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.
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 1-22 are 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 1-14 are directed to a computer product claim and claims 15-22 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 a similarity estimate is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
and identify the situation event graph as a match to one of the plurality of scenarios based on the similarity estimate. (This step for identifying a situation event graph as a match to scenarios is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgement).)
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, and a knowledge graph associated with the situation event graph into a neural network model, the neural network model including a plurality of scenarios received from a database, wherein the situation event graph represents a situation and each of the plurality of scenarios represents at least two similar situations; (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 similarity estimate) - 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, topology data associated with the situation event graph, and a knowledge graph associated with the situation event graph into a neural network model, the neural network model including a plurality of scenarios received from a database, wherein the situation event graph represents a situation and each of the plurality of scenarios represents at least two similar situations; (This step is directed to receiving 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 similarity estimate) - 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 15: see the rejection of claim 1 above. Same rationale applies.
Regarding Claim 2, 16
2A Prong 1: None
2A Prong 2: update the database by adding the situation event graph to the one of the plurality of scenarios identified as the match by the neural network model. (The step directed to updating information by adding into a model, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
2B: update the database by adding the situation event graph to the one of the plurality of scenarios identified as the match by the neural network model. (This step is directed to updating information by adding into a model, which is understood to be insignificant extra-solution activity and is well understood into a model, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV)))))
Regarding Claim 3, 17
2A Prong 1: None
2A Prong 2: generate and output a visualization to a user interface, the visualization indicating the match between the situation event graph and the one of the plurality of scenarios. (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 match between the situation event graph and the one of the plurality of scenarios. (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 4, 18
2A Prong 1: None
2A Prong 2: receive feedback via the user interface based on the match between the situation event graph and the one of the plurality of scenarios; (The step directed to receiving information, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
and update the neural network model based on the feedback. (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., updating a model based on feedback) - see MPEP 2106.05(f).)
2B: receive feedback via the user interface based on the match between the situation event graph and the one of the plurality of scenarios; (This step is directed to receiving 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)))))
and update the neural network model based on the feedback. (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., updating a model based on feedback) - see MPEP 2106.05(f).)
Regarding Claim 5, 19
2A Prong 1: identify the situation event graph as a new scenario based on the similarity estimate indicating no match between the situation event graph and the plurality of scenarios; (This step for identifying a situation event graph as a new scenario based on the 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: and update the database by adding the new scenario to the plurality of scenarios to form an updated plurality of scenarios. (The step directed to updating the database, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
2B: and update the database by adding the new scenario to the plurality of scenarios to form an updated plurality of scenarios. (This step is directed to updating the database, 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 6, 20
2A Prong 1: None
2A Prong 2: input the updated plurality of scenarios to the neural network model. (The step directed to inputting information, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
2B: input the updated plurality of scenarios to 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 of presenting offers as identified by the court (MPEP 2106.05(d)(ll)(iv)))))
Regarding Claim 7, 21
2A Prong 1: process, (This step for determining a similarity estimate is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
and identify the new situation event graph as a match to one of the updated plurality of scenarios based on the similarity estimate. (This step for identifying a situation event graph as a new scenario based on the 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: input a new situation event graph, new topology data associated with the new situation event graph, and a new knowledge graph associated with the new situation event graph into the neural network model; (The step directed to inputting information, 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., predicting) - see MPEP 2106.05(f).)
2B: input a new situation event graph, new topology data associated with the new situation event graph, and a new knowledge graph associated with the new situation event graph into 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 of presenting offers 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., predicting) - see MPEP 2106.05(f).)
Regarding Claim 8, 22
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: process, by the similarity matching model, the first situation event graph, the first topology data, the first knowledge graph, the second situation event graph, the second topology data, and the second knowledge graph to determine a similarity estimate between the first situation event graph and the second situation event graph; (This step for determining a similarity estimate is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
and create a scenario when the first situation event graph and the second situation event graph match based on the similarity estimate, the scenario including the first situation event graph and the second situation event graph. (This step for creating a scenario based on the similarity estimate is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: 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 first situation event graph, first topology data associated with the first situation event graph, and a first knowledge graph associated with the first situation event graph into a similarity matching model; (The step directed to inputting information, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
input a second situation event graph, second topology data associated with the second situation event graph, and a second knowledge graph associated with the second situation event graph into the similarity matching model; (The step directed to inputting information, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
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.
2B: 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 first situation event graph, first topology data associated with the first situation event graph, and a first knowledge graph associated with the first situation event graph into a similarity matching 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 of presenting offers as identified by the court (MPEP 2106.05(d)(ll)(iv)))))
input a second situation event graph, second topology data associated with the second situation event graph, and a second knowledge graph associated with the second situation event graph into the similarity matching 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 of presenting offers as identified by the court (MPEP 2106.05(d)(ll)(iv)))))
by the similarity matching 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 a similarity estimate) - 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 10
2A Prong 1: None
2A Prong 2: store the scenario in a database as one of a plurality of scenarios. (The step directed to storing information, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
2B: store the scenario in a database as one of a plurality of scenarios. (This step is directed to storing 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 11
2A Prong 1: compare, by the similarity matching model, the similarity estimate to a predictive score to determine a loss function; (This step for comparing estimate to a score in order to determine a loss function is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., identifying).)
2A Prong 2 & 2B: There are no additional elements.
Regarding Claim 12
2A Prong 1: None
2A Prong 2 & 2B: create a neural network model from the similarity matching model to process new situation event graphs as compared to the plurality of scenarios. (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., predicting) - see MPEP 2106.05(f).)
Regarding Claim 13
2A Prong 1: None
2A Prong 2 & 2B: wherein the neural network model comprises a graph neural network (GNN) model. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 14
2A Prong 1: None
2A Prong 2 & 2B: wherein the similarity matching model comprises a supervised learning similarity matching model. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
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) 1-4, 8-10, 15-18, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vutukuru et al. (US 20240202061 A1, hereinafter “Vutukuru”), in view of
Zhou et al. (“Leveraging on causal knowledge for enhancing the root cause analysis of equipment spot inspection failures”, hereinafter “Zhou”).
Regarding Claim 1
Vutukuru 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: ([Vutukuru, Fig 1, 0056-0064] discloses a computing device with memory and instructions)
input a situation event the neural network model including a plurality of scenarios received from a database, ([Vutukuru, 0125, 0144, 0149-0153] discloses inputting vector records (i.e situation event) into a similarity model (i.e neural network), the model including already-identified clusters (i.e plurality of scenarios) received from a database mentioned in [Fig. 8 & 0166]: “Additionally, database 820 may include vector representations of these incident logs and incident solutions, which may be generated from models 852 in central instance 850…”)
wherein the situation event ([Vutukuru, 0151, 0155] discloses a record (i.e situation event) represents an incident (i.e situation) and clusters (i.e plurality of scenarios) representing similar records (i.e similar situations))
process, by the neural network model, the situation event ([Vutukuru, 0144, 0152-0158] discloses using the similarity model (i.e neural network model) to determine a similarity estimate between the record (i.e situation event) and cluster (i.e plurality of scenarios))
and identify the situation event ([Vutukuru, 0152-0158] discloses assigning (i.e identifying) records (i.e situation event) to a cluster (i.e plurality of scenarios) based on its similarity estimates)
Vutukuru does not explicitly disclose: a situation event graph, topology data associated with the situation event graph, and a knowledge graph associated with the situation event graph
However, Zhou discloses: a situation event graph, topology data associated with the situation event graph, and a knowledge graph associated with the situation event graph ([Zhou, Page 3, Fig. 2(b); Page 3, Col 1, Para 1-2; Page 4, Col 2, Para 1; Page 4, Fig. 4] discloses Casual Relation Knowledge 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 graph associated with the Casual Relation Knowledge graph (i.e situation event graph))
Vutukuru and Zhou are analogous art to the present invention because they are from the same field of endeavor directed to machine learning and root cause analysis.
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 program to use similarity scores to identify situations to scenarios disclosed by Vutukuru with the use of a situation event graph, topology, and knowledge graph 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 to mine the root causes. ([Zhou, Abstract; Page 2, Col 1])
Regarding Claim 2
Vutukuru discloses: update the database by adding the situation event ([Vutukuru, Fig. 8, 0149, 0155, 0166] discloses updating the database 820 in Fig. 8 by including (i.e adding) representations of the incident logs and incident solutions (i.e situation event and plurality of scenarios) generated from clustering similar incidents together as a match by a neural network)
However, Vutukuru does not disclose: situation event graph
Zhou discloses: a situation event graph ([Zhou, Page 4, Col 1 & Fig. 4] discloses Casual Relation Knowledge graph (i.e situation event graph))
Vutukuru and Zhou are analogous art to the present invention because they are from the same field of endeavor directed to machine learning and root cause analysis.
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 updating a database with matches between an event and scenarios disclosed by Vutukuru with the use of a situation event graph by Zhou. One of ordinary skill in the art would have been motivated to make this modification in order to store casual data and external knowledge into a situation event graph. ([Zhou, Abstract; Page 2, Col 1])
Regarding Claim 3
Vutukuru discloses: generate and output a visualization to a user interface, the visualization indicating the match between the situation event ([Vutukuru, Fig. 9B-9D, 0173-1075] discloses generating and outputting a graphical interface (i.e visualization to a user interface), indicating a matches between an incident (i.e situation event) and other incidents (i.e plurality of scenarios))
However, Vutukuru does not disclose: situation event graph
Zhou discloses: a situation event graph ([Zhou, Page 4, Col 1 & Fig. 4] discloses Casual Relation Knowledge graph (i.e situation event graph))
Vutukuru and Zhou are analogous art to the present invention because they are from the same field of endeavor directed to machine learning and root cause analysis.
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 updating a database with matches between an event and scenarios disclosed by Vutukuru with the use of a situation event graph by Zhou. One of ordinary skill in the art would have been motivated to make this modification in order to store casual data and external knowledge into a situation event graph. ([Zhou, Abstract; Page 2, Col 1])
Regarding Claim 4
Vutukuru discloses: receive feedback via the user interface based on the match between the situation event ([Vutukuru, Fig. 9B-9D, 0179-0180] discloses receiving user selection (i.e feedback) via the user interface between the situation event and an incident (i.e one of the plurality of scenarios) and retraining the model based on the user selection)
However, Vutukuru does not disclose: situation event graph
Zhou discloses: a situation event graph ([Zhou, Page 4, Col 1 & Fig. 4] discloses Casual Relation Knowledge graph (i.e situation event graph))
Vutukuru and Zhou are analogous art to the present invention because they are from the same field of endeavor directed to machine learning and root cause analysis.
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 updating a database with matches between an event and scenarios disclosed by Vutukuru with the use of a situation event graph by Zhou. One of ordinary skill in the art would have been motivated to make this modification in order to store casual data and external knowledge into a situation event graph. ([Zhou, Abstract; Page 2, Col 1])
Regarding Claim 8
Vutukuru in view of Zhou: wherein the neural network model comprises a graph neural network (GNN) model. ([Zhou, Page 4, Col 2, Para 2] discloses neural network model CompGCN comprises a GCN, a type of GNN model)
Regarding Claim 9
Vutukuru 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: ([Vutukuru, Fig 1, 0056-0064] discloses a computing device with memory and instructions)
input a first situation event ([Vutukuru, 0152-0153] discloses putting in vector A (i.e first situation event) into the similarity model (i.e matching model))
input a second situation event ([Vutukuru, 0152-0153] discloses putting in vector B (i.e second situation event) into the similarity model (i.e matching model))
process, by the similarity matching model, the first situation event([Vutukuru, 0152-0153] discloses processing by the similarity model (i.e matching model) in order to get a similarity measurement (i.e similarity estimate) between the vector A (i.e first situation event) and vector B (i.e second situation event))
and create a scenario when the first situation event ([Vutukuru, 0152-0153, 0155-0157] discloses creating a cluster (i.e scenario) vector A (i.e first situation event) and vector B (i.e second situation event) based on a similarity measurement (i.e similarity estimate), the cluster including the vector A (i.e first situation event) and vector B (i.e second situation event). Clustering techniques disclosed in 0156 generate clusters within a set of records, represented in vectors.)
Vutukuru does not explicitly disclose: a situation event graph, topology data associated with the situation event graph, and a knowledge graph associated with the situation event graph
However, Zhou discloses: a situation event graph, topology data associated with the situation event graph, and a knowledge graph associated with the situation event graph ([Zhou, Page 3, Fig. 2(b); Page 3, Col 1, Para 1-2; Page 4, Col 2, Para 1; Page 4, Fig. 4] discloses Casual Relation Knowledge 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 graph associated with the Casual Relation Knowledge graph (i.e situation event graph))
Vutukuru and Zhou are analogous art to the present invention because they are from the same field of endeavor directed to machine learning and root cause analysis.
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 program to use similarity scores to identify situations to scenarios disclosed by Vutukuru with the use of a situation event graph, topology, and knowledge graph 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 to mine the root causes. ([Zhou, Abstract; Page 2, Col 1])
Regarding Claim 10
Vutukuru in view of Zhou discloses: store the scenario in a database as one of a plurality of scenarios. ([Vutukuru, 0156, 0166] discloses storing the database representations of the incident logs and incident solutions (i.e scenario) generated from clustering similar incidents together (i.e one of a plurality of scenarios). [0166] further explains that outputs from the model doing the clustering calculations are stored in the database: “Additionally, database 820 may include vector representations of these incident logs and incident solutions, which may be generated from models 852 in central instance 850, as discussed above.”)
Regarding Claim 15
(Claim 15 recites analogous limitations to Claim 1 and therefore is rejected on the same ground as Claim 1.)
Regarding Claim 16
(Claim 16 recites analogous limitations to Claim 2 and therefore is rejected on the same ground as Claim 2.)
Regarding Claim 17
(Claim 17 recites analogous limitations to Claim 3 and therefore is rejected on the same ground as Claim 3.)
Regarding Claim 18
(Claim 18 recites analogous limitations to Claim 4 and therefore is rejected on the same ground as Claim 4.)
Regarding Claim 22
(Claim 22 recites analogous limitations to Claim 8 and therefore is rejected on the same ground as Claim 8.)
Claim(s) 5, 6, 7, 19, 20, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vutukuru in view of Zhou et al. and Casnastro et al. (“Root Cause Analysis in 5G/6G Networks”, hereinafter “Casnastro”).
Regarding Claim 5
Vutukuru in view of Zhou discloses: The computer program product of claim 1, wherein the instructions are further configured to cause the at least one computing device to
Vutukuru in view of Zhou does not disclose: identify the situation event graph as a new scenario based on the similarity estimate indicating no match between the situation event graph and the plurality of scenarios; and update the database by adding the new scenario to the plurality of scenarios to form an updated plurality of scenarios.
However, Casnastro discloses: identify the situation event graph as a new scenario based on the similarity estimate indicating no match between the situation event; and update the database by adding the new scenario to the plurality of scenarios to form an updated plurality of scenarios. ([Casnastro, Page 219, Fig. 2; Page 220, Col 2, Para 6; Page 221, Col 1, Para 1] discloses identifying candidate (i.e situation event graph) as a new error ID (i.e new scenario) based on the similarity score, indicating low similarity (i.e no match) between the candidate and anomaly (i.e plurality of scenarios) and update the knowledge base database to form an updated list of anomalies (i.e plurality of scenarios))
Vutukuru, Zhou, and Casnastro are analogous art to the present invention because they are from the same field of endeavor directed to machine learning and root cause analysis.
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 combined the program to use similarity scores to identify situations to scenarios disclosed by Vutukuru in view of Zhou with creating a new scenario to be added to the database when there are no matches by Casnastro. One of ordinary skill in the art would have been motivated to make this modification in order to update the database with new scenarios. ([Casnastro, Page 220, Col 2, Para 6; Page 221, Col 1, Para 1])
Regarding Claim 6
Vutukuru in view of Zhou and Casnastro discloses: input the updated plurality of scenarios to the neural network model. ([Vutukuru, 0166, 0169-170] discloses inputting vector representations of incident logs and incident solutions generated from similarity engine model (i.e the updated plurality of scenarios). [0166] states “Computational instance 810 may also include similarity engine 812 which may receive information from models 852 of central instance 850, as well as data from database 820.” [0170] further states “In further examples, this incident log, the grouping, the assigned incident solution, and other incident logs, groupings, and solutions, may serve as a basis to further train models 852 after a predetermined period of time.” Therefore, similarity engine 812 obtains any updated vector representations stored from models 852 for similarity calculations, as seen in Fig. 8 for the sharing of information. )
Regarding Claim 7
Vutukuru in view of Casnastro discloses: input a new situation event ([Vutukuru, 0179-0180] discloses inputting new data (i.e situation event) into the similarity model for retraining and find-tuning. [0125, 0144, 0149-0153] further explains the process of inputting vector records (i.e situation event) into a similarity model (i.e neural network),)
process, by the neural network model, the new situation event ([Vutukuru, 0151, 0155] discloses a record (i.e situation event) represents an incident (i.e situation) and clusters (i.e plurality of scenarios) representing similar records (i.e similar situations))
and identify the new situation event ([Vutukuru, 0152-0158] discloses assigning (i.e identifying) records (i.e situation event) to a cluster (i.e plurality of scenarios) based on its similarity estimates. [0166, 0169-170] discloses that the cluster is an updated plurality of scenarios because the vector representations of incident logs and incident solutions generated from similarity engine model are inputted again into the model (i.e the updated plurality of scenarios). [0166] further states “Computational instance 810 may also include similarity engine 812 which may receive information from models 852 of central instance 850, as well as data from database 820.” [0170] then states “In further examples, this incident log, the grouping, the assigned incident solution, and other incident logs, groupings, and solutions, may serve as a basis to further train models 852 after a predetermined period of time.” Therefore, similarity engine 812 obtains any updated vector representations stored from models 852 for similarity calculations and thus, using the updated plurality of scenarios, as seen in Fig. 8 for the sharing of information.)
However, Vutukuru in view of Casnastro does not disclose: a new situation event graph, new topology data associated with the new situation event graph, and a new knowledge graph associated with the new situation event graph
Zhou discloses: a new situation event graph, new topology data associated with the new situation event graph, and a new knowledge graph associated with the new situation event graph ([Zhou, Page 3, Fig. 2(b); Page 3, Col 1, Para 1-2; Page 4, Col 2, Para 1; Page 4, Fig. 4] discloses Casual Relation Knowledge 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 graph associated with the Casual Relation Knowledge graph (i.e situation event graph) [Page 6, Col 2, Section 5.3, Para 3] discloses testing the trained model ALBERT with different kinds of knowledge on the dataset. Therefore, it is interpreted that new graph data is used to run the model.)
Vutukuru, Zhou, and Casnastro are analogous art to the present invention because they are from the same field of endeavor directed to machine learning and root cause analysis.
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 new data graph input information disclosed by Vutukuru in view of Casnastro with the use of new graph data as inputs disclosed by Zhou. One of ordinary skill in the art would have been motivated to test the model with new data. ([Zhou, Page 6, Col 2, Section 5.3, Para 3])
Regarding Claim 19
(Claim 19 recites analogous limitations to Claim 5 and therefore is rejected on the same ground as Claim 5.)
Regarding Claim 20
(Claim 20 recites analogous limitations to Claim 6 and therefore is rejected on the same ground as Claim 6.)
Regarding Claim 21
(Claim 21 recites analogous limitations to Claim 7 and therefore is rejected on the same ground as Claim 7.)
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vutukuru et al. in view of Zhou et al. and Ducau et al. (US 20200364338 A1, hereinafter “Ducau”).
Regarding Claim 11
Vutukuru in view of Zhou discloses: The computer program product of claim 10, wherein the instructions are further configured to cause the at least one computing device to
Vutukuru in view of Zhou does not disclose: compare, by the similarity matching model, the similarity estimate to a predictive score to determine a loss function; and update the similarity matching model based on the loss function.
However, Ducau discloses: compare, by the similarity matching model, the similarity estimate to a predictive score to determine a loss function; ([Ducau, 0100, 0103, 0107-0108] discloses comparing by the Joint Embedding model (i.e similarity matching model) the similarity score (i.e similarity estimate) to a cross-entropy loss score for the prediction of each tag (i.e predictive score) to minimize the loss function)
and update the similarity matching model based on the loss function. ([Ducau, 0100, 0103, 0107-0108] discloses updating the Joint Embedding model (i.e similarity matching model) by minimizing the loss function)
Vutukuru, Zhou, and Ducau 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 combined the program to use similarity scores to create a scenario disclosed by Vutukuru in view of Zhou with updating a similarity matching model based on a loss function by Ducau. One of ordinary skill in the art would have been motivated to make this modification in order to minimize the loss function to improve performance. ([Ducau, 0107])
Claim(s) 12, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vutukuru et al. view of Zhou. Ducau, and Kumar et al. (US 20190236679 A1, hereinafter “Kumar”).
Regarding Claim 12
Vutukuru in view of Zhou and Ducau discloses: The computer program product of claim 11
Vutukuru in view of Zhou and Ducau does not disclose: The computer program product of claim 11, wherein the instructions are further configured to cause the at least one computing device to: create a neural network model from the similarity matching model to process new situation event graphs as compared to the plurality of scenarios.
However, Kumar discloses: create a neural network model from the similarity matching model to process new situation event ([Kumar, 0065] discloses creating a trained model (i.e neural network msodel) from a cosine similarity model (i.e similarity matching model) to process affinities (i.e new situation event) as compared to other product brand categories (i.e plurality of scenarios))
Vutukuru, Zhou, Ducau, and Kumar 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 combined the program to use similarity scores to create a scenario disclosed by Vutukuru in view of Zhou and Ducau with creating a new model by Kumar. One of ordinary skill in the art would have been motivated to make this modification in order to have a model to determine similarities for users. ([Kumar, 0065])
However, Kumar does not disclose: a situation event graph
Zhou discloses: a situation event graph ([Zhou, Page 4, Col 1 & Fig. 4] discloses Casual Relation Knowledge graph (i.e situation event graph))
Kumar and Zhou are analogous art to the present invention because they are from the same field of endeavor directed to machine learning and root cause analysis.
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 creating a new model from a similarity matching model disclosed by Kumar with the use of a situation event graph by Zhou. One of ordinary skill in the art would have been motivated to make this modification in order to store casual data and external knowledge into a situation event graph. ([Zhou, Abstract; Page 2, Col 1])
Regarding Claim 13
Vutukuru in view of Zhou, Ducau, and Kumar discloses: wherein the neural network model comprises a graph neural network (GNN) model. ([Zhou, Page 4, Col 2, Para 1] discloses neural network model CompGCN comprises a GCN, a type of GNN model)
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vutukuru in view of
Zhou, and Schmitz (“Product Taxonomy Matching in E-Commerce Environments”)
Regarding Claim 14
Vutukuru in view of Zhou discloses: The computer program product of claim 9
Vutukuru in view of Zhou does not disclose: wherein the similarity matching model comprises a supervised learning similarity matching model.
However, Schmitz discloses: wherein the similarity matching model comprises a supervised learning similarity matching model. ([Schmitz Page 47-48; Page 45, Section 6.1.2; Page 55, Para 1]) discloses supervised learning similarity matching models such as Ontology and AdaBoost)
Vutukuru, Zhou, and Schmitz are analogous art to the present invention because they are from the same field of endeavor directed to machine learning and similarity.
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 program to create scenarios disclosed by Vutukuru in view of Zhou with the use of a supervised learning model by Schmitz. One of ordinary skill in the art would have been motivated to make this modification in order to improve the performance of the model since supervised methods outperform distantly supervised methods. ([Schmitz, Page 16, Para 2])
Conclusion
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/AMANDA D NGUYEN/Examiner, Art Unit 2127
/TEWODROS E MENGISTU/Primary Examiner, Art Unit 2127
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