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 Amendment
The Amendment filed 09/03/2025 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 and Ploegert et al. (US 20210200807 A1) hereinafter Ploegert
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 to the ontology graph 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 to the ontology graph 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 to the ontology graph 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]
As to dependent claim 2, the rejection of claim 1 is incorporated, Liu, Mckibbin and Ploegert 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 and Ploegert 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 and Ploegert 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 and Ploegert 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 and Ploegert 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 and Ploegert 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 and Ploegert 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 and Ploegert teach the method of claim 2 above that is incorporated,
Liu, Mckibbin and Ploegert 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 and Ploegert 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 and Ploegert 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. With respect to the 112/102 rejections these rejections have been withdrawn.
Applicant's arguments filed 09/03/2025. In the remark, applicant argues that:
(1) Bouneffa and Leng fail to teach new language including "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. " 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 and Ploegert.
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.
Shi et al. (US 20200034135 A1) teaches change impact models that analyze historical change data (see ¶30)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
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/BEAU D SPRATT/ Primary Examiner, Art Unit 2143
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