Prosecution Insights
Last updated: April 19, 2026
Application No. 18/073,372

METHOD AND SYSTEM FOR DEPLOYING INFERENCE MODEL

Non-Final OA §103
Filed
Dec 01, 2022
Examiner
WAI, ERIC CHARLES
Art Unit
2195
Tech Center
2100 — Computer Architecture & Software
Assignee
Pegatron Corporation
OA Round
1 (Non-Final)
82%
Grant Probability
Favorable
1-2
OA Rounds
3y 9m
To Grant
99%
With Interview

Examiner Intelligence

Grants 82% — above average
82%
Career Allow Rate
529 granted / 644 resolved
+27.1% vs TC avg
Strong +27% interview lift
Without
With
+27.2%
Interview Lift
resolved cases with interview
Typical timeline
3y 9m
Avg Prosecution
27 currently pending
Career history
671
Total Applications
across all art units

Statute-Specific Performance

§101
15.7%
-24.3% vs TC avg
§103
50.0%
+10.0% vs TC avg
§102
11.4%
-28.6% vs TC avg
§112
14.4%
-25.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 644 resolved cases

Office Action

§103
DETAILED ACTION Claims 1-15 are presented for examination. 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 . Specification The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Claim Rejections - 35 USC § 103 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, 4, 7, 9, and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goli (US PG Pub No. 2020/0356415 A1) in view of Yassin et al. (US PG Pub No. 2020/0336566 A1). Regarding claim 1, Goli teaches a system for deploying an inference model, suitable for deploying an inference model, the system for deploying the inference model comprising: an edge computing device (Fig 1, 112 “Edge Device(s)”); and a model management server communicatively coupled to the edge computing device (Fig 1, 142, “Centralized IoT Manager”), wherein the model management server is configured to: obtain an estimated resource usage of each of a plurality of model settings of the inference model ([0083], wherein “Estimating computational requirements for a given ML model may be approximated based on a number of floating point operations per second (FLOPS) required per request and the memory requirements may be based on a file size of the given ML model”); select one of the model settings as a specific model setting based on deploy the inference model configured with the specific model setting to the edge computing device ([0013], wherein the ML model application is deployed). Goli does not teach obtaining a production requirement for setting model settings as a specific model setting. However, it is old and well known to utilize production requirements for selecting model settings. For example, Yassin teaches configuring models with one or more parameters and constraints specified by a tenant, cloud provider, SLA, i.e. production requirement ([0036]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize production requirements for selecting model settings. One would be motivated by the desire to adhere to performance guarantee for guaranteeing a certain level of service as taught by Yassin ([0001]). Regarding claim 4, Goli teaches wherein the model management server comprises: a model training element for training the inference model ([0092]); and a model inference test element for applying the trained inference model to each of the model settings to perform a pre-inference operation corresponding to each of the model settings, so as to obtain the estimated resource usage and an estimated model performance of each of the model settings ([0013], wherein “Runtime information for the ML model may be determined based on heuristics and statistics collected for similar ML models, which can be estimated based on size”). Regarding claim 7, Goli teaches wherein the edge computing device runs a plurality of reference inference models (Fig 1, 110; [0021]), and the edge computing device comprises: an inference service interface element for receiving at least one request (Fig 1, 161; Fig 2, 270, “ML Inference Service”; [0050]); an inference service database for recording each of the reference inference models and a usage time of each of the reference inference models (Fig 2, 280, “storage”; Fig 2, 261, “runtimes”); a model data management element communicatively coupled to the model management server and configured to store and update each of the reference inference models ([0050], wherein “The 270 may retrieve ML model and inference data 282 from the storage 280”); and an inference service core element for providing an inference service corresponding to the edge computing device and adaptively optimizing or unloading at least one of the reference inference models ([0053]; [0056]). Regarding claims 9 and 12, they are the method claims of claims 1 and 4 above. Therefore, they are rejected for the same reasons as claims 1 and 4 above. Allowable Subject Matter Claims 2-3, 5-6, 8, 10-11, and 13-15 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 ERIC C WAI whose telephone number is (571)270-1012. The examiner can normally be reached Monday - Friday 9-5. 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, Aimee Li can be reached at (571) 272-4169. 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. /Eric C Wai/Primary Examiner, Art Unit 2195
Read full office action

Prosecution Timeline

Dec 01, 2022
Application Filed
Dec 17, 2025
Non-Final Rejection — §103 (current)

Precedent Cases

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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
82%
Grant Probability
99%
With Interview (+27.2%)
3y 9m
Median Time to Grant
Low
PTA Risk
Based on 644 resolved cases by this examiner. Grant probability derived from career allow rate.

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