Prosecution Insights
Last updated: April 19, 2026
Application No. 18/844,157

MACHINE LEARNING MODEL PERFORMANCE MONITORING REPORTING

Non-Final OA §102
Filed
Sep 05, 2024
Examiner
CHEN, CAI Y
Art Unit
2425
Tech Center
2400 — Computer Networks
Assignee
Qualcomm Incorporated
OA Round
1 (Non-Final)
72%
Grant Probability
Favorable
1-2
OA Rounds
2y 9m
To Grant
81%
With Interview

Examiner Intelligence

Grants 72% — above average
72%
Career Allow Rate
570 granted / 789 resolved
+14.2% vs TC avg
Moderate +9% lift
Without
With
+9.0%
Interview Lift
resolved cases with interview
Typical timeline
2y 9m
Avg Prosecution
19 currently pending
Career history
808
Total Applications
across all art units

Statute-Specific Performance

§101
6.9%
-33.1% vs TC avg
§103
46.7%
+6.7% vs TC avg
§102
26.1%
-13.9% vs TC avg
§112
5.8%
-34.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 789 resolved cases

Office Action

§102
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 . 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, UE is refers as el. 120+el. 104, 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 of the KPIs and additional performance feedback for the ML model (Fig. 1, el. 116, Fig. 2, el. 230, the reporting module of the computing device to receive the report, para. 45, para. 61, the performance feedback is referred as the KPI training data as described in the specification; the additional KPI data are generated thru an update). 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 (Fig. 1, para. 26, UE is refers as el. 120+el. 104, the UE to run the machine learning model to obtain KPI data); and receiving performance feedback generated by the UE, in accordance with the performance feedback configuration information (Fig. 5, el. 506, el. 508; the reporting module of the computing device to receive the report with respect to the KPI data, para. 45, para. 61). 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 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. 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, Brian Pendleton can be reached at 571-272-7527. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CAI Y CHEN/ Primary Examiner, Art Unit 2425
Read full office action

Prosecution Timeline

Sep 05, 2024
Application Filed
Mar 05, 2026
Non-Final Rejection — §102 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
72%
Grant Probability
81%
With Interview (+9.0%)
2y 9m
Median Time to Grant
Low
PTA Risk
Based on 789 resolved cases by this examiner. Grant probability derived from career allow rate.

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