into the prediction model, and the network device may not need to configure a parameter related to the layer 3 filtering for the terminal. Optionally, in Case 2, the output of the prediction model may further include a predicted value of the layer 1 measurement quantity. In other words, in Case 2, the terminal may directly obtain the predicted value of the layer 1 measurement quantity (for example, the predicted value of the layer 1 measurement quantity may be used for LTM handover decision) and the predicted value of the layer 3 measurement quantity based on the output of the prediction model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 9, Jeon does not teach receiving, from the network, configuration information identifying the cell, and wherein the one or more predicted L3 cell-level measurements are for the cell.
In an analogous art, Lu teaches receiving, from the network, configuration information identifying the cell, and wherein the one or more predicted L3 cell-level measurements are for the cell ([0103] S302: The terminal performs measurement on a serving cell and a candidate cell based on the measurement configuration information; [0108] the handover command may include related information about the target cell and the configuration information needed by the terminal to access the target cell, for example, an identifier of the target cell and frequency information corresponding to the target cell).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 10, Jeon does not teach receiving, from the network, configuration information identifying a frequency, and wherein the one or more predicted L3 cell-level measurements are for the frequency.
In an analogous art, Lu teaches receiving, from the network, configuration information identifying a frequency, and wherein the one or more predicted L3 cell-level measurements are for the frequency ([0108] the handover command may include related information about the target cell and the configuration information needed by the terminal to access the target cell, for example, an identifier of the target cell and frequency information corresponding to the target cell).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 11, the combination of Jeon and Lu, specifically Jeon teaches receiving, from the network, configuration information identifying a condition for generating the one or more predicted L3 cell-level measurements, and wherein the one or more predicted L3 cell-level measurements are generated in response to determining, at the UE, that the condition has been met ([0164] the UE received information related to transmitting the performance monitoring report may include/indicate at least one of (i) a triggering condition for transmitting the performance monitoring report, where the triggering condition is indicated by one or more threshold values on respective one or more performance indexes and (ii) an uplink channel (e.g., a physical uplink control channel (PUCCH)) for the transmission of the performance monitoring report; [0169] The UE 116 can be provided from the network 130 other performance index to monitor the performance of the currently used CSI prediction model and the triggering events to send the report to the network 130 when the conditions are met).
Regarding Claim 12, the combination of Jeon and Lu, specifically Jeon teaches the condition comprises whether a prior L3 measurement is less than a threshold ([0164] the UE 116 may send the report if the metric value is lesser than a certain threshold).
Regarding Claim 14, Jeon does not teach receiving, from the network, configuration information comprising an identification of the cell, and wherein the UE selects to generate the one or more predicted L3 cell-level measurements based on the identification of the cell in the configuration information.
In an analogous art, Lu teaches receiving, from the network, configuration information comprising an identification of the cell, and wherein the UE selects to generate the one or more predicted L3 cell-level measurements based on the identification of the cell in the configuration information ([0103] S302: The terminal performs measurement on a serving cell and a candidate cell based on the measurement configuration information; [0108] the handover command may include related information about the target cell and the configuration information needed by the terminal to access the target cell, for example, an identifier of the target cell and frequency information corresponding to the target cell).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 15, Jeon does not teach the one or more predicted measurements comprise one or more predicted Layer 3 (L3) beam-level measurements.
In an analogous art, Lu teaches the one or more predicted measurements comprise one or more predicted Layer 3 (L3) beam-level measurements ([0149] Case 2: Refer to (b) in FIG. 7. The input of the prediction model is a historical measurement value and/or a current measurement value of the layer 1 measurement quantity, and the output of the prediction model is a predicted value of the layer 3 measurement quantity. In this case, beam consolidation and layer 3 filtering have been integrated into the prediction model, and the network device may not need to configure a parameter related to the layer 3 filtering for the terminal. Optionally, in Case 2, the output of the prediction model may further include a predicted value of the layer 1 measurement quantity. In other words, in Case 2, the terminal may directly obtain the predicted value of the layer 1 measurement quantity (for example, the predicted value of the layer 1 measurement quantity may be used for LTM handover decision) and the predicted value of the layer 3 measurement quantity based on the output of the prediction model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 16, Jeon does not teach the one or more actual measurements comprise one or more actual L3 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 beam-level measurements are generated using the first measurement prediction model by providing the one or more actual L3 beam-level measurements to the first measurement prediction model.
In an analogous art, Lu teaches the one or more actual measurements comprise one or more actual L3 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 beam-level measurements are generated using the first measurement prediction model by providing the one or more actual L3 beam-level measurements to the first measurement prediction model ([0150] Case 3: Refer to (c) in FIG. 7. The input of the prediction model is a historical measurement value and/or a current measurement value of the layer 3 measurement quantity, and the output of the prediction model is a predicted value of the layer 3 measurement quantity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 17, Jeon does not teach the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 beam-level measurements are generated using the first measurement prediction model by: providing the one or more actual L1 beam-level measurements to the first measurement prediction model to generate one or more predicted L1 beam-level measurements; and performing L3 filtering over the one or more actual L1 beam-level measurements and the one or more predicted L1 beam level-measurements.
In an analogous art, Lu teaches the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 beam-level measurements are generated using the first measurement prediction model by: providing the one or more actual L1 beam-level measurements to the first measurement prediction model to generate one or more predicted L1 beam-level measurements; and performing L3 filtering over the one or more actual L1 beam-level measurements and the one or more predicted L1 beam level-measurements ([0148] Refer to (a) in FIG. 7. The input of the prediction model is a historical measurement value of the layer 1 measurement quantity, and the output of the prediction model is a predicted value of the layer 1 measurement quantity. In this case, the terminal may process, for example, perform beam consolidation and layer 3 filtering on, the predicted value of the layer 1 measurement quantity, to obtain a predicted value of the layer 3 measurement quantity. An existing configuration may be reused for a correlation coefficient of the layer 3 filtering. ... the terminal may process the predicted value of the layer 1 measurement quantity, to obtain the predicted value of the layer 3 measurement quantity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Regarding Claim 18, Jeon does not teach the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 beam-level measurements are generated using the first measurement prediction model by providing the one or more actual L1 beam-level measurements to the first measurement prediction model.
In an analogous art, Lu teaches the one or more actual measurements comprise one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and the one or more predicted L3 beam-level measurements are generated using the first measurement prediction model by providing the one or more actual L1 beam-level measurements to the first measurement prediction model ([0149] Case 2: Refer to (b) in FIG. 7. The input of the prediction model is a historical measurement value and/or a current measurement value of the layer 1 measurement quantity, and the output of the prediction model is a predicted value of the layer 3 measurement quantity. In this case, beam consolidation and layer 3 filtering have been integrated into the prediction model, and the network device may not need to configure a parameter related to the layer 3 filtering for the terminal. Optionally, in Case 2, the output of the prediction model may further include a predicted value of the layer 1 measurement quantity. In other words, in Case 2, the terminal may directly obtain the predicted value of the layer 1 measurement quantity (for example, the predicted value of the layer 1 measurement quantity may be used for LTM handover decision) and the predicted value of the layer 3 measurement quantity based on the output of the prediction model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Lu’s method with Jeon’s method so that predicted L3 measurement reporting relies on strict 3GPP-standardized events which guarantee interoperability across multi-vendor equipment. Moreover, Layer-3 aggregates raw measurements into cell-level metrics, providing a comprehensive assessment of overall cell strength rather than momentary link-level quality.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Jeon in view of Lu et al. and Sabouri-Sichani et al. (US 2024/0014868).
Regarding Claim 8, the combination of Jeon and Lu does not teach the one or more predicted L3 cell-level measurements are for a neighbor cell to the cell; and the generating, using the first measurement prediction model, one or more predicted L3 cell-level measurements is further based on correlation information for the neighbor cell.
In an analogous art, Sabouri-Sichani teaches the one or more predicted L3 cell-level measurements are for a neighbor cell to the cell; and the generating, using the first measurement prediction model, one or more predicted L3 cell-level measurements is further based on correlation information for the neighbor cell ([0099] the UE provides estimates of beam measurements for neighbor cells based on power (e.g. RSRP) measurements made for these cells using the best panel selected for the serving cell. Such measurements refer to the gNB beam measurement evaluated by the UE panel exhibiting a broad beam configuration. Note that the UE panel can be configured for different beamwidths as well as for different angular directions. During neighbor cell measurements, the UE is typically configured with a broad beam as it does not know where the power is coming from. The UE thus does not switch panels for obtaining the estimates, since it only uses the serving cell's panel. In order to obtain this estimate for a given neighbor cell, the measurement made for the given neighbor cell using the panel selected for the serving cell is corrected by an offset. This offset for the given neighbor cell has previously been determined during a default measurement cycle: it is equal to the difference between a measurement carried out for the given neighbor cell during that default cycle as previously described and a measurement made using the serving cell's panel).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sabouri-Sichani’s method with Jeon’s method so that neighbor cell measurements can be efficiently obtained because the UE thus does not switch panels for obtaining the estimates, since it only uses the serving cell's panel.
Claim 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jeon in view of Lu et al. and Da Silva et al. (US 2024/0040461).
Regarding Claim 13, the combination of Jeon and Lu does not teach the condition comprises determining that the one or more predicted L3 cell-level measurements would correspond to inter-frequency measurements.
In an analogous art, Da Silva teaches the condition comprises determining that the one or more predicted L3 cell-level measurements would correspond to inter-frequency measurements ([0306] Upon reception of legacy A2 messages the network node 403 may become aware that a given serving cell, e.g. the SpCell, is getting worse than a threshold. And, if the network node 403 has not received any A3 message for that frequency, the network node 403 may configure inter-frequency measurements to possibly trigger an inter-frequency handover).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Da Silva’s method with Jeon’s method so that the network node may configure inter-frequency measurements to possibly trigger an inter-frequency handover (Da Silva [0306]).
Regarding Claim 20, the combination of Jeon and Lu does not teach receiving, from the network, configuration information identifying a beam, and wherein the one or more predicted L3 beam-level measurements are for the beam.
In an analogous art, Da Silva teaches receiving, from the network, configuration information identifying a beam, and wherein the one or more predicted L3 beam-level measurements are for the beam ([0493] The network may also configure the UE to report measurement or predicted information per beam (which can either be measurement results or predictions per beam with respective beam identifier(s) or only beam identifier(s)) associated to predicted measurements, derived. If beam measurement information is configured to be included in measurement and/or prediction reports, the UE applies the layer 3 beam filtering. On the other hand, the exact L1 filtering of beam measurements used to derive cell measurement results is implementation dependent).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Da Silva’s method with Jeon’s method so that the UE can select or lists the “best” beams according to criteria such as beam with strongest RSRP and/or RSRQ and/or SINR and at a given instant in time (Da Silva [0220]).
Allowable Subject Matter
Claim 19 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhou et al. (US 2025/0148295) teaches method of artificial intelligence request analysis.
Li et al. (US 2026/0046666) teaches CSI report using inference measurement resources.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YU-WEN CHANG whose telephone number is (408)918-7645. The examiner can normally be reached M-F 8:00am-5:00pm PT.
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, Un Cho can be reached at 571-272-7919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YU-WEN CHANG/Primary Examiner, Art Unit 2413
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