Prosecution Insights
Last updated: July 17, 2026
Application No. 18/594,008

EPISTEMIC UNCERTAINTY REDUCTION USING SIMULATIONS, MODELS AND DATA EXCHANGE

Non-Final OA §103
Filed
Mar 04, 2024
Priority
Oct 28, 2015 — CIP of 14/925,974 +48 more
Examiner
BAYOU, YONAS A
Art Unit
2499
Tech Center
2400 — Computer Networks
Assignee
Qomplx LLC
OA Round
3 (Non-Final)
84%
Grant Probability
Favorable
3-4
OA Rounds
5m
Est. Remaining
99%
With Interview

Examiner Intelligence

Grants 84% — above average
84%
Career Allowance Rate
728 granted / 864 resolved
+26.3% vs TC avg
Strong +15% interview lift
Without
With
+15.3%
Interview Lift
resolved cases with interview
Typical timeline
2y 10m
Avg Prosecution
18 currently pending
Career history
879
Total Applications
across all art units

Statute-Specific Performance

§101
2.8%
-37.2% vs TC avg
§103
56.0%
+16.0% vs TC avg
§102
29.7%
-10.3% vs TC avg
§112
1.7%
-38.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 864 resolved cases

Office Action

§103
DETAILED ACTION This Office Action is in response to Applicant’s Arguments /Remarks and RCE filed on 05/04/2026. As per instant Amendment, claims 6-17 have been added; claims 1 and 3 have been amended; claims 1 and 3 are independent claims. Claims 1-17 have been examined and are pending. This Action is made non-FINAL. 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 . Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/04/2026 has been entered. Response to Arguments Applicant’s arguments, see the above Applicant’s Arguments /Remarks, filed 05/04/2026, with respect to the rejection(s) of claims 1-5 under 35 U.S.C. 103 as being unpatentable over Watts, Pub. No.: US 2008/0027690 in view of Stojanovic et. al., (hereinafter Stojanovic), Pub. No.: US 2016/0092557 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made under 35 U.S.C. 103 as being unpatentable over Watts, Pub. No.: US 2008/0027690 in view of Bishop, Pub. No.: US 2017/0075721. 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. Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Watts, Pub. No.: US 2008/0027690 in view of Bishop, Pub. No.: US 2017/007572. Referring to claim 1, Watts teaches a system for epistemic uncertainty reduction using simulations, models and data exchange, comprising (abstract, para. 0041): a plurality of computing devices each comprising at least a processor, a memory, and a network interface; wherein a plurality of programming instructions stored in one or more of the memories and operating on one or more of the processors of the plurality of computing devices causes the plurality of computing devices to (para. 0041, 0053, assessment system): receive a plurality of initial conditions from external data sources (paras. 0012, 0021, 0051 and 0062, criteria (including initial condition….); see also para. 0077); perform a comparison by comparing at least a portion of the plurality of initial conditions against a plurality of configuration rules (para. 0012, comparing; paras. 0064-0068, comparing and verifying); define a scenario model using a model definition language based the plurality of initial conditions and the comparison (paras. 0050-0053, given a scenario….); perform the following steps in close-loop iterative process until a pre-defined optimization goal for a scenario outcome is achieved (abstract, paras. 0051, 0057-0068 and claim 1, processes the models/parameters to obtain the result): perform a simulation using the scenario model (paras. 0057-0060); produce a scenario outcome based on results of the simulation; and in response to the scenario outcome, automatically modify a plurality of parameters of the scenario model based on the scenario outcome to generate an updated set of parameters for a subsequent simulation (paras. 0057-0067-0068, a result of the HAM run of a given scenario; see also paras. 0050-0053). Watt does not explicitly disclose upon achievement of the optimization goal, send the updated set of the plurality of parameters to a distributed computational graph for use in managing execution of a real-time process, wherein the distributed computational graph defines one or more contexts for one or more activities constituting a pipeline foe executing the real-time process. However, in an analogous art, Bishop teaches upon achievement of the optimization goal, send the updated set of the plurality of parameters to a distributed computational graph for use in managing execution of a real-time process, wherein the distributed computational graph defines one or more contexts for one or more activities constituting a pipeline foe executing the real-time process. (Abstract; paras. 0053, big-data platform; fig. 2, 208, 210 214; paras. 0037 and 0038; paras. 0110 and fig. 6A, transform 604 and 606 send the processed information to transformer 608). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to combine the teachings of Watts with the method and system of Bishop, wherein upon achievement of the optimization goal, send the updated set of the plurality of parameters to a distributed computational graph for use in managing execution of a real-time process, wherein the distributed computational graph defines one or more contexts for one or more activities constituting a pipeline foe executing the real-time process to provide generally to a processing framework for stream processing systems, and in particular to providing an improved stream processing framework that uses a combination of concurrent and multiplexed processing (Bishop: paras. 0005). Referring to claim 2, Watts and Bishop teach the system of claim 1. Bishop further teaches wherein at least some of the pluralities of initial conditions are received from the distributed computational graph and are based on previously provided parameters that have been processed by the distributed computational graph (abstract, paras. 0037-0038; para.: 0053 and fig. 2; para. 0110 and fig. 6). Referring to claim 3, This claim is similar in scope to claim 1, and is therefore rejected under similar rationale. Referring to claim 4, This claim is similar in scope to claim 2, and is therefore rejected under similar rationale. Referring to claim 5, This claim is similar in scope to claim 1, and is therefore rejected under similar rationale. Referring to claim 6, Watts and Bishop teach the system of claim 1. Bishop further teaches wherein the execution of the real-time process managed by the distributed computational graph is expressed using a declarative formalism (Bishop: para. 0087 and fig. 1, Orchestration 112 utilizes a declarative and visual programming model that generates a data entry columnar 114 which accepts declarative and drag-drop input). Referring to claim 7, Watts and Bishop teach the system of claim 6. Bishop further teaches wherein the declarative formalism defines an appropriateness of at least one model or system condition for a task (Bishop: paras. 0087-0088 and fig. 1). Referring to claim 8, Watts and Bishop teach the system of claim 6. Bishop further teaches wherein the pipeline for executing the real-time process is generated by the distributed computational graph based on the declarative formalism (Bishop: paras. 0053, 0087-0088 and fig. 1). Referring to claim 9, Watts and Bishop teach the system of claim 1. Bishop further teaches wherein the one or more contexts includes a data context, a streaming context, or a batch context (Bishop: paras. 0061 and fig. 1; para. 0069, batch container 108). Referring to claim 10, Watts and Bishop teach the system of claim 1. Bishop further teaches wherein the one or more contexts include a specific data model used by the pipeline for executing the real-time process (Bishop: paras. 0055, 0062 and figs. 1-2). Referring to claim 11, Watts and Bishop teach the system of claim 10. Bishop further teaches wherein the specific data model is selected based on a determined suitability for the one or more activities constituting the pipeline (Bishop: para. 0062 and fig. 2). Referring to claim 12, This claim is similar in scope to claim 6, and is therefore rejected under similar rationale. Referring to claim 13, This claim is similar in scope to claim 7, and is therefore rejected under similar rationale. Referring to claim 14, This claim is similar in scope to claim 8, and is therefore rejected under similar rationale. Referring to claim 15, This claim is similar in scope to claim 9, and is therefore rejected under similar rationale. Referring to claim 16, This claim is similar in scope to claim 10, and is therefore rejected under similar rationale. Referring to claim 17, This claim is similar in scope to claim 11, and is therefore rejected under similar rationale. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached PTO-892. Any inquiry concerning this communication or earlier communications from the examiner should be directed to YONAS A BAYOU whose telephone number is (571)272-7610. The examiner can normally be reached Monday-Friday 7AM-4PM. 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, Philip Chea can be reached at 571-272-3951. 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. /YONAS A BAYOU/Primary Examiner, Art Unit 2499 05/18/2026
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Prosecution Timeline

Mar 04, 2024
Application Filed
Sep 08, 2025
Non-Final Rejection mailed — §103
Dec 08, 2025
Response Filed
Feb 04, 2026
Final Rejection mailed — §103
May 04, 2026
Request for Continued Examination
May 12, 2026
Response after Non-Final Action
May 22, 2026
Non-Final Rejection mailed — §103 (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

3-4
Expected OA Rounds
84%
Grant Probability
99%
With Interview (+15.3%)
2y 10m (~5m remaining)
Median Time to Grant
High
PTA Risk
Based on 864 resolved cases by this examiner. Grant probability derived from career allowance rate.

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