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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/05/2023 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 2, 7, 8, 9, 12-15, 20, and 21 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 7, 8, 9, 12-15, 20, and 21 of corresponding application No. 18/511,550 in view of Mandal et al. (US 20230105304 A1, hereinafter “Mandal”). The corresponding application (18/511,550) discloses similar limitations, shown in the table below, to the instant application (18/511,543). For claims 1, 9, and 14, the instant application (18/511,543) mentions “determine a priority of the situation” and the corresponding application (18/511,550) mentions “determine a causal impact of the situation.” Specs [00440] applicant discloses that the causal impact are related to each other as priority is used in the causal impact calculations: “The neural network model 4980 outputs a prediction of the causal impact 5208 in terms of predicted event and a predicted incident priority as well as the timing for the predicted event, P((2,4)I, t8).” Additionally, by definition, casual impact is the effect of a situation on an outcome and Specs [00462] applicant discloses that priority is how critical or major the situation is. Therefore, impact and priority are closely associated with each other in terms of calculations and overall meaning and are considered synonymous. The differences between both applications appear to be the in the first limitation stated in claims 1, 9, and 14 of the instant application (18/511,543) stating "historical ticket data.” The limitations are taught by Mandal (Fig. 5A, 0091-0096 see 35 USC 103 rejection below). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the similar limitations taught in the corresponding application (18/511,550) with having a model including and inputting historical ticket data by Mandal. Doing so provides allows a model to take historical incident reports in order to determine the severity of performance issues for incidents. ([Mandal, 0095])
This is a provisional nonstatutory double patenting rejection.
Instant Application: 18/511,543
Corresponding Application: 18/511,550
Claim 1: A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: input a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios and historical ticket data, wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation; and process, by the neural network model, the situation event graph and the corresponding scenario to determine a priority of the situation.
Claim 1: A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: input a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios, wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation; and process, by the neural network model, the situation event graph and the corresponding scenario to determine a causal impact of the situation.
Claim 9: A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: input a situation event graph, topology data associated with the situation event graph, a knowledge graph associated with the situation event graph, and historical ticket data into a neural network model; generate a predicted priority for the situation event graph using the neural network model; compare the predicted priority to an actual priority for the situation event graph to determine a loss; and input the loss as feedback to the neural network model to improve the neural network model.
Claim 9: A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: input a situation event graph, topology data associated with the situation event graph, and a knowledge graph associated with the situation event graph into a neural network model; generate a predicted causal impact for the situation event graph using the neural network model; compare the predicted causal impact to an actual causal impact for the situation event graph to determine a loss; and input the loss as feedback to the neural network model to improve the neural network model.
Claim 14: A computer-implemented method, the computer-implemented method further comprising: inputting a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios and historical ticket data, wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation; and processing, by the neural network model, the situation event graph and the corresponding scenario to determine a priority of the situation.
Claim 14: A computer-implemented method, the computer-implemented method further comprising: inputting a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios, wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation; and processing, by the neural network model, the situation event graph and the corresponding scenario to determine a causal impact of the situation.
Claim 2, 15: The computer program product of claim 1, wherein the situation event graph and corresponding scenario include topology data and knowledge graph data.
Claim 2, 15: The computer program product of claim 1, wherein the situation event graph and corresponding scenario include topology data and knowledge graph data.
Claim 7, 20: The computer program product of claim 1, wherein the situation event graph and the corresponding scenario are grouped as similar based on a similarity estimate.
Claim 7, 20: The computer program product of claim 1, wherein the situation event graph and the corresponding scenario are grouped as similar based on a similarity estimate.
Claim 8, 13, 21: The computer program product of claim 1, wherein the neural network model comprises a graph neural network (GNN) model.
Claim 8, 13, 21: The computer program product of claim 1, wherein the neural network model comprises a graph neural network (GNN) model.
Claim 12: The computer program product of claim 9, wherein the actual priority includes an actual event and an actual priority of the event.
Claim 12: The computer program product of claim 9, wherein the actual causal impact includes an actual event and an actual predicted incident priority.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-13 are directed to a computer program product claim and claims 14-21 are directed to a method claim. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1
2A Prong 1:
process, (This step for determining priority of a situation is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f).)
input a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios and historical ticket data, wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation; (The step directed to inputting information into a model, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
by the neural network model, (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., determine priority) - see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f).)
input a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios and historical ticket data, wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation; (This step is directed to inputting information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV)))))
by the neural network model, (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., determine priority) - see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 2, 15
2A Prong 1: None
2A Prong 2 & 2B: wherein the situation event graph and corresponding scenario include topology data and knowledge graph data. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 3, 16
2A Prong 1: None
2A Prong 2 & 2B: wherein the priority of the situation includes a status indicator for the situation. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 4, 17
2A Prong 1: None
2A Prong 2: generate and output a visualization to a user interface, the visualization indicating the status indicator for the situation. (The step directed to presenting information, is understood to be insignificant extra- solution activity as presenting offer and gathering statistics. See MPEP 2106.05(g).)
2B: generate and output a visualization to a user interface, the visualization indicating the status indicator for the situation. (This step is directed to presenting information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of presenting offers as identified by the court (MPEP 2106.05(d)(ll)(iv)))))
Regarding Claim 5, 18
2A Prong 1: order the situation in relation to other situations using the priority. (This step for ordering a situation using priority is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgement).)
2A Prong 2 & 2B: There are no additional elements.
Regarding Claim 6, 19
2A Prong 1: process(This step for determining priority of a situation is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: input a new situation event graph and a corresponding new scenario into the neural network model, wherein the new situation event graph represents a new situation; (The step directed to inputting information into a model, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
by the neural network model (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., determine priority) - see MPEP 2106.05(f).)
and output and reorder the visualization to include the new situation and the new priority (The step directed to outputting information to a visualization, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
2B: input a new situation event graph and a corresponding new scenario into the neural network model, wherein the new situation event graph represents a new situation; (This step is directed to inputting information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV)))))
by the neural network model (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., determine priority) - see MPEP 2106.05(f).)
and output and reorder the visualization to include the new situation and the new priority (This step is directed to presenting information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of presenting offers as identified by the court (MPEP 2106.05(d)(ll)(iv)))))
Regarding Claim 7, 20
2A Prong 1: wherein the situation event graph and the corresponding scenario are grouped as similar based on a similarity estimate. (This step for grouping situation event graph and scenario based on similarity estimate is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgement).)
2A Prong 2 & 2B: There are no additional elements.
Regarding Claim 8, 21
2A Prong 1: None
2A Prong 2 & 2B: wherein the neural network model comprises a graph neural network (GNN) model. (The specification of model used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 9
2A Prong 1:
generate a predicted priority for the situation event graph (This step for generating priority of a situation is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
compare the predicted priority to an actual priority for the situation event graph to determine a loss; (This step for comparing priority of a situation and determining a loss is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f).)
input a situation event graph, topology data associated with the situation event graph, a knowledge graph associated with the situation event graph, and historical ticket data into a neural network model; (The step directed to inputting information into a model, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
using the neural network (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., determine priority) - see MPEP 2106.05(f).)
and input the loss as feedback to the neural network model to improve the neural network model. (The step directed to inputting information into a model, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f).)
input a situation event graph, topology data associated with the situation event graph, a knowledge graph associated with the situation event graph, and historical ticket data into a neural network model; (This step is directed to inputting information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV)))))
using the neural network (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., determine priority) - see MPEP 2106.05(f).)
and input the loss as feedback to the neural network model to improve the neural network model. (This step is directed to inputting information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV)))))
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 10
2A Prong 1: determine the actual priority for the situation using the historical ticket data. (This step for determining a priority is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., determining a priority).)
2A Prong 2 & 2B: There are no additional elements.
Regarding Claim 11
2A Prong 1: None
2A Prong 2 & 2B: wherein the predicted priority includes a status indicator. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 12
2A Prong 1: None
2A Prong 2 & 2B: wherein the actual priority includes an actual event and an actual priority of the event. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 13
2A Prong 1: None
2A Prong 2 & 2B: wherein the neural network model includes a graph neural network model. (The specification of model used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 14: see the rejection of claim 1 above. Same rationale applies.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 7, 8, 14, 15, 20, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mandal et al. (US 20230105304 A1, hereinafter “Mandal”) in view of Li et al. (US 20190354689 A1, hereinafter “Li”)
Regarding Claim 1
Mandal discloses: A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: ([Mandal, Fig. 7, 0141-0149] discloses a computer system containing processors with memory storing software instructions)
input a situation event graph ([Mandal, 0025, 0032] discloses a casual dependency graph (i.e situation event graph) representing KPIs that represents outliers (i.e situation event), described as events in [0026]. [Specs, 00387] applicant describes a situation event graph as a casual graph that lists all events causally related from each other. [Mandal, 0091] also describes a casual dependency graph having all dependences, causes, and effects of a computing environment, so it represents a situation. [Fig. 5A, 0091-0096] discloses inputting the causal dependency graph (i.e situation event graph) and historical incident reports (i.e historical ticket data) into an incident predictor (i.e model). Inputting historical incident reports indicates that the model includes historical ticket data.)
and process, by the ([Mandal, 0095-0096, Fig. 6E, 0131] discloses processing by the incident predictor (i.e model) a severity of the predicted performance issues for the casual dependency graph (i.e situation event graph))
Mandal does not explicitly disclose: input a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios
However, Li discloses: input a situation event graph and a corresponding scenario into a neural network model ([Li, Fig. 4, 0101] discloses first graph node state representation vectors 404 (i.e situation event graph) and a cross-graph matching vector (i.e corresponding scenario) into a node update neural network 402 (i.e neural network model))
the neural network model including a plurality of scenarios ([Li, Fig. 4 , 0101, 0105] discloses training the neural network model using a dataset of graphs. The inputs of node update neural network 402 (i.e neural network model) are the first graph node state representation vectors 404 (i.e situation event graph) and a cross-graph matching vector (i.e corresponding scenario), so the neural network includes a plurality of cross-graph matching vectors (i.e scenarios). [0095] further describes the corresponding scenario being generated as related to the first graph node state representation vectors 404 (i.e situation event graph): “a similarity between a particular node of the first input graph 101 and one or more nodes of the second input graph” [0104] indicates that the neural network contains these scenarios “generate further cross-graph matching vectors in order to perform additional rounds of updating by the node update neural network 402.”)
wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation; ([Li, 0006-0009] discloses graph representations represents physical systems and entities and the relationships between entities, which represents a situation. [Fig. 4, 0095, 0101] discloses the cross-graph matching vector (i.e corresponding scenario) represents nodes from first graph node state representation vectors and second graph node state representation vectors (i.e plurality of situations similar to situations). First graph node state representation vectors and second graph node state representation vectors are similar based on the matching subsystem 403.)
Mandal and Li are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the computer program to find priority disclosed by Mandal with using a neural network and a corresponding scenario by Li. One of ordinary skill in the art would have been motivated to make this modification in order to use a neural network to predict an output and use a corresponding scenario to enable better modeling from neural networks. ([Li, 0003, 0023])
Regarding Claim 2
Mandal in view of Li discloses: wherein the situation event graph and corresponding scenario include topology data and knowledge graph data. ([Li, Fig. 4, 0101] discloses first graph node state representation vectors 404 (i.e situation event graph) and a cross-graph matching vector (i.e corresponding scenario). [0006-0009] discloses that the graph representations include edges and nodes that represent the relationships, associations, properties, and entities of graph, so it includes a topology data and knowledge graph data)
Regarding Claim 7
Mandal in view of Li discloses: situation event graph and the corresponding scenario are grouped as similar based on a similarity estimate. ([Li, Fig. 1, 0069; Fig. 4, 0095, 0101] discloses grouping the first graph node state representation vectors (i.e situation event graph) and cross-graph matching vector (i.e scenario) as similar based on a similarity between nodes from first graph node state representation vectors and second graph node state representation vectors by the matching subsystem 403.)
Regarding Claim 8
Mandal in view of Li discloses: wherein the neural network model comprises a graph neural network (GNN) model ([Li, 0089-0091; Fig. 4, 0095, 0101] discloses the node update neural network 402 (i.e neural network model) comprises a GNN because it takes in and operate on graph data inputs.)
Regarding Claim 14
(Claim 14 recites analogous limitations to Claim 1 and therefore is rejected on the same ground as Claim 1.)
Regarding Claim 15
(Claim 15 recites analogous limitations to Claim 2 and therefore is rejected on the same ground as Claim 2.)
Regarding Claim 20
(Claim 20 recites analogous limitations to Claim 7 and therefore is rejected on the same ground as Claim 7.)
Regarding Claim 21
(Claim 21 recites analogous limitations to Claim 8 and therefore is rejected on the same ground as Claim 8.)
Claim(s) 3-6, 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Li and in view of Murata et al (US 20250005684 A1, hereinafter “Murata”)
Regarding Claim 3
Mandal in view of Li discloses: predicted priority ([Mandal, 0095-0096, Fig. 6E, 0131] discloses predicting a severity of the predicted performance issues for the casual dependency graph)
Mandal in view of Li does not explicitly disclose: wherein the predicted priority includes a status indicator.
However, Murata discloses: wherein the ([Murata, Fig. 11, 0112-123] discloses a priority (i.e priority) including an status indicator)
Mandal, Li, and Murata are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the computer program to determine a predicted priority disclosed by Mandal in view of Li with having a status indicator associated with the priority by Murata. One of ordinary skill in the art would have been motivated to make this modification in order to for a system and users to keep track of an incident, whether the incident is closed or not, and assign an assignee to address the incidents. ([Murata, 0119])
Regarding Claim 4
Mandal in view of Li and Murata discloses: generate and output a visualization to a user interface, the visualization indicating the status indicator for the situation. ([Murata, Fig. 11, 0123] discloses outputting tickets (i.e situation) in relation to other tickets based on prioirty: “The new ticket created (via the ticket creation GUI 900) may be displayed at the top of the ticket list 1100 with other previously created tickets following in the list (i.e., tickets are displayed in order of most recently created). In another example embodiment, the tickets may be displayed in order of urgency, priority…”)
Regarding Claim 5
Mandal in view of Li and Murata discloses: order the situation in relation to other situations using the priority. ([Murata, 0123] discloses outputting tickets (i.e situation) in relation to other tickets based on priority: “The new ticket created (via the ticket creation GUI 900) may be displayed at the top of the ticket list 1100 with other previously created tickets following in the list (i.e., tickets are displayed in order of most recently created). In another example embodiment, the tickets may be displayed in order of urgency, priority…”)
Regarding Claim 6
Mandal discloses: input a new situation event graph ([Mandal, 0083-0084, Fig. 4, 0089] discloses the PRT system getting real-time data from Data Pre-processor 410 and storing them in Operational Data 420, which indicates new inputs being received. As seen in Fig. 4 and 0089-0091, the PRT contains an Incident Predictor 450A (i.e model) and receives the data to be used as input for an Incident Predictor 450A (i.e model) that makes a new casual dependency graph (i.e situation event graph). [0082, 0137] discloses that it is continuously taking in feedback to improve the models in real time: “Additionally, there is a continuous learning mechanism which takes feedback from past misses of the system and learns to predict them in future, thereby improving the performance of the ML/DL models in the real time.” Therefore, the Incident Predictor 450A (i.e model) will be inputting with new data. [Fig. 1, 0064, 0093-0095] further indicates that casual dependency graph represent KPIs and these KPIs are collected real time. The following discloses inputs and model used for situation event graphs: [0025, 0032] discloses a casual dependency graph (i.e situation event graph) representing KPIs that represents outliers (i.e situation event), described as events in [0026]. [Specs, 00387] applicant describes a situation event graph as a casual graph that lists all events causally related from each other. [Mandal, 0091] also describes a casual dependency graph having all dependences, causes, and effects of a computing environment, so it represents a situation. [Fig. 5A, Fig. 4, 0089, 0091-0096] discloses inputting the causal dependency graph (i.e situation event graph) and historical incident reports (i.e historical ticket data) into an Incident Predictor 450A (i.e model).)
process, by the ([Mandal, 0064] discloses the system getting real-time data, which indicates new inputs being received. The following discloses processing of situation event graphs: [0095-0096, Fig. 6E, 0131] discloses processing by the incident predictor (i.e model) a severity of the predicted performance issues for the casual dependency graph (i.e situation event graph))
However, Mandal does not explicitly disclose: input a new situation event graph and a corresponding new scenario into the neural network model, wherein the new situation event graph represents a new situation;
Li discloses: input a new situation event graph and a corresponding new scenario into the neural network model, wherein the new situation event graph represents a new situation; ([Li, 0105] discloses training the neural network models using a dataset of graphs. [0125] using the neural network models for other technical applications after it is trained: “The system may be trained on data representing molecules and optionally their known properties. The trained system may then be used to identify graphs representing other physically realistic molecules with the same or similar properties to those in the training data e.g., molecules that bind to a particular site and/or families of drugs.” The following discloses inputs and model used for situation event graphs and corresponding scenarios: [Li, Fig. 4, 0101] discloses first graph node state representation vectors 404 (i.e situation event graph) and a cross-graph matching vector (i.e corresponding scenario) into a node update neural network 402 (i.e neural network model))
Mandal and Li are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the computer program to find priority disclosed for new inputs by Mandal with using a neural network and a corresponding new scenario by Li. One of ordinary skill in the art would have been motivated to make this modification in order to use a neural network to predict an output, use a corresponding scenario to enable better modeling from neural networks, and receive new inputs data to apply a trained system to other technical applications. ([Li, 0003, 0023, 0125])
However, Mandal in view of Li does not explicitly disclose: and output and reorder the visualization to include the new situation and the new priority
Murata discloses: and output and reorder the visualization to include the new situation and the new priority. ([Murata, 0123] discloses outputting and reordering the GUI (i.e visualization) to include new tickets (i.e situation) and its new priority that can in order of the priority: “The new ticket created (via the ticket creation GUI 900) may be displayed at the top of the ticket list 1100 with other previously created tickets following in the list (i.e., tickets are displayed in order of most recently created). In another example embodiment, the tickets may be displayed in order of urgency, priority…”)
Mandal, Li, and Murata are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the computer program to find priority with new inputs disclosed by Mandal in view of Li outputting and reordering results by Murata. One of ordinary skill in the art would have been motivated to make this modification in order to output new data and order them. ([Murata, 0123])
Regarding Claim 16
(Claim 16 recites analogous limitations to Claim 3 and therefore is rejected on the same ground as Claim 3.)
Regarding Claim 17
(Claim 17 recites analogous limitations to Claim 4 and therefore is rejected on the same ground as Claim 4.)
Regarding Claim 18
(Claim 18 recites analogous limitations to Claim 5 and therefore is rejected on the same ground as Claim 5.)
Regarding Claim 19
(Claim 19 recites analogous limitations to Claim 6 and therefore is rejected on the same ground as Claim 6.)
Claim(s) 9, 12, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Zhou et al. (“Leveraging on causal knowledge for enhancing the root cause analysis of equipment spot inspection failures”, hereinafter “Zhou”) and in view of Shao et al. (US 20240321077 A1, hereinafter “Shao”).
Regarding Claim 9
Mandal discloses: A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: ([Mandal, Fig. 7, 0141-0149] discloses a computer system containing processors with memory storing software instructions)
input a situation event graph,([Mandal, 0025, 0032] discloses a casual dependency graph (i.e situation event graph) representing KPIs that represents outliers (i.e situation event), described as events in [0026]. [Specs, 00387] applicant describes a situation event graph as a casual graph that lists all events causally related from each other. [Mandal, 0091] also describes a casual dependency graph having all dependences, causes, and effects of a computing environment. [Fig. 5A, 0091-0096] discloses inputting the causal dependency graph (i.e situation event graph) and historical incident reports (i.e historical ticket data) into an incident predictor (i.e model).)
generate a predicted priority for the situation event graph using the ([Mandal, 0095-0096, Fig. 6E, 0131] discloses generating a severity of the predicted performance issues for the casual dependency graph (i.e situation event graph) using the incident predictor (i.e model))
Mandal does not explicitly disclose: input
and a neural network model;
However, Zhou discloses: input a situation event graph, topology data associated with the situation event graph, a knowledge graph associated with the situation event graph, and ([Zhou, Page 4, Fig. 4; Page 4, Col 1, Para 1; Page 4, Col 2, Para 1] discloses three different inputs for a neural network model ALBERT: Casual Relation Knowledge (CRK) graph (i.e situation event graph), entity and relationship information from the problem description (i.e topology data associated with situation event graph), and Spot Inspection Knowledge (SIKG) graph associated with the Casual Relation Knowledge graph (i.e knowledge graph data) into the CausalKG-ALBERT knowledge reasoning model (i.e neural network model))
and a neural network model; ([Zhou, Page 4, Fig. 4; Page 4, Col 1, Para 1; Page 4, Col 2, Para 1] discloses use of CausalKG-ALBERT knowledge reasoning model (i.e neural network model)
Mandal and Zhou are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the computer program to determine priority of a situation event by model disclosed by Mandal with the use of a situation event graph, topology, and knowledge graph and neural network model by Zhou. One of ordinary skill in the art would have been motivated to make this modification in order to model casual data and external knowledge into a situation event graph, topology, and knowledge graph, and then input them into a model for an output. ([Zhou, Abstract; Page 2, Col 1])
Mandal in view of Zhou does not explicitly disclose: compare the predicted priority to an actual priority for the situation event
and input the loss as feedback to the neural network model to improve the neural network model.
However, Shao discloses: compare the predicted priority to an actual priority for the situation event ([Shao, 0072, 0078, 0080] discloses comparing an output result of the severity prediction layer (i.e predicted priority) and a label (i.e actual priority) for a gas leakage (i.e situation event) to determine a loss using a loss function)
and input the loss as feedback to the neural network model to improve the neural network model. ([Shao, 0072, 0078, 0080] discloses using the loss as feedback to the leakage prediction layer of the severity prediction model (i.e neural network model) to make the efficiency of the severity prediction model higher for predicting severity)
Mandal, Zhou, and Shao are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the computer program to determine priority of a situation event by model disclosed by Mandal in view of Zhou with the use of loss to improve the model by Shao. One of ordinary skill in the art would have been motivated to make this modification in order to increase the efficiency of a neural network model. ([Shao, 0080])
Regarding Claim 12
Mandal in view of Zhou and Shao discloses: wherein the actual priority includes an actual event and an actual priority of the event. ([Shao, 0071-0072] discloses a label (i.e actual priority) which corresponds to an actual gas leakage (i.e event) and a label (i.e actual priority) in training a severity prediction model using a loss function, which uses an actual severity or label and predicted severity: “…and an output of the severity prediction model may include the severity of the gas leakage event.”)
Regarding Claim 13
Mandal in view of Zhou and Shao discloses: wherein the neural network model includes a graph neural network model. ([Shao, 0109] discloses the gas supply volume prediction model 520 (i.e neural network) be a graph neural networks model)
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Zhou, Shao, and in view of Wu et al (US 12333470 B1, hereinafter “Wu”)
Regarding Claim 10
Mandal in view of Zhou and Shao discloses: historical ticket data ([Mandal, 0095] discloses inputting the causal dependency graph (i.e situation event graph) and historical incident reports (i.e historical ticket data) into an incident predictor (i.e model).
Mandal in view of Zhou and Shao does not explicitly disclose: wherein the instructions are further configured to cause the at least one computing device to: determine the actual priority for the situation using the historical ticket data.
However, Wu discloses: wherein the instructions are further configured to cause the at least one computing device to: determine the actual priority for the situation using the historical ([Wu, Col 11, Lines 15-21, Lines 53-57] discloses determining training labels (i.e actual priority) for road water accumulation (i.e situation) using historical data. The labels are the risk for road water accumulation (i.e situation): “The training labels may be determined based on the historical road water accumulation.”)
Mandal, Zhou, Shao, and Wu are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have training a model with a loss function for historical ticket data disclosed by Mandal in view of Zhou and Shao with finding an actual priority using historical data by Wu. One of ordinary skill in the art would have been motivated to make this modification in order to use the actual priority as input into a model for training. ([Wu, Col 11, Lines 56-67, Col 12, Lines 1-3])
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Zhou, Shao, and in view of Murata et al (US 20250005684 A1, hereinafter “Murata”)
Regarding Claim 11
Mandal in view of Zhou and Shao disclose: predicted priority ([Mandal, 0095-0096, Fig. 6E, 0131] discloses predicting a severity of the predicted performance issues for the casual dependency graph)
Mandal in view of Zhou and Shao does not explicitly disclose: wherein the predicted priority includes a status indicator.
However, Murata discloses: wherein the ([Murata, Fig. 11, 0112-123] discloses a priority (i.e priority) including a status indicator)
Mandal, Zhou, and Murata are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the predicted priority disclosed by Mandal in view of Zhou with having a status indicator associated with the priority by Murata. One of ordinary skill in the art would have been motivated to make this modification in order to for a system and users to keep track of an incident, whether the incident is closed or not, and assign an assignee to address the incidents. ([Murata, 0119])
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amanda D. Nguyen whose telephone number is (571)270-1854. The examiner can normally be reached M-F, 7:30am to 5:00 pm ET First Fridays off, 2nd Friday 7:00 am - 4:00 pm ET.
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/AMANDA D NGUYEN/Examiner, Art Unit 2127
/TEWODROS E MENGISTU/Primary Examiner, Art Unit 2127
