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 .
Continued Examination Under 37 CFR 1.114
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 26th 2026 has been entered.
Response to Amendment
3. The Amendment filed on January 26th 2026 has been entered. Claims 1 and 11 have been amended, claims 5 and 15 have been cancelled and claims 1 – 4, 6 – 14 and 16 - 20 are pending in the application.
Response to Arguments
35 U.S.C. §103
4. Applicant's arguments, see Remarks pp. 9 -13, filed January 26th 2026, with
respect to the rejections of claims 1 – 4, 6 – 14 and 16 - 20 under 35 U.S.C. §103 have been fully considered and they are persuasive.
The crux of applicant’s arguments is that the amendment to the independent claims are not taught by the art of record
Examiner respectfully agrees
Upon further consideration new grounds of rejection have been necessitated due
to Applicant's amendments and are made in view of Balalau et al., “SubRank: Subgraph Embeddings via a Subgraph Proximity Measure” hereinafter Balalau
Claim Rejections – 35 U.S.C. §103
5. 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.
6. The factual inquiries set forth in Graham v John Deere Co., 383 U.S. 1, 148 USPQ
459 (1966), that are applied for establishing a background for determining obviousness
under 35 U.S.C. 103 are summarized as follows:
a. Determining the scope and contents of the prior art
b. Ascertaining the differences between the prior art and the claims at issue
c. Resolving the level of ordinary skill in the pertinent art
d. Considering objective evidence present in the application indicating
obviousness or nonobviousness
Claims 1, 3, 7 - 11, 13, 17, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Settle et al. (United States Patent Publication Number 20230289252), in view of She et al., (United States Patent Publication Number 20230088676) hereinafter She and in further view of Balalau et al., “SubRank: Subgraph Embeddings via a Subgraph Proximity Measure” hereinafter Balalau
Regarding claim 1 Settle teaches a computer-implemented method (method [0021]) for finding historically similar incidents(Fig. 4, (420) plurality of historical system fault data associated with the plurality of system fault events [0037]) such as “historically similar incidents” in a system, (Fig. 1, a computing system event grouping environment 100 [0024]) the method (method [0021]) comprising: receiving a plurality of historical data objects (plurality of historical data 125 [0025]) corresponding to (associated with [0025]) a plurality of previous events, (plurality of computing system fault events 130 [0025]) each of the plurality of historical data objects (plurality of historical data 125 [0025]) indicating an occurrence of a previous event (It should be noted that an event may or may not indicate something anomalous and may be a point-in-time, immutable statement about the entity in question; however, for the purpose of the disclosure events may also include alerts and stories representing ongoing anomalous conditions and context of issues associated with the applicable system. [0025]) and being associated with (associated with [0025]) a corresponding (corresponding [0028]) line of business data object; (computing node [0024]) such as “line of business data object” determining (determined [0027]) a plurality of historical embedding vectors (Fig. 4, (440) generating a plurality of vectors [0040]) for the plurality of historical data objects; (plurality of historical data 125 [0025]) receiving (receives [0037]) a current data object (particular node [0025]) indicating an occurrence of a current incident (incident [0025]) associated with (associated with [0039]) a configurable item, (Fig 6 any one of the components computers [0047]) such as “a smart phone, a computer system, PDA, or other electronic devices. Examples of computing systems, environments, and/or configurations that may represented by data processing system 602, 604 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, network PCs, minicomputer systems and distributed cloud computing environments” [0048] the current data object (particular node [0025]) being associated with(associated with [0025]) a line of business data object; (computing node [0024]) such as “line of business data object” and determining a set of historically similar incidents (one or more similarities are determined [0027]) by applying a Euclidean distance formula (measures include the Euclidean distance [0027]) to the feature embedding vector (nexus vector [0026]) such as “feature embedding vector” and the plurality of historical embedding vectors (plurality of vectors 150 generated based on plurality of historical data [0026])
Settle does not fully disclose determining a configurable item graph including one or more subtrees, wherein the configurable item graph is a graph of logical associations of the line of business data object and related IT operation events; determining an amount of events that occurred within a set period of time for each of the one or more subtrees;
She teaches determining a configurable item graph (pattern graph [0073]) such as “configurable item graph” including one or more subtrees, (decomposed into a set of subgraphs [0097]) such as “one or more subtrees” wherein the configurable item graph(pattern graph [0073]) such as “configurable item graph” is a graph of logical associations (edges [0062]) such as “logical associations” of the line of business data object (atomic operations [0085]) such as “line of business data object” and related IT operation events; (The atomic operation may describe a sequence of system-generated events). [0062]) such as “related IT operation events” determining an amount of events that occurred within a set period of time for each of the one or more subtrees; (typically an atomic operation is some small set of common actions used by more than one process. Based on those sequences, one or more statistical methods are then applied to find a set of atomic operations, which are shared by all ( or a given subset of the) processes, and that are representative of sequences. Representative statistical methods include, without limitation, frequency analysis, and co-occurrence
analysis. [0089])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Settle to incorporate the teachings of She wherein determining a configurable item graph including one or more subtrees, wherein the configurable item graph is a graph of logical associations of the line of business data object and related IT operation events; determining an amount of events that occurred within a set period of time for each of the one or more subtrees. By doing so a deep neural network (DNN) is applied to learn intentions from labeled data, taking the vectors generated as step 1210 as input. She [0089]
Balalau teaches determining a set of subtrees (Given a graph G = (V, E) and set of subgraphs of G, S = {S1 , S2 , • • • , Sk}, we learn their representations as dense vectors, i.e. as embeddings Page 4) with a most amount of events that occurred; (denser networks Cithep and DBLP, with ego networks of size 1 Page 7) generating embeddings for only the set of subtrees with the most amount of events; (SUB2VEC [1] computes subgraph embeddings and for the experimental evaluation, we compute the embeddings of the ego networks. Using the guidelines of the authors, for Cora, Citeseer and Polblogs we select ego networks of size 2 and for the denser networks Cithep and DBLP, ego networks of size 1 Page 7) computing a feature embedding vector for the current data object by averaging the embeddings for each subtree of the set of subtrees with the most amount of events; (VERSEAVG is a adaption of VERSE, in which the embedding of a node is
the average of the VERSE embeddings of the nodes in its ego network. Page 7 )and determining a set of historically similar incidents (similarity between pairs of graphs Page 3) by applying a Euclidean distance formula (a graph proximity measure such as graph edit distance Page 3) such as “a Euclidean distance formula” to the feature embedding vector (subgraph embedding Page 2) and the plurality of historical embedding vectors (precomputed Pagerank vectors of subgraphs {S1, S2, … Sk} Page 4 ) such as “plurality of historical embedding vectors”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Settle in view of She to incorporate the teachings of Balalau wherein determining a set of subtrees with a most amount of events that occurred; generating embeddings for only the set of subtrees with the most amount of events; computing a feature embedding vector for the current data object by averaging the embeddings for each subtree of the set of subtrees with the most amount of events; and determining a set of historically similar incidents by applying a Euclidean distance formula to the feature embedding vector and the plurality of historical embedding vectors. By doing so the authors propose to learn node embeddings such that the embeddings preserve an input similarity distribution between nodes. Balalau Page 4
Claims 11 and 18 correspond to claim 1 and are rejected accordingly
Regarding claim 3 Settle in view of She and Balalau teaches the method of claim 1,
Settle as modified further teaches wherein the plurality of historical data objects (plurality of historical data 125 [0025]) corresponding (corresponding [0039]) line of business data object (computing node [0024]) such as “line of business data object” is associated with (associated with [0025]) the current data object’s(particular node [0025]) line of business data object (computing node [0024]) such as “line of business data object”
Claims 13 and 20 correspond to claim 3 and are rejected accordingly
Regarding claim 7 Settle in view of She and Balalau teaches the method of claim 1,
Settle as modified further teaches including: determining a similarity score (similarity measure [0027]) such as “similarity score” for each of the set of historically similar incidents (Fig. 4, (420) plurality of historical system fault data associated with the plurality of system fault events [0037]) such as “historically similar incidents” based on an application of the Euclidean distance formula (measures include the Euclidean distance [0027])
Claim 17 corresponds to claim 7 and is rejected accordingly
Regarding claim 8 Settle in view of She and Balalau teaches the method of claim 7,
Settle as modified further teaches further including: determining that the similarity score (similarity measure [0027]) such as “similarity score” for each of the set of historically similar incidents (Fig. 4, (420) plurality of historical system fault data associated with the plurality of system fault events [0037]) such as “historically similar incidents” is above (exceeding [0033]) a threshold value (event threshold score [0033]) and outputting (provided to the user [0039]) the historically similar incident (Fig. 4, (420) plurality of historical system fault data associated with the plurality of system fault events [0037]) such as “historically similar incidents” to a user (user [0039])
Regarding claim 9 Settle in view of She and Balalau teaches the method of claim 8,
Settle as modified further teaches further including: saving (For example, data source 115, alone or in combination with server 120, may collect … any other
ascertainable data associated with the issue and/or solution pertaining to an event [0025]) the historically similar incidents (Fig. 4, (420) plurality of historical system fault data associated with the plurality of system fault events [0037]) such as “historically similar incidents” with a value above (exceeding [0033]) the threshold value (event threshold score [0033]) to storage (storage within data source 115 [0025]) SEE ALSO paragraph [0037]
Regarding claim 10 Settle in view of She and Balalau teaches the method of claim 1,
Settle as modified further teaches wherein a description of the current data object is not utilized (in some embodiments, textual data of summaries is used [0025]) to determine the set of historically similar incidents. (Fig. 4, (420) plurality of historical system fault data associated with the plurality of system fault events [0037]) such as “historically similar incidents”
Claims 2, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Settle et al. (United States Patent Publication Number 20230289252), in view of She et al., (United States Patent Publication Number 20230088676) hereinafter She, in view of Balalau et al., “SubRank: Subgraph Embeddings via a Subgraph Proximity Measure” hereinafter Balalau and in further view of Poghosyan et al., (United States Patent Publication Number 20200183769) hereinafter Poghosyan
Regarding claim 2 Settle in view of She and Balalau teaches the method of claim 1,
Settle does not fully disclose wherein the one or more subtrees are determined by applying clustering techniques on events that occurred within a set period of time prior to the current incident and the events are logically associated with the line of business data object.
Poghosyan teaches wherein the one or more subtrees (Figs, 24 A - F one or more decision trees [0099]) such as “subtrees” SEE EXAMPLE subtree in [0089], [0085] are determined by applying clustering techniques (employ a modified K-means clustering technique or another clustering technique [0069]) on events (system events [0057]) that occurred (queued events [0098]) within a set period of time (periodic intervals [0059]) prior to (continuously collected and processed [0069]) the current incident (incident [0099]) and the events (events [0098]) are logically associated with (associated with [0090]) the line of business data object (Fig. 10 (“VCC nodes” [0014], [0052]) )
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Settle in view of She and Balalau to incorporate the teachings of Poghosyan wherein the one or more subtrees are determined by applying clustering techniques on events that occurred within a set period of time prior to the current incident and the events are logically associated with the line of business data object. By doing so one or more of various types of machine-learning techniques to the training dataset in order to generate an abnormal-observation detector that can
be used to detect, in real time, abnormal metric-data observations as they are generated within the distributed computing system. Poghosyan [0004]
Claims 12 and 19 correspond to claim 2 and are rejected accordingly
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Settle et al. (United States Patent Publication Number 20230289252), in view of She et al., (United States Patent Publication Number 20230088676) hereinafter She, in view of Balalau et al., “SubRank: Subgraph Embeddings via a Subgraph Proximity Measure” hereinafter Balalau and in further view of Leiderfarb et al. (United States Patent Publication Number 2017 /0171230), hereinafter referred to as Leiderfarb
Regarding claim 4 Settle in view of She and Balalau teaches the method of claim 1,
Settle as modified does not fully disclose wherein the configurable item graph includes a subset of associations for all configurable items that includes the line of business data object and further includes all events that occurred for the configurable items associated with the line of business data object.
Leiderfarb teaches wherein the configurable item graph (Fig. 5 generalized tree graph [0031]) such as “configurable item graph” SEE ALSO Fig 6 normalized tree graph [0032] includes(includes [0083]) a subset (part of it [0150]) such as “subset” of associations (links [0040], [0041], [0044], [0045], [0150]) see examples [0046] – [0061]for all configurable items (computer components [0076], [0135], MS Word®, MS Excel® [0085], servers [0134], [0135], [0138]) such as “configurable items” that includes (includes [0083]) the line of business data object (Fig. 7 computer/node A – C [0135]) such as “line of business data object” and further includes (includes [0083])all events (events [0090]) that occurred (that occur [0082]) for the configurable items (computer components [0076], [0135], MS Word®, MS Excel® [0085], servers [0134], [0135], [0138]) such as “configurable items” associated with (associated with [0080]) the line of business data object (Fig. 7 computer/node A – C [0135]) such as “line of business data object”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Settle in view of She and Balalau to incorporate the teachings of Leiderfarb wherein the configurable item graph includes a subset of associations for all configurable items that includes the line of business data object and further includes all events that occurred for the configurable items associated with the line of business data object. By doing so having identified the infected machine, the attack tree is again denormalized, replacing the generalized data with specific artifacts on the machine. Leiderfarb [0151]
Claim 14 corresponds to claim 4 and is rejected accordingly
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Settle et al. (United States Patent Publication Number 20230289252), in view of She et al., (United States Patent Publication Number 20230088676) hereinafter She, in view of Balalau et al., “SubRank: Subgraph Embeddings via a Subgraph Proximity Measure” hereinafter Balalau and in further view of Nguyen et al., (United States Patent Publication Number 20210049236) hereinafter Nguyen
Regarding claim 6 Settle in view of She and Balalau teaches the method of claim 1,
Settle does not fully disclose wherein the embeddings for each of the one or more subtrees is generated by a transformer with a graph attention network (GAT) encoder.
Nguyen teaches wherein the embeddings (Fig. 3B, (311) embeddings [0054]) for each of the one or more subtrees (g-rooted subtree [0069]) is generated (generated [0034], [0052], [0058]) by a transformer (tree transformer [0015]) with a graph attention network (GAT) (attention-based mechanism that encodes trees in a bottom-up manner [0021]) encoder (encoder [0021])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Settle in view of She and Balalau to incorporate the teachings of Nguyen wherein the embeddings for each of the one or more subtrees is generated by a transformer with a graph attention network (GAT) encoder. By doing In this way, by encoding trees in a bottom-up manner, the proposed model can leverage attention mechanism to achieve high efficiency and performance and is applicable to self-attention and encoder-decoder attention in the Transformer sequence-to-sequence skeleton. Nguyen [0023]
Claim 16 corresponds to claim 6 and is rejected accordingly
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure.
Liu et al., (United States Patent Publication Number 20090282032) teaches “The retrieval system then calculates a subtree feature for subtrees that have an identified document as their root. The subtree feature is a combination of a contribution of that feature derived from the root document along with a contribution of that feature derived from the descendant documents
of that root document [0009]”
8. Any inquiry concerning this communication or earlier communications from the
examiner should be directed to Kweku Halm whose telephone number is (469) 295-
9144. The examiner can normally be reached on 7:30AM - 5:30PM Mon - Thur. If
attempts to reach the examiner by telephone are unsuccessful, the examiner's
supervisor, Sanjiv Shah can be reached on (571) 272-4098. The fax phone
number for the organization where this application or proceeding is assigned is 571-273-
8300.
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/KWEKU WILLIAM HALM/Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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