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 1-20 are pending.
Figure 6A of the application illustrates the claimed invention.
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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.
Claim(s) 1, 3-4, 8-9, 12, 16, 18-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Esswie et al. (US 2024/0196230 A1).
Regarding claims 1, 8 and 16, Esswie et al. discloses an apparatus for wireless communication at a user equipment (UE), comprising:
one or more memories; and
one or more processors, coupled to the one or more memories, which individually or in any combination, are operable to cause the apparatus to:
transmit a UE capability reporting message including information associated with identifying a set of UE conditions (report condition 220A-220n), associated with a first set of functionalities, wherein the first set of functionalities (function 205A-205n) corresponds to a set of model features (215A-215n), and
receive, based at least in part on transmitting the UE capabilities reporting message, control signaling identifying a set of second functionalities that is a subset of the first set of functionalities.
([0092]: “Turning now to FIG. 2, the figure illustrates a system 200 comprising a RAN node 105 in communication with a user equipment 115 via wireless link 125, UE 115 may perform various radio functions 205A-205n that may be facilitated by corresponding machine learning models 215A-215n, respectively. UE 115 may transmit an indication of radio function learning model information 207 to RAN 105. RAN 105 may transmit to UE 115 a machine learning model management indication configuration 210 corresponding to, or based on, learning model information 207. During UE 115 wireless operation and communication with RAN 105, the UE may transmit parameter metric reports 220A-220n, that may comprise one or more learning parameter metrics, corresponding to 215A-215n, respectively. In an embodiment, reports 220A-220n may comprise one or more control action requests, for example, requesting that one or more of models 215A-215n be deactivated, or retrained.”), see figures 2 and 4
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Regarding claims 3, 9 and 18, Esswie et al. teaches wherein the first set of functionalities (210) is derived based on a correspondence between the set of UE conditions ([0092]: “metrics transmitted in one or more reports 220A-220n). In another word, the user equipment conditions may be transmitted in condition report 220A-220n). ([0092]: “RAN 105 may transmit to UE 115 a machine learning model management indication configuration 210 corresponding to, or based on, learning model information 207.”).
Regarding claims 4, 12 and 19, Esswie et al. teaches wherein the control signaling includes at least one of:
activation information,
deactivation information, ([0004]: “The at least one control operation may comprise deactivating the first radio function learning model and activating a configured default radio function to perform the radio function, wherein the at least one control operation is determined by the radio access network node.”).
switching information, fallback information,
fallback information ([0046]: “a network RAN can monitor AI/ML learning model performance of user equipment via UE feedback/signaling and to react to metrics corresponding to such monitoring that may enable fallback mechanisms, or that initiate, trigger, or otherwise cause retraining of an ill-performing learning model (e.g., ill-performing as determined from the monitored learning model metrics) at the user equipment.”) or
monitoring information ([0003]: “The user equipment3 may monitor a model performance parameter, which may be indicated for monitoring in a first radio function learning model implement a radio function on the user equipment, to result in a monitored model performance metric.”).
Claims 1, 3-4, 8-9, 12, 16, 18-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lo et al. (US 2023/0006913 A1).
Regarding claims 1, 8 and 16, Lo et al. disclos99es an apparatus for wireless communication at a user equipment (UE), comprising:
one or more memories; and
one or more processors, coupled to the one or more memories, which individually or in any combination, are operable to cause the apparatus to:
transmit a UE capability reporting message including information associated with identifying a set of UE conditions (channel environment) associated with a first set of functionalities, wherein the first set of functionalities corresponds to a set of model feature ([0149]: “a method 1200 for operations at a UE to support AI/ML techniques for channel environment classification where the UE sends information on a particular channel environment. At operation 1201, the UE reports capability information to a BS, including the support of an ML approach for channel environment classification.”); and
receive, based at least in part on transmitting the UE capabilities reporting message, control signaling identifying a set of second functionalities that is a subset of the first set of functionalities.
([0149]: “At operation 1202, the UE receives configuration information from the BS, which can include ML-related configuration information such as enabling/disabling of an ML approach for channel environment classification, an ML model to be used, trained model parameters, and/or whether model parameter updated reported by the UE will be used or not.”).
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Regarding claims 3, 9 and 18, Lo et al. teaches wherein the first set of functionalities is derived based on a correspondence between the set of UE conditions (channel environment classification) and the first set of functionalities (enabling/disabling). (“The user equipment may receive configuration for ML based channel environment classification, including at least enabling/disabling of ML based channel environment classification.” See abstract). Herein, the set of functionalities are enabling/disabling and User condition is channel environment).
Regarding claims 4, 12 and 19, Lo et al. teaches wherein the control signaling includes at least one of:
activation information,
deactivation information,
switching information,
fallback information or
monitoring information ([0159]: “The BS can utilize the channel environment report, which can include recommendations for particular use cases, to adapt the BS’ transmission and/or reception parameters...the BS can switch the DL transmission mode from transmit diversity to spatial multiplexing if the coherence time and the coherence bandwidth of the channel increases. In yet another example, the BS can issue a BS handover command, change transmission mode, change time/frequency resource for scheduling, update MIMO beam direction, etc.”).
Allowable Subject Matter
Claims 2, 5-7, 10-11, 13-15 and 20 are 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDA H PHAM whose telephone number is (571)272-3135. The examiner can normally be reached 571-272-3135.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Jiang can be reached at 571-270-7191. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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BRENDA H. PHAM
Primary Examiner
Art Unit 2412
/BRENDA H PHAM/Primary Examiner, Art Unit 2412