Prosecution Insights
Last updated: October 02, 2026
Application No. 17/660,143

ONTOLOGY CHANGE GRAPH PUBLISHING SYSTEM

Non-Final OA §103
Filed
Apr 21, 2022
Examiner
SPRATT, BEAU D
Art Unit
2100
Tech Center
2100 — Computer Architecture & Software
Assignee
Optum Inc.
OA Round
3 (Non-Final)
79%
Grant Probability
Favorable
3-4
OA Rounds
0m
Est. Remaining
99%
With Interview

Examiner Intelligence

Grants 79% — above average
79%
Career Allowance Rate
360 granted / 457 resolved
+23.8% vs TC avg
Strong +24% interview lift
Without
With
+24.3%
Interview Lift
resolved cases with interview
Typical timeline
3y 0m
Avg Prosecution
32 currently pending
Career history
478
Total Applications
across all art units

Statute-Specific Performance

§101
12.6%
-27.4% vs TC avg
§103
65.4%
+25.4% vs TC avg
§102
10.6%
-29.4% vs TC avg
§112
5.7%
-34.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 457 resolved cases

Office Action

§103
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/28/2026 has been entered. Response to Amendment The Amendment filed 08/20/2026 has been entered. Claims 8, 17 and 19-20 are canceled and claims 21-24 are new. Claims 1-7, 9-16, 18 and 21-24 are now pending in the application. Allowable Subject Matter Claims 5-6, 10 and 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. 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 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 of this title, 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, 2 ,7, 9, 11 and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over LIU et al. (US 20080294644 A1) hereinafter Liu in view of Mckibbin et al. (US 20220083578 A1) hereinafter Mckibbin, Ploegert et al. (US 20210200807 A1) hereinafter Ploegert and Subramanian et al. (US 10445170 B1) hereinafter Subramanian. As to independent claim 1, Liu teaches a computer-implemented method comprising: receiving, from a memory stream in communication with a first computing system and by a publisher provided by one or more processors of a second computing system, a change graph that represents a change to an ontology graph and [publisher, server (Fig. 3) with processor, memory (¶168) with OWL graph ¶18 "a server for a semantic publish-subscribe system, comprises: a subscription maintenance module for receiving and storing subscriptions that are described in OWL graph patterns from a plurality of subscribers"] the publisher is configured to provide a common forum between the first computing system and one or more subscribing devices without a direct communication channel between the first computing system and the one or more subscribing devices, and [subscribers receive from publishers via a server (match server) separate from publishers ¶4, ¶10 "server receives an event from a publisher, the server expands the OWL assertions of the event using a reasoner to produce an expanded event, the server matches the expanded event to a subscription, and then, the server notifies a subscriber whose interest matches the event." ] Liu does not specifically teach metadata for the change graph, the metadata comprising an impact score of the change graph, wherein: the impact score is generated by the first computing system based on applying at least one of a deterministic rule or a machine learning technique to the change graph, and transmitting, by the publisher and in response to determining that the impact score satisfies a threshold impact score, a notification to the one or more subscribing devices, the notification comprising the metadata and instructions to cause the one or more subscribing devices to update local copies of the ontology graph based on the change graph, the local copies being used to facilitate one or more searches. However, Mckibbin teaches metadata for the change graph, the metadata comprising an impact score of the change graph, wherein: [impact score for changes on a graph ¶25-28 "aggregate risk associated with a change in a particular object towards all other objects in the QMS can be referred to as the impact score"] the impact score is generated by the first computing system based on applying at least one of a deterministic rule or a machine learning technique to the change graph, [sematic rules, categorization, ML to determine metadata ¶22, score based on risk metadata ¶43] transmitting, by the publisher and in response to determining that the impact score satisfies a threshold impact score, a notification to the one or more subscribing devices, the notification comprising the metadata and instructions to cause the one or more subscribing devices to update local copies of the ontology graph based on the change graph, the local copies being used to facilitate one or more searches. [Alerts/notification based on score and stats and updates accordingly ¶29-30 "server 100 can transmit an alert or notification user device 106 of changes in the impact score of one or many objects and generate visual representations reflecting the temporal variation of such statistics over time"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the Graph subscribing disclosed by Liu by incorporating the metadata for the change graph, the metadata comprising an impact score of the change graph, wherein: the impact score is generated by the first computing system based on applying at least one of a deterministic rule or a machine learning technique to the change graph, and transmitting, by the publisher and in response to determining that the impact score satisfies a threshold impact score, a notification to the one or more subscribing devices, the notification comprising the metadata and instructions to cause the one or more subscribing devices to update local copies of the ontology graph based on the change graph, the local copies being used to facilitate one or more searches disclosed by Mckibbin because both techniques address the same field of graph analysis and by incorporating Mckibbin into Liu better quantifies the changes in graphs making uses more aware of the impacts [Mckibbin ¶5-7] Liu and Mckibbin do not specifically teach the publisher is associated with a specialty that contains a portion of the ontology graph impacted by the change and the one or more subscribing devices subscribed to the publisher; However, Ploegert teaches the publisher is associated with a specialty that contains a portion of the ontology graph impacted by the change and the one or more subscribing devices subscribed to the publisher; [change feed topic (specialty) with modifications to a graph (impacted portions) ¶24, subscribers to change feed ¶29 "add the change feed event to a change feed topic, wherein the one or more consuming applications are subscribed to the change feed topic and receive the change feed event in response to the change feed event being added to the change feed topic"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the graph subscribing disclosed by Liu and Mckibbin by incorporating the publisher is associated with a specialty that contains a portion of the ontology graph impacted by the change and the one or more subscribing devices subscribed to the publisher disclosed by Ploegert because all techniques address the same field of graph analysis and by incorporating Ploegert into Liu and Mckibbin allow a more dynamic scalable graphing with shared knowledge [Ploegert ¶2, ¶5]. Liu, Mckibbin, Ploegert and Subramanian do not specifically teach However, Subramanian teaches Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the impact score disclosed by Liu, Mckibbin, Ploegert and Subramanian by incorporating disclosed by Subramanian because all techniques address the same field of graph analysis and by incorporating Subramanian into Liu, Mckibbin, Ploegert and Subramanian [Subramanian]. As to dependent claim 2, the rejection of claim 1 is incorporated, Liu, Mckibbin, Ploegert and Subramanian further teach retrieving, by the first computing system, a change made by a user to a local copy of the ontology graph: [Ploegert receive modifications ¶24, broker external ¶51] determining, by the first computing system, the change made by the user to a copy of the ontology graph stored by the first computing system to generate the change graph; [Ploegert records modifications and creates change feed ¶110 removes/adds nodes ¶419] generating, by the first computing system, the impact score for the change graph by applying a graph machine learning model to a dataset comprising the change graph; and [Mckibbin impact score for changes on a graph ¶25-28] storing, by the first computing system, the change graph and the impact score in the memory stream. [Ploegert stores in a topic in a cloud ¶172], [Mckibbin systems with score ¶48] As to dependent claim 7, the rejection of claim 2 is incorporated, Liu, Mckibbin, Ploegert and Subramanian further teach wherein generating the change graph comprises: inserting, by the first computing system, a node into the change graph, wherein the node corresponds to the change; and [Mckibbin addition (insert) new objects ¶28] attaching, by the first computing system, a node property label to the node indicating a type of the change. [Mckibbin attributes, labels ¶30] As to dependent claim 9, the rejection of claim 1 is incorporated, Liu, Mckibbin, Ploegert and Subramanian further teach wherein determining that the impact score satisfies the threshold impact score comprises determining that the impact score for the change graph does not exceed the threshold impact score. [Mckibbin low/minimal scores ¶43] As to dependent claim 11, the rejection of claim 1 is incorporated, Liu, Mckibbin, Ploegert and Subramanian further teach wherein the impact score corresponds to a predicted severity level of the change on one or more systems downstream of the ontology graph. [Mckibbin risk (severity level) ¶26] As to dependent claim 22, the rejection of claim 18 is incorporated, Liu, Mckibbin, Ploegert and Subramanian further teach determine that the impact score satisfies the threshold impact score by determining that the impact score does not exceed the threshold impact score. [Mckibbin low/minimal scores ¶43] As to dependent claim 23, the rejection of claim 18 is incorporated, Liu, Mckibbin, Ploegert and Subramanian further teach retrieve a change made by a user to a local copy of the ontology graph;[Ploegert receive modifications ¶24, broker external ¶51] generate the change graph based on the change made by the user to the local copy of the ontology graph; [Ploegert records modifications and creates change feed ¶110 removes/adds nodes ¶419]generate the impact score for the change graph by applying a graph machine learning model to a dataset comprising the change graph; and [Mckibbin impact score for changes on a graph ¶25-28] store the change graph and the impact score in the memory stream. [Ploegert stores in a topic in a cloud ¶172], [Mckibbin systems with score ¶48] Claims 3-4, 14 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Liu, Mckibbin, Ploegert and Subramanian as applied in the rejection of claim 2 above, and further in view of Chen et al. (US 20210256355 A1) hereinafter Chen. As to dependent claim 3, Liu, Mckibbin, Ploegert and Subramanian teach the method of claim 2 above that is incorporated, Liu, Mckibbin, Ploegert and Subramanian do not specifically teach wherein the graph machine learning model comprises a graph convolutional network, and wherein applying the graph machine learning model to the dataset comprises using the graph convolutional network to perform a graph classification technique on the dataset. However, Chen teaches wherein the graph machine learning model comprises a graph convolutional network, and [GCN ¶54] wherein applying the graph machine learning model to the dataset comprises using the graph convolutional network to perform a graph classification technique on the dataset. [datasets ¶100, classifies node dataset ¶54-56 "node classification, edge classification, graph classification, and link prediction."] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the graph subscribing disclosed by Liu, Mckibbin, Ploegert and Subramanian by incorporating the wherein the graph machine learning model comprises a graph convolutional network, and wherein applying the graph machine learning model to the dataset comprises using the graph convolutional network to perform a graph classification technique on the dataset disclosed by Chen because all techniques address the same field of graph manipulation and by incorporating Chen into Liu, Mckibbin, Ploegert and Subramanian automates classification and provides further understanding of graph data and how it evolves [Chen ¶54]. As to dependent claim 4, the rejection of claim 3 is incorporated, Liu, Mckibbin, Ploegert and Chen further teach using the graph convolutional network to perform the graph classification technique on the dataset comprises performing the graph classification technique using a neural network model; and [Chen classifies node dataset ¶54-56 "node classification, edge classification, graph classification, and link prediction."] Regarding claims 12-14, 16, 18 and 24, claims 12 and 18 are non-transitory machine-readable storage medium claims and system claims respectively that correspond to the method of claim 1. Therefore, claims 12-14, 16 18 and 24 are rejected for at least the same reasons as method counterpart claims above. Further see Liu computers with medium and logic (¶20). Response to Arguments Applicant's arguments filed 09/03/2025. In the remark, applicant argues that: (1) Lium Mckibbin and Ploegert fail to teach " the impact score indicating a numerical representation of a potential impact of the change on the ontology graph," as recited in amended claim 1 and discussed over interview. As to point (1), Applicant’s arguments with respect to claim 1 have been considered but are moot in view of a new ground of rejection as set forth above of Liu in view of Mckibbin, Ploegert and Subramanian. Note that applicants own specification doesn’t restrict impact score solely to structural alteration of the ontology itself. Graph can be used to determine predicted severity on downstream systems. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action. Chitalia et al. (US 10560309 B1) teaches an impact score for alerts based on a dependency graph (see Col. 37-38 ln. 56-9). It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (EST). 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, Jennifer Welch can be reached at 571 272 7212. The fax phone number for the organization where this application or proceeding is assigned is 571 483 7388. 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. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866 217 9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800 786 9199 (IN USA OR CANADA) or 571 272 1000. /BEAU D SPRATT/ Primary Examiner, Art Unit 2143
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Prosecution Timeline

Show 6 earlier events
Jun 22, 2026
Final Rejection mailed — §103
Jul 06, 2026
Interview Requested
Jul 20, 2026
Examiner Interview Summary
Jul 20, 2026
Applicant Interview (Telephonic)
Aug 20, 2026
Response after Non-Final Action
Aug 28, 2026
Request for Continued Examination
Sep 02, 2026
Response after Non-Final Action
Sep 22, 2026
Non-Final Rejection mailed — §103 (current)

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Prosecution Projections

3-4
Expected OA Rounds
79%
Grant Probability
99%
With Interview (+24.3%)
3y 0m (~0m remaining)
Median Time to Grant
High
PTA Risk
Based on 457 resolved cases by this examiner. Grant probability derived from career allowance rate.

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