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 .
Claims 1-19 and 34 are pending. Claims 20-33 are canceled.
Claim Rejections - 35 USC § 103
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 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.
2. Claim(s) 1-19 and 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al, US 2025/0184764 hereafter Li in view of Bai et al, US 2023/0354077 hereafter Bai.
As for claims 1 and 16, Li discloses:
A wireless device (Li, [0070] A UE 115 may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or some other suitable terminology, where the “device” may also be referred to as a unit, a station, a terminal, or a client, among other examples.), comprising: a transceiver; a memory; and at least one processor operatively coupled to the transceiver and the memory, and adapted to:
control the transceiver to receive, from a network, a measurement configuration including a measurement object (Li, [0115] The network entity 105-a may transmit the machine learning model 305 to the UE 115-a. The network entity 105-a also may transmit control signaling 310 indicating a configuration for the UE 115-a 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. [0144] At 405, the network entity 105-b may transmit, and UE 115-b may receive, control signaling. The control signaling may be the control signaling 310 as described with reference to FIG. 3. The control signaling may indicate 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, where the characteristic may be associated with one or more of a spatial domain, a time domain, or a frequency domain, as described with reference to FIG. 3.);
derive a first prediction for the measurement object; (Li, [0147] At 420, the UE 115-b may perform the machine learning-based inference as described with reference to FIG. 3. For example, the UE 115-b may perform the 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 (e.g., the configuration indicated in the control signaling at 405).)
control the transceiver to transmit information about the first prediction to the network; (0104] For example, the network entity 105 may create the machine learning model (e.g., based on common cases or conditions) and deploy the machine learning model to the UE 115, then configure the UE 115 to report machine learning-based inference errors, such as errors that arise for less-common cases or outlier conditions. The UE 115 may perform inferences for an indicated characteristic using the machine learning model in the spatial domain, time domain, or frequency domain. The UE 115 also may perform an actual measurement of the characteristic and may determine the error between the machine learning model predicted inference and the actual measurement. The UE 115 may report the inference errors to the network entity 105 to update the machine learning model. In some cases, the UE 115 may report the inference error based on a trigger, such as if the inference error satisfies (e.g., exceeds) a threshold value.)
derive a second prediction for the measurement object (Li, [0103] To further train a machine learning model at the network, a network entity 105 may configure a UE 115 to perform inferences (i.e., predictions) for one or more, resource, channel, or beam characteristics using the machine learning model, then compare the inferences to actual measurements of the resource, channel, or beam characteristics to identify and report errors in the machine learning model to the network entity 105 (e.g., in addition to traditional beam management).)
Li does not explicitly disclose based on difference between the first prediction and the second prediction, control the transceiver to transmit information related to the difference to the network.
However, Bai discloses based on difference between the first prediction and the second prediction, control the transceiver to transmit information related to the difference to the network. (Bai, FIG. 9, [0126] At 910, the UE may determine, based on the first predicted information, that one or more conditions are satisfied. The one or more conditions are based on comparison of a difference between a first predicted value of the at least one predicted value and a second predicted value of the at least one predicted value with a fifth reporting threshold. [0134] At 912, the UE may transmit, to the base station based on the determination, a first report including second predicted information, with the second predicted information including at least a subset of the first predicted information.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li with based on difference between the first prediction and the second prediction, control the transceiver to transmit information related to the difference to the network as taught by Bai to provide reduce overhead and improve quality (Bai, [0035]-[0037]).
As for claims 2 and 17, Li discloses the measurement object includes information on at least one cell. (Li, [0129], the machine learning model input may include measured characteristics associated with a first resource set (e.g., a CSI-RS or an SSB resource set) associated with a first bandwidth part or a first serving cell, and the machine learning model output or the machine learning-based inference may include characteristics associated with a non-measured second resource set associated with a second bandwidth part or a second serving cell.)
As for claims 3 and 18, Li discloses the measurement object includes information on at least one reference signal and/or at least one Synchronization Signal Block (SSB). (Li, [0117], [0119] The predicted characteristic results based on the machine learning-based inference may be a first set of identifiers (e.g., channel state information reference signal (CSI-RS) or a synchronization symbol block (SSB) identifiers) corresponding to one or more resources with a highest predicted measurement of the characteristic. Also see Bai [0085] the UE may measure a subset of a set of signals (e.g., SSBs, CSI-RSs, etc.) transmitted by the base station in order to predict measurements for one, some, or all signals (e.g., SSBs, CSI-RSs, etc.) at a future time step.)
As for claims 4 and 19, Bai discloses determining, by the wireless device, whether to transmit information related to the difference to the network. (Bai, FIG. 9, [0126] At 910, the UE may determine, based on the first predicted information, that one or more conditions are satisfied. The one or more conditions are based on comparison of a difference between a first predicted value of the at least one predicted value and a second predicted value of the at least one predicted value with a fifth reporting threshold. [0134] At 912, the UE may transmit, to the base station based on the determination, a first report including second predicted information, with the second predicted information including at least a subset of the first predicted information.) Also see Li, [0030], transmitting the indication of the difference between the machine learning-based inference and the measurement of the characteristic may include operations, features, means, or instructions for transmitting an indication of a state of one or more hidden layers of a machine learning model associated with the machine learning-based inference. [0033], transmitting control signaling indicating a configuration for a 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 means for receiving, 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.)
As for claim 5, Bai discloses the information related to the difference is transmitted based on the difference between the first prediction and the second prediction being equal to or larger than a threshold. (FIG. 9, [0126] At 910, the UE may determine, based on the first predicted information, that one or more conditions are satisfied. The one or more conditions are based on comparison of a difference between a first predicted value of the at least one predicted value and a second predicted value of the at least one predicted value with a fifth reporting threshold. Also see Li, [0024], the triggering condition occurs when the difference between the machine learning-based inference and the measurement of the characteristic satisfies a threshold.)
As for claim 6, Li discloses the measurement configuration includes a reporting condition. (Li, [0058], In some cases, the UE may report the inference error based on a trigger, such as if the inference error satisfies (e.g., exceeds) a threshold value. In some cases, the UE may report its capability to provide feedback about inference errors for a machine learning model to the network entity.)
As for claim 7, Li discloses the first prediction for the measurement object is derived based on the reporting condition being satisfied. (Li, [0146] In some cases, at 415, the network entity 105-b may transmit, and the UE 115-a may receive, the triggering condition may indicate when the UE 115-b is to transmit an indication of a difference between the machine learning-based inference and the measurement of the characteristic. The triggering condition may occur when the difference between the machine learning-based inference and the measurement of the characteristic satisfies (e.g., exceeds) a threshold. [0147] At 420, the UE 115-b may perform the machine learning-based inference. The UE 115-b may perform the 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 (e.g., the configuration indicated in the control signaling at 405).)
As for claim 8, Li discloses the second prediction for the measurement object (0103] To further train a machine learning model at the network, a network entity 105 may configure a UE 115 to perform inferences (i.e., predictions) for one or more, resource, channel, or beam characteristics using the machine learning model) is derived based on the reporting condition being satisfied. (Li, [0146] In some cases, at 415, the network entity 105-b may transmit, and the UE 115-a may receive, the triggering condition may indicate when the UE 115-b is to transmit an indication of a difference between the machine learning-based inference and the measurement of the characteristic. The triggering condition may occur when the difference between the machine learning-based inference and the measurement of the characteristic satisfies (e.g., exceeds) a threshold. [0147] At 420, the UE 115-b may perform the machine learning-based inference. The UE 115-b may perform the 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 (e.g., the configuration indicated in the control signaling at 405).)
As for claim 9, Li discloses the first prediction is derived at a first time point for a particular future time point. (Li, [0121], The predicted later value of the characteristic may be the machine learning-based inference, such as an output of the machine learning model predicting the later (e.g., future) value. Also disclosed in Bai FIG. 5, [0082], The prediction function 520 may include a set of algorithms that may be designed to predict measurements at a future time, such as a future RSRP or future SNR, based on historical measurements obtained based on receiving signals from the base station 502.))
As for claim 10, Li discloses the second prediction (0103] To further train a machine learning model at the network, a network entity 105 may configure a UE 115 to perform inferences (i.e., predictions) for one or more, resource, channel, or beam characteristics using the machine learning model) is derived at a second time point for the particular future time point. (Li, [0121], The predicted later value of the characteristic may be the machine learning-based inference, such as an output of the machine learning model predicting the later (e.g., future) value. Also disclosed in Bai FIG. 5, [0082], The prediction function 520 may include a set of algorithms that may be designed to predict measurements at a future time, such as a future RSRP or future SNR, based on historical measurements obtained based on receiving signals from the base station 502.))
As for claim 11, Li discloses the first prediction includes at least one predictive measurement results at a future time point. (Li, [0121], The predicted later value of the characteristic may be the machine learning-based inference, such as an output of the machine learning model predicting the later (e.g., future) value. Also disclosed in Bai FIG. 5, [0082], The prediction function 520 may include a set of algorithms that may be designed to predict measurements at a future time, such as a future RSRP or future SNR, based on historical measurements obtained based on receiving signals from the base station 502.))
As for claim 12, Li discloses the second prediction (0103] To further train a machine learning model at the network, a network entity 105 may configure a UE 115 to perform inferences (i.e., predictions) for one or more, resource, channel, or beam characteristics using the machine learning model) includes at least one predictive measurement results at the future time point. (Li, [0121], The predicted later value of the characteristic may be the machine learning-based inference, such as an output of the machine learning model predicting the later (e.g., future) value. Also disclosed in Bai FIG. 5, [0082], The prediction function 520 may include a set of algorithms that may be designed to predict measurements at a future time, such as a future RSRP or future SNR, based on historical measurements obtained based on receiving signals from the base station 502.)
As for claim 13, Li discloses the first prediction includes at least one prediction time at which a reporting condition is satisfied. (Li, [0145] In some cases, at 410, the network entity 105-b may transmit, and the UE 115-b may receive, a length of time series indication, as described with reference to FIG. 3. For example, if the characteristic is associated with the time domain, the UE 115-a may use the length of time series indication to perform the machine learning-based inference for a number of historic values of the characteristic included in the length of time series. In some cases, the length of time series indication may be included in the control signaling at 405.)
As for claim 14, Li discloses wherein the second prediction (0103] To further train a machine learning model at the network, a network entity 105 may configure a UE 115 to perform inferences (i.e., predictions) for one or more, resource, channel, or beam characteristics using the machine learning model) includes at least one prediction time at which the reporting condition is satisfied. (Li, [0145] In some cases, at 410, the network entity 105-b may transmit, and the UE 115-b may receive, a length of time series indication, as described with reference to FIG. 3. For example, if the characteristic is associated with the time domain, the UE 115-a may use the length of time series indication to perform the machine learning-based inference for a number of historic values of the characteristic included in the length of time series. In some cases, the length of time series indication may be included in the control signaling at 405.)
As for claim 15, Li discloses the wireless device is in communication with at least one of a user equipment (Li, [0070] A UE 115 may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or some other suitable terminology, where the “device” may also be referred to as a unit, a station, a terminal, or a client, among other examples.), a network, or an autonomous vehicle other than the wireless device. (Li, [0070] a UE 115 may include or be referred to as a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine type communications (MTC) device, among other examples, which may be implemented in various objects such as appliances, or vehicles, meters, among other examples.)
Claim 34 is rejected for similar reasons as claims 1 and 16 above.
Conclusion
3. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENEE HOLLAND whose telephone number is (571)270-7196. The examiner can normally be reached 8:30 AM - 5:00 PM.
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JENEE HOLLAND
Examiner
Art Unit 2469
/JENEE HOLLAND/Primary Examiner, Art Unit 2469
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