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 .
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.
Claim(s) 1-2, 4-6, 8-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tullberg US 20220078637 in view of Chen US 20230216745
1. An apparatus for wireless communications at a user equipment (UE), comprising: a [[processor]]; memory coupled with the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to:
receive, from a base station, machine learning model information defining a machine learning model for the UE (Tullberg: fig. 8, unit 802, 804 [0205] the network node 100 may transmit updated ML model parameters W and a corresponding index);
receive, from the base station, a configuration defining a trigger for reporting a status of the machine learning model (Tullberg: fig. 8, unit 805 [0206] request to transmit ML training error msg);
detect the trigger for reporting the status of the machine learning model based at least in part on the configuration (Tullberg: fig. 8, unit 806 [0207] ML training error msg); and
transmit, to the base station, a report message indicating the status of the machine learning model based at least in part on detecting the trigger (Tullberg: fig. 8, unit 806 [0207] ML training error msg).
Tullberg merely discloses the term “processor”
Chen teaches the term processor (Chen: [0012] one or more processors)
Thus, it would have been obvious to one skill in the art before the effective filing date of the claim invention to include the above recited limitation into Tullberg’s invention in order to perform an AI-based network intelligent optimization flow [0003], as taught by Chen.
2. (Original) The apparatus of claim 1, wherein the instructions are further executable by the processor to cause the apparatus to: determine a periodic resource pattern for reporting the status of the machine learning model based at least in part on the configuration, wherein the instructions to transmit the report message are executable by the processor to cause the apparatus to transmit the report message in an uplink resource according to the periodic resource pattern (Chen: [0063] event reporting or periodic reporting).
4. (Currently Amended) The apparatus of claim 2, wherein the instructions to detect the trigger arc executable by the processor to cause the apparatus to the trigger comprises: trigger a transmission of the report message based at least in part on each an occurrence of a periodic uplink resource of the periodic resource pattern, one or more conditions of the machine learning model satisfying one or more threshold conditions, an indication from the base station to report the status of the machine learning model, a priority of the machine learning model satisfying a priority threshold, or any combination thereof (Chen: [0063] event reporting or periodic reporting).
5. (Original) The apparatus of claim 1, wherein the instructions to detect the trigger are executable by the processor to cause the apparatus to: detect a failure of the machine learning model based at least in part on a model outage detection method configured by the configuration, wherein the instructions to transmit the report message are executable by the processor to cause the apparatus to transmit the report message based at least in part on detecting the failure of the machine learning model (Chen: [0051] fails, or the ML model in the setup request message cannot be satisfied).
6. (Original) The apparatus of claim 5, wherein the instructions are further executable by the processor to cause the apparatus to: transmit, to the base station, a model failure indication based at least in part on detecting the failure of the machine learning model; and receive, from the base station and in response to the model failure indication, a failure report query, wherein the instructions to transmit the report message are executable by the processor to cause the apparatus to transmit the report message in response to the failure report query (Chen: [0096-0097] failure of terminal device measurement).
8. (Original) The apparatus of claim 1, wherein the instructions to detect the trigger are executable by the processor to cause the apparatus to: receive, from the base station, a configuration message indicating to report the status of the machine learning model, wherein the instructions to transmit the report message are executable by the processor to cause the apparatus to transmit the report message in response to the configuration message indicating to report the status of the machine learning model (Chen: [0149] measurement report message).
9. (Original) The apparatus of claim 8, wherein the configuration message indicating to report the status of the machine learning model comprises a model index corresponding to the machine learning model, a resource indication for transmission of the report message, a timer corresponding to the status of the machine learning model, a timestamp corresponding to the status of the machine learning model, or any combination thereof (Chen: [0085] report message optionally contains an ML session ID configured to uniquely identify an ID of a certain process; [0151] model iterative (i.e., index) update).
10. (Original) The apparatus of claim 1, wherein the instructions to receive the machine learning model information and the instructions to receive the configuration are executable by the processor to cause the apparatus to: receive, from the base station, a model download message comprising the machine learning model information defining the machine learning model and the configuration defining the trigger for reporting the status of the machine learning model, wherein the configuration is specific to the machine learning model (Chen: [0059-0061] first model deployment message).
11. (Original) The apparatus of claim 1, wherein the instructions to receive the configuration are executable by the processor to cause the apparatus to: receive, from the base station, a model status reporting configuration message separate from the machine learning model information, the model status reporting configuration message comprising an indication of a model index corresponding to the machine learning model or an indication that the configuration corresponds to a general configuration for machine learning models (Chen: [0059-0061] a first model performance reporting indication; [0151] model iterative (i.e., index) update).
12. (Currently Amended) The apparatus of claim 1, wherein the report message comprises: a status report for the machine learning model, the status report comprising at least a first model index corresponding to the machine learning model and the status of the machine learning model, wherein the status of the machine learning model comprises model variation information for the machine learning model; a failure report for the machine learning model, the failure report comprising a payload size, an indication of a fallback mode, the first model index corresponding to the machine learning model, a second model index corresponding to a fallback machine learning model, the status of the machine learning model, or any combination thereof, wherein the status of the machine learning model comprises input data to the machine learning model, statistics for the machine learning model, an output distribution of the machine learning model, or any combination thereof; or both (Chen: [0059-0061] a first model performance reporting indication; [0151] model iterative (i.e., index) update).
Regarding claims 13-16, 31-44 the independent claim and each dependent claim are related to the same limitation set for hereinabove in claims 1-12, where the difference used is a “method” & “apparatus” with a processor and a memory and the wordings of the claims were interchanged within the claim itself or some of the claims were presented as a combination of two or more previously presented limitations. This change does not affect the limitation of the above treated claims. Adding these phrases to the claims arid interchanging the wording did not introduce new limitations to these claims. Therefore, these claims were rejected for similar reasons as stated above.
Allowable Subject Matter
Claim 3 & 7 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.
Response to Arguments
Applicant's arguments filed on 6/9/26 have been fully considered.
Applicant Argument:
Applicant respectfully elects Group I & II (claims 1-16) with traversal.
Response to Arguments:
With respect to the above argument, Examiner would like to draw attention to that
the Applicant’s traversal has been considered for Group I & II (claims 1-16), however, the non-elected Group III & Group IIII (claims 17-30) were deleted/cancelled.
Remark:
The examiner stresses that the claims are too broad and require detail or specialization of the steps as recited in the claims. Alone and as claimed, the limitations are too open.
Examiner has cited particular portions of the references as applied to each claim limitation for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In addition, an interview could expedite the prosecution.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sulaiman Nooristany whose telephone number is (571) 270-1929. The examiner can normally be reached on M-F from 9 to 5. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Jeffrey Rutkowski, can be reached on (571) 270-1215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/SULAIMAN NOORISTANY/Primary Examiner, Art Unit 2415
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