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 .
Response to Arguments
Applicant's arguments filed 06/01/2026 have been fully considered but they are not persuasive.
Regarding claim 1, on pages 7-8 of applicant’s remarks, applicant argues that Tapia does not explicitly disclose the claim limitation of “obtaining a set of key performance indicators (KPls) for a machine learning (ML) model running on the UE; and transmitting, to an entity associated with the ML model, a report including an aggregation of the KPIs and additional performance feedback for the ML model;”
In response, the examiner respectfully disagrees. Under the broadest reasonable in interpretation under MPEP, the examiner respectfully interprets the claim limitation of “UE” as el. 120, el. 118, and el. 104 as illustrated in Fig. 1, as such, Tapia disclose the UE would include the functionality of performance extrapolation engine and performance analytic engine, and further teaches the claim limitation of use a machine learning model to determine the KPIs of the UE (para. 26). Further Tapia disclose that a report including an aggregation of the KPIs is transmitted (Fig. 2, el. 230, Fig. 3, el. 308, para. 57-60), and also Tapia teaches that the additional performance data [refers as feedback, el. 314, Fig. 3, para. 59-60] are further analyzed in the machine learning model or the modified training data are used to retrain the machine learning model (Fig. 5, el. 516, para. 70-72). As such, the rejection is maintained.
Regarding claim 2, on pages 7-8 of applicant’s remarks, applicant argues that Tapia does not explicitly disclose the claim limitation of “receiving a subscription request from the entity associated with the ML model, wherein the report is transmitted to the entity in response to the subscription request;”
In response, the examiner respectfully disagrees. Under the broadest reasonable interpretation allowed under MPEP, the examiner reasonably interprets the UE in the wireless carrier network which included the operation of ML as described in the claim 1 as the entity associated the ML model (para. 17, para. 20), and further Tapia teaches the report is transmitted (Fig. 1, Fig. 2, el. 230). As such, the rejection is maintained.
Regarding claim 11, on pages 7-8 of applicant’s remarks, applicant argues that Tapia does not explicitly disclose the claim limitation of “receiving additional performance feedback from a network entire;”
In response, the examiner respectfully disagrees. Tapia teaches that the additional performance data [refers as feedback, el. 314, Fig. 3, para. 59-60] are further analyzed in the machine learning model or the modified training data are used to retrain the machine learning model (Fig. 5, el. 516, para. 70-72). As such, the rejection is maintained.
Regarding claim 20, on pages 9-10 of applicant’s remarks, applicant argues that Tapia does not explicitly disclose the claim limitation of “transmitting performance feedback configuration information configuring a user equipment (UE) to generate a set of key performance indicators (KPIs) for a machine learning (ML) model running on the UE; and receiving performance feedback generated by the UE, in accordance with the performance feedback configuration information;”
In response, the examiner respectfully disagrees. Under the broadest reasonable in interpretation under MPEP, the examiner respectfully interprets that the claim limitation of “UE” as el. 120, el. 118, and el. 104 as illustrated in Fig. 1, the UE would include the functionality of performance extrapolation engine and performance analytic engine, and further teaches the claim limitation of use a machine learning model to determine the KPIs of the UE (para. 26). As such, Tapia disclose that the performance of data are transmitted and collected (Fig. 1, el. 304, para. 55), and generates one or more key performance indicators for the performance data (el. 402, para. 61). Further, Tapia teaches that the performance data [refers as feedback, el. 314, Fig. 3, para. 59-60] are received further analyzed in the machine learning model or the modified training data are used to retrain the machine learning model (Fig. 5, el. 516, para. 70-72). As such, the rejection is maintained.
Regarding claim 26, on page 10 of applicant’s remarks, applicant argues that Tapia does not explicitly disclose the claim limitation of “receiving additional performance feedback from a network entire;”
In response, the examiner respectfully disagrees. Tapia teaches that the additional performance data [refers as feedback, el. 314, Fig. 3, para. 59-60] are further analyzed in the machine learning model or the modified training data are used to retrain the machine learning model (Fig. 5, el. 516, para. 70-72). As such, the rejection is maintained.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-32 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tapia (US 2018/0270126 A1).
Regarding claim 1, Tapia discloses a method of wireless communications by a user equipment (UE), comprising:
obtaining a set of key performance indicators (KPIs) for a machine learning (ML) model running on the UE (Fig. 1, para. 26, el. 120, el. 118, and el. 104 as illustrated in Fig. 1, the UE to run the machine learning model to obtain KPI data);
and transmitting, to an entity associated with the ML model, a report including an aggregation (Fig. 3, el. 308) of the KPIs and additional performance feedback for the ML model (the additional performance data [refers as feedback, el. 314, Fig. 3, para. 59-60] are further analyzed in the machine learning model or the modified training data are used to retrain the machine learning model (Fig. 5, el. 516, para. 70-72)).
Regarding claim 2, Tapia discloses receiving a subscription request from the entity associated with the ML model, wherein the report is transmitted to the entity in response to the subscription request (para. 45, the reporting module [el. 230] to provide the report to the subscribers).
Regarding claim 3, Tapia discloses receiving, from a network entity, configuration information configuring the UE to run the ML model (Fig. 5, el. 506, el. 508).
Regarding claim 4, Tapia discloses receiving performance feedback configuration information from a network entity (Fig. 5, el. 508, the performance feedback is referred as training KPI data in view of the specification).
Regarding claim 5, Tapia discloses wherein the performance feedback configuration information indicates at least one of: that the UE is to provide performance feedback for the ML model to the network entity; or the set of KPIs that the UE is to obtain (Fig. 5, el. 508).
Regarding claim 6, Tapia discloses wherein the set of KPIs includes KPIs associated with system performance and KPIs associated with model performance (Fig. 5, el. 506-508).
Regarding claim 7, Tapia discloses reporting performance feedback to the network entity, in accordance with the performance feedback configuration information (Fig. 5, el. 506-516, retraining the KPI training data).
Regarding claim 8, Tapia discloses wherein the performance feedback is reported via at least one of a media access control (MAC) control element (MAC-CE), radio resource control (RRC) signaling, or uplink control information (UCI) (Fig. 1, el. 102, para. 22, it is reported via a uplink control information in an internet network in el. 102).
Regarding claim 9, Tapia discloses wherein the performance feedback is reported with a periodicity indicated by the performance feedback configuration information (para. 61, KPI data are updated periodically).
Regarding claim 10, Tapia discloses wherein the performance feedback is reported in response to one or more event-triggers defined by the performance feedback configuration information (Fig. 4, el. 406-408, para. 43-45, the KPI data report is being updated response to an alert of any changes in the rule).
Regarding claim 11, Tapia discloses receiving the additional performance feedback from a network entity (to generate additional KPI data, para. 61).
Regarding claim 12, Tapia discloses wherein the performance feedback is received periodically (para. 61, KPI data are updated periodically).
Regarding claim 13, Tapia discloses wherein the performance feedback is received in response to one or more configured event-triggers (Fig. 4, el. 406-408, para. 43-45, the KPI data report is being updated response to an alert of any changes in the rule)).
Regarding claim 14, Tapia discloses wherein the performance feedback comprises: training data; and an indication that the ML model is to be retrained using the training data (Fig. 5, el. 514).
Regarding claim 15, Tapia discloses sending a request to receive the additional performance feedback from the network entity (to generate additional KPI data, para. 61, el. 510).
Regarding claim 16, Tapia discloses changing the ML model running on the UE (Fig. 5, i.el, to retrain the ML model).
Regarding claim 17, Tapia discloses wherein the changing the ML model running on the UE is performed in response to an indication from the network entity (Fig. 5, el. 508-516).
Regarding claim 18, Tapia discloses wherein the changing the ML model running on the UE comprises falling back to an ML model that was previously running on the UE (Fig. 5, el. 512-516, this flow chart would include to retrain a ML model using previous KPI data).
Regarding claim 19, Tapia discloses wherein the indication of-from the network entity was transmitted in response to an indication transmitted via UE assistance information (UAI) (Fig. 908, the identification [refers as an indication] of the device features is transmitted to a ML model to be trained).
Regarding claim 20, Tapia discloses method of wireless communications by a network entity, comprising:
transmitting performance feedback configuration information (Fig. 1, el. 108; el. 114) configuring a user equipment (UE) to generate a set of key performance indicators (KPIs) for a machine learning (ML) model running on the UE (the examiner respectfully interprets that the claim limitation of “UE” as el. 120, el. 118, and el. 104 as illustrated in Fig. 1, the UE would include the functionality of performance extrapolation engine and performance analytic engine, and further teaches the claim limitation of use a machine learning model to determine the KPIs of the UE (para. 26), the performance of data are transmitted and collected (Fig. 1, el. 304, para. 55), and generates one or more key performance indicators for the performance data (el. 402, para. 61));
and receiving performance feedback generated by the UE, in accordance with the performance feedback configuration information (the additional performance data [refers as feedback, el. 314, Fig. 3, para. 59-60] are further analyzed in the machine learning model or the modified training data are used to retrain the machine learning model (Fig. 5, el. 516, para. 70-72)).
Regarding claim 21, the instant claim is analyzed with respect to claim 5.
Regarding claim 22, the instant claim is analyzed with respect to claim 6.
Regarding claim 23, the instant claim is analyzed with respect to claim 8.
Regarding claim 24, the instant claim is analyzed with respect to claim 9.
Regarding claim 25, the instant claim is analyzed with respect to claim 10.
Regarding claim 26, Tapia discloses transmitting additional performance feedback (para. 61), generated at the network entity, for the UE to aggregate with the set pf KPIs generated at the UE (Fig. 2, el. 222, para. 37, para. 45).
Regarding claim 27, the instant claim is analyzed with respect to claim 12.
Regarding claim 28, the instant claim is analyzed with respect to claim 13.
Regarding claim 29, the instant claim is analyzed with respect to claim 14.
Regarding claim 30, the instant claim is analyzed with respect to claim 15.
Regarding claim 31, the instant claim is analyzed with respect to claim 17.
Regarding claim 32, the instant claim is analyzed with respect to claim 18.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAI Y CHEN whose telephone number is (571)270-5679. The examiner can normally be reached 8:30 AM -4:30 PM.
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/CAI Y CHEN/Primary Examiner, Art Unit 2425
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