DETAILED ACTION
This Office Action is in response to communications filed on March 13th, 2026 for Application
No. 18/030,292, in which claims 1-18 are presented for examination. The amendments March 13th, 2026 have been entered, where claims 1-2, 7, 11, and 13-15 are amended and claims 17-18 are added.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-18 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Claim 1, the claim recites “to merge equivalent entities or to insert relations between pairs of equivalent entities” (ln. 8-9). The term “equivalent” is a relative term which renders the claim indefinite. The term “equivalent” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As a result, it is not clear what “entities” qualify as “equivalent”, which in turn makes the scope of the “merge” and “insert relations” limitations indefinite. As a result, the claim is rejected. The claim should be amended to establish a standard for ascertaining the requisite degree necessary for “equivalent”.
Regarding Claims 2-14, the claims are rejected because they are dependent on a rejected claim.
Regarding Claim 15, the claim recites the term “equivalent” (ln. 8-9), which is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 1. As such, the claim is similarly rejected and should be amended in a similar manner.
Regarding Claim 16, the claim is rejected because it is dependent on a rejected claim.
Regarding Claims 17-18, the claims recite the term “equivalent” (Claim 17, ln. 2; Claim 18, ln. 2), which is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 1. As such, the claims are similarly rejected and should be amended in a similar manner.
Additionally, the claims are rejected because they are dependent on a rejected claim.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a process claim. Therefore, Claims 1-14 and 16-18 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, steps of the claimed process are mental processes. Specifically, the claim recites
“A method for knowledge-based process support . . . the method comprising . . . exploiting semantic information contained in the process events or event log by computing a representation of the events, the process model, and semantic information as a common knowledge graph, wherein the common knowledge graph is modified by a knowledge base (KB) matcher to merge equivalent entities or to insert relations between pairs of equivalent entities” (mental process - amounts to exercising judgment to form an opinion on a representation of information in the form of a graph, which may include exercising judgment to merge or establish relationships between equivalent entities, with reference to an intended use of process support and known or observed information, which may be aided by pen and paper) and
“using the common knowledge graph . . . in support of a process” (mental process – amounts to exercising judgement to form an opinion, with reference to an intended use of process support, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein a process model is related to the process . . . related to process events” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea)
“receiving an event log of event data” (receiving a log of data amounts to insignificant extra-solution activity because gathering and transmission of data is incidental to the claimed subject matter); and
“by a data mining tool . . . in graph-based machine learning” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein a process model is related to the process . . . related to process events” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept)
“receiving an event log of event data” (data gathering is well-understood, routine and conventional, see OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, and transmitting data is well‐understood, routine, and conventional, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; see also buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration); and
“by a data mining tool . . . in graph-based machine learning” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-14 and 16-18. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 2 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the event log data . . . is received as a result of process mining” (receiving a log of data amounts to insignificant extra-solution activity because gathering and transmission of data is incidental to the claimed subject matter) and
“of event data related to the process events” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the event log data . . . is received as a result of process mining” (data gathering is well-understood, routine and conventional, see OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, and transmitting data is well‐understood, routine, and conventional, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; see also buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration) and
“of event data related to the process events” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 2 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 3:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 3 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the support comprises prediction of at least one future event and/or at least one attribute of such a future event or future events” (mental process – amounts to exercising judgement to form an opinion on a future event of features of a future event, with reference to a known or observed representation of information and a known intended use, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 3 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 4:
Step 2A Prong 1: See the rejection of Claim 3 above, which Claim 4 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the at least one attribute is used to allocate resources prior to the usage of the resources by a future event or future events” (mental process – amounts to exercising judgement to form an opinion on features of a future event, with reference to a known or observed representation of information and a known intended use of resource allocation for usage by the event, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 4 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 5:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 5 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the support comprises a prescription and/or recommendation of a future process behavior and/or future event and/or future activity” (mental process – amounts to exercising judgement to form an opinion on a prescription or recommendation of a future behavior, event, or activity, with reference to a known or observed representation of information and a known intended use of process support, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 5 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 6:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 6 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the support is used in automated decision making and/or predictive resource allocation for the process” (mental process – amounts to exercising judgement to form an opinion on future decisions or future resource allocation, which may happen automatically depending on an individual’s knowledge base and experience, with reference to a known or observed representation of information and a known intended use of process support, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 6 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 7:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 7 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the method comprises combining an event flow graphifier with the knowledge base (KB) matcher” (mental process - amounts to exercising judgment to form opinions on the relationships and matches between known or observed information, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 7 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 8:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 8 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein computing the representation comprises a use of an event flow graphifier wherein events or raw events and a process model graph are represented as graph nodes” (mental process - amounts to exercising judgment to form opinions on the relationships and matches between known or observed information, in order to determine a specific formulation of a graph representation of specific information, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 8 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 9:
Step 2A Prong 1: See the rejection of Claim 8 above, which Claim 9 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the events or raw events and the process model graph are inter-linked via at least one common activity type and/or via a mapping of event sequences conforming to the process model” (mental process - amounts to exercising judgment to form an opinion on the representation of known or observed information, wherein the opinion includes a determination of a connection between subcomponents of the observed data based on observed or evaluated common attributes or an inter-data mapping, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 9 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 10:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 10 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein an event flow graph is created from the event log and a process model graph” (mental process - amounts to exercising judgment to form opinion on a representation of known or observed information, in the form of a specific graph structure, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 10 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 11:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 11 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the knowledge base (KB) matcher is applied to an event flow graph and context knowledge base to create the common knowledge graph or a knowledge and flow graph (KnFG)” (mental process - amounts to exercising judgment to form opinion on a representation of known or observed information, in a specific graph structure, by determining matches between a known or observed data representation and known or observed information, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 11 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 12:
Step 2A Prong 1: See the rejection of Claim 11 above, which Claim 12 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the event flow graph and the knowledge base are unified using the knowledge base (KB) matcher” (mental process – amounts to exercising judgement to determine the connections between known or observed information in a graph, by forming opinion on a matching relationship between the components of the known or observed information, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 12 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 13:
Step 2A Prong 1: See the rejection of Claim 11 above, which Claim 13 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the knowledge graph or knowledge and flow graph (KnFG) is a . . . representation of recorded event flows” (mental process – amounts to exercising judgement to form an opinion on a representation of known or observed recorded event information, of the form of a specific type of graph, which may be aided by pen and paper) and
“a generalization of the recorded event flows in the form of a process model and the semantic information” (mental process – amounts to exercising judgment to form an opinion on a generalization of known or observed data representations, in a form consistent with known or observed models and information, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“machine-readable” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“machine-readable” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Accordingly, Claim 13 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 14:
Step 2A Prong 1: See the rejection of Claim 11 above, which Claim 14 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the knowledge graph or knowledge and flow graph (KnFG) admits application . . . link prediction, node classification and node attribute prediction in the graph” (mental process – amounts to exercising judgement to evaluate a known or observed graph representation of information in order to form opinions on links, nodes, and node attributes of the graph, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“of the graph-based machine learning for” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“of the graph-based machine learning for” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Accordingly, Claim 14 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 15:
Step 1: Claim 15 is a machine claim. Therefore, Claim 15 is directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, the claim recites limitations that are substantially the same as the limitations of Claim 1. As a result, and as elaborated above, these limitations are abstract ideas because they are mental processes.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A system . . . the system comprising one or more processors configured to: . . . in graph-based machine learning . . .” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea) and
“wherein a process model is related to the process” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“A system . . . the system comprising one or more processors configured to: . . . in graph-based machine learning . . .” (mere instructions to apply the exception using generic computer components does not provide an inventive concept) and
“wherein a process model is related to the process” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
For the reasons above, Claim 15 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 16:
Step 2A Prong 1: See the rejection of Claim 12 above, which Claim 16 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the knowledge base identifies equivalence relations between entities of inputs provided by the event flow graph and the knowledge base” (mental process – amounts to exercising judgement to form opinions on matches and relationships between known or observed information, which identifies equivalence between the information, with reference to known or observed representations of information, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 16 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 17:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 17 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the common knowledge graph is modified by the knowledge base (KB) matcher to merge equivalent entities” (mental process – amounts to exercising judgement to form opinions on equivalent values that should be considered a merged whole, with reference to known or observed representations of information, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 17 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 18:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 18 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the common knowledge graph is modified by the knowledge base (KB) matcher to insert relations between pairs of equivalent entities” (mental process – amounts to exercising judgement to form opinions on relations between equivalent values, with reference to known or observed representations of information, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 18 is rejected as being directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 5-18 are rejected under 35 U.S.C. 103 as being unpatentable over Okoye et al. (hereinafter Okoye) (“Ontology: Core Process Mining and Querying Enabling Tool”) in view of Ying et al. (hereinafter Ying) (“GNNExplainer: Generating Explanations for Graph Neural Networks”) and Drury et al. (hereinafter Drury) (“Causation Generalization Through the Identification of Equivalent Nodes in Causal Sparse Graphs Constructed from Text using Node Similarity”).
Regarding Claim 1, Okoye teaches a method for knowledge-based process support, wherein a process model is related to the process, the method comprising (Pg. 150, Para. 3, “The design of the semantic-based process mining approach is primarily constructed on the following building blocks as shown in Figure 1”; Pg. 150, Fig. 1, where the “Proposed Framework for . . . process mining and querying method” is a method comprising “Process Models”; see also Pg. 146-147, Para. 5-3, “this work explores the technological potentials and prospects in using ontology as a core process mining and querying enabling tool . . . the conceptual method of analysis provides an easy way to analyse the datasets (i.e. the event logs and models) . . . in order to make available inference knowledge that could be utilized to determine useful patterns . . . to support the discovery, monitoring and enhancement of real-time processes”, where the “method” “utilize[s]” “inference knowledge” to “support . . . real-time processes”; and Pg. 145, Abstract, “process models derived from mining the various data stored in many information systems . . . from the different domain processes”, where the “process models” are related to the “domain processes”):
receiving an event log of event data related to process events (Pg. 150, Fig. 1, where the event log of event data, “Event Data Logs”, which are related to process events, see Pg. 146, Para. 3, “events log is capable of providing vital and valuable information . . . For example, revealing the underlying relationships the process elements or resources share”, are acted on by the “Process Mining Algorithms”, which requires a receiving of the “events log”);
exploiting semantic information contained in the process events or event log by computing a representation of the events, the process model, and semantic information (Pg. 150, Fig. 1, where the “Proposed Framework for the semantic-based (ontology) process mining and querying method” exploits data to compute a representation in the form of a “graph” of “Conceptual Information & Enhanced Process”, see Pg. 155, Para. 3-4, “the defined concepts and process descriptions as explained in the steps above means that the semantic annotation is also another essential component in realizing such an ontology-based approach . . . Essentially, semantic annotation(SemAn) is defined formally as a function that returns a set of concepts from the ontology for each node or edge in the graph”; see also Pg. 153-154, Algorithm 1, where a graph is output, “Output: Semantic annotated graphs/labels & an ontology-driven search for process models and explorative analysis”, which is calculated based on the events and the process model, “4: Developing ontology from process models and event logs . . . For all process models M and event log EV”, as well as semantic information contained in the “Classes”, “For each class C ← semantically analyse the extracted relationships (R) to state facts i.e. Axioms (A) . . . create the semantic schema by adding the extracted relationships and individuals to the ontology”, which are exploited from the event log, “Extract Classes C ← from M and EV”)
as a common knowledge graph (Fig. 1, where the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of a graph, first in the form of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”),
wherein the common knowledge graph is . . . [created] by a knowledge base (KB) matcher . . . (Fig. 1, where, as discussed above, the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”; Pg. 156-157, “Algorithm 2”, where the algorithm to convert the event flow graph into a common knowledge graph through “Reasoning over Ontologies and Classification of Entities and Outputs” is within the broadest reasonable interpretation of a knowledge base matcher, see also Pg. 156, Para. 1, “the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process”, where the knowledge base includes the “underlying information”, such as the “instances (individuals)” available to the “reasoner”); and
using the common knowledge graph in graph-based . . . [reasoning] for the support of the process (Pg. 156, Para. 3, “Algorithm 2: Reasoning over Ontologies and Classification of Entities and Outputs”, where, as discussed above, the event flow diagram is converted into a common knowledge graph when algorithm 2 is executed as a computer program, “Output: classified classes, process instances and attributes”, which can be utilized to perform graph-based “semantic reasoning” to support the process by performing “automatic classification and/or retrieval of the process instances (entities) within the ontology”, see Pg. 163, Para. 2-3, “automatic classification and/or retrieval of the process instances (entities) within the ontology . . . DQ1. Is DefineTopic an Activity of the first Milestone (DefineTopicArea)? DL Query: ActivityConcept and is ActivityType Of some DefineTopicArea == the DL query checks if the activity of the first Milestone equal to Define Topic, thus compares the activity of the first Milestone DefineTopicArea with Activity Concept (DefineTopic)”; see also Pg. 156, Para. 1-2, “the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model. This means that based on the process description (i.e. assertions) within the domain ontology, the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process. Accordingly, the following Algorithm 2 describes how this study makes use of the reasoner to classify and infer the necessary associations to produce the outputs”).
Okoye does not explicitly disclose . . . modified . . . to merge equivalent entities or insert relations between pairs of equivalent entries . . . (where the knowledge base (KB) matcher is not specifically described as modifying the common knowledge graph, such as to process equivalent entries)
machine learning . . . (where the graph-based reasoning is not specifically described as machine learning).
However, Ying teaches . . . [using graph-based] machine learning [to model relational data] . . . (Pg. 1, Para. 1, “Graphs are powerful data representations but are challenging to work with because they require modeling of rich relational information as well as node feature information . . . To address this challenge, Graph Neural Networks (GNNs) have emerged as state-of-the-art for machine learning on graphs, due to their ability to recursively incorporate information from neighboring nodes in the graph, naturally capturing both graph structure and node features”).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the use of the knowledge graph in graph-based reasoning for the support of the process of Okoye with the use of graph-based machine learning to model relational data of Ying in order to leverage the recursive abilities of state-of-the-art machine learning to identify important graph pathways and highlight relevant node feature information (Ying, Pg. 9, Para. 6, “We show how GNNEXPLAINER can leverage recursive neighborhood-aggregation scheme of graph neural networks to identify important graph pathways as well as highlight relevant node feature information that is passed along edges of the pathways”; see also Ying, Pg. 1, Para. 1, “Graphs are powerful data representations but are challenging to work with because they require modeling of rich relational information as well as node feature information . . . To address this challenge, Graph Neural Networks (GNNs) have emerged as state-of-the-art for machine learning on graphs, due to their ability to recursively incorporate information from neighboring nodes in the graph, naturally capturing both graph structure and node features”; see also Ying, Pg. 2, Para. 5, “GNNEXPLAINER can provide important domain insights by robustly identifying important graph structures and node features that influence a GNN’s predictions”), which is more accurate than alternative approaches, see Ying, Pg. 1, Abstract, “Experiments on synthetic and real-world graphs show that our approach can identify important graph structures as well as node features, and outperforms alternative baseline approaches by up to 43.0% in explanation accuracy”).
Additionally, Drury teaches [a method where a graph is] . . . modified . . . to merge equivalent entities or insert relations between pairs of equivalent entries . . . (Pg. 1, Col. 1, Abstract, “This paper proposes to reduce sparseness by merging: equivalent nodes and their edges”; see also Pg. 1, Col. 2, Para. 2, “The merged node process is demonstrated in Figures 1 and 2. The figures demonstrate two candidates nodes for merging B and B#. The two candidates have very similar node names as well as common neighbours C and A. The merge process joins the two nodes into one node B[B#] which combines the neighbours of the previous two graphs”, where the “merging process” inserts a common “neighbour” relation, see Pg. 2, Col. 1, Fig. 1-2, “D”, “C”, and “E”, between pairs pf equivalent entries, “B[B#]”, and/or alternatively inserts an equivalence relation, “B[B#]”, between pairs of equivalent entries, “B” and “B#”).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the utilization of the common knowledge graph in graph-based machine learning to support a process, wherein the common knowledge graph is created by a knowledge base matcher of Okoye in view of Ying with the modification of a graph to merge equivalent entities and to insert relations between pairs of equivalent entries of Drury in order to increase the effectiveness of graph-based reasoning by modifying the common knowledge graph to reduce sparseness (Drury, Pg. 1, Col. 1, Abstract, “Causal Bayesian Graphs can be con structed from causal information in text. These graphs can be sparse because the cause or effect event can be expressed in various ways to represent the same information. This sparseness can corrupt inferences made on the graph. This paper proposes to reduce sparseness by merging: equivalent nodes and their edges”), which will uncover hidden relationships missing from the unmodified common knowledge graph (compare Okoye, Pg. 156, Fig. 4 and Okoye, Pg. 156, Para. 1, “To this end, the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model. This means that based on the process description (i.e. assertions) within the domain ontology, the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class”, where the common knowledge graph organizes elements through “classes and relationships” based on text “assertions”, with Drury, Pg. 1, Col. 2, Para. 1, “A major disadvantage of constructing graphs from text is that the same assertions can be stated in various different ways . . . The consequence of varying language is that the graph generated from it can have many nodes, that have one edge, consequently accurate inference may be difficult due to the sparse structure of the graph . . . An approach to minimize this characteristic of text built graphs is to merge similar nodes and their edges. This will improve the graph by . . . inferring new causes or effects for events which are not explicitly stated in the text the graph is constructed from”, where “constructing graphs from text . . . assertions” can obfuscate relevant connections between entities, which can be uncovered by “merg[ing] similar nodes and their edges”).
Regarding Claim 2, Okoye in view of Ying and Drury teach the method according to claim 1, wherein the event log data of the event data related to the process events is received (Okoye, Pg. 150, Fig. 1, where the event log of event data, “Event Data Logs”, which are related to process events, see Okoye, Pg. 146, Para. 3, “events log is capable of providing vital and valuable information . . . For example, revealing the underlying relationships the process elements or resources share”, are acted on by the “Process Mining Algorithms”, which requires a receiving of the “events log”)
as a result of process mining (Okoye, Pg. 150, Fig. 1, where the “Proposed Framework for the semantic-based (ontology) process mining and querying method” requires a computer system, “Framework for . . . process mining”, which provides process mining of the event log data by providing the “Event Data Logs” to its internal system components and algorithms “Process Mining Algorithms”; see also Okoye, Pg. 153, Para. 2, “the study applies the process mining techniques in order to make available the process mappings for the learning process, and check its conformance with the event logs based on the Fuzzy Miner”).
Regarding Claim 3, Okoye in view of Ying and Drury teach the method according to claim 1, wherein the support comprises prediction of at least one future event and/or at least one attribute of such a future event or future events (Okoye, Pg. 146-147, Para. 5-3, “this work explores the technological potentials and prospects in using ontology as a core process mining and querying enabling tool . . . the conceptual method of analysis provides an easy way to analyse the datasets (i.e. the event logs and models) . . . in order to make available inference knowledge that could be utilized to determine useful patterns . . . to support the discovery, monitoring and enhancement of real-time processes”, where the “method” “utilize[s]” “inference knowledge” to “support . . . real-time processes”, which includes predicting future events, “predict[ing] future outcomes”, see Okoye, Pg. 145-146, Para. 1-1, “Ontologies has been proven to be one of the essential tools used for semantic-based process mining . . . to generate inference knowledge that could be used to determine useful patterns as well as predict future outcomes”; see also Okoye, Pg. 158, Para. 5, “as described in Figure 4 the work shows the Learning Activity concepts that are defined in the learning model ontology, and how they are mapped to the various milestones of the Research Process to ensure sequence of transitions during the entire learning process” and Okoye, Pg. 156, Fig. 4, where, where predicted future events in the “research process”, such as “Review Literature”, have attributes, such as “milestones”, which are predicted to be part of the future events, “comput[ed] and ascertain[ed]”, and used for process support “queries”, see Okoye, Pg. 163, Para. 1, “this work makes use of the syntax to compute and ascertain the inferred classes and individuals within the learning domain ontology [23]. The queries are implemented in order to check that all parameters (entities) within the defined classes are true and at least falls within the universal restriction of validity by definition, and that there are no inconsistency of data or repeatable contradicting discovery”).
Regarding Claim 5, Okoye in view of Ying and Drury teach the method according to claim 1, wherein the support comprises a prescription and/or recommendation of a future process behavior and/or future event and/or future activity (Okoye, Pg. 146-147, Para. 5-3, “this work explores the technological potentials and prospects in using ontology as a core process mining and querying enabling tool . . . the conceptual method of analysis provides an easy way to analyse the datasets (i.e. the event logs and models) . . . in order to make available inference knowledge that could be utilized to determine useful patterns . . . to support the discovery, monitoring and enhancement of real-time processes”, where the “method” “utilize[s]” “inference knowledge” to “support . . . real-time processes”, which includes recommendations of future process behavior, see Okoye, Pg. 145, Abstract, “The proposed method is a semantic-based process mining approach that is able to induce new knowledge based on previously unobserved behaviours . . . the study claims that it is possible to apply effective reasoning methods to make inferences over a process knowledge-base (e.g. the learning process) that leads to automated discovery of learning patterns and/or behaviour”, where “inferences over . . . the learning process” that create “new knowledge based on previously unobserved behaviours” are future process behavior, which are recommended in the form of “automated discovery of . . . behavior”; see also Okoye, Pg. 156, Fig. 4, where future process behaviors like “AddressProblem” are recommended, when the event flow graph, “Fig. 4”, is converted into a common knowledge graph through execution of “Algorithm 2”, and the “necessary results or associates” output from the common knowledge graph are the recommendations, see Okoye, pg. 156-157, “Algorithm 2”, and Okoye, Pg. 156, Para. 1-2, “This means that based on the process description (i.e. assertions) within the domain ontology, the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process. Accordingly, the following Algorithm 2 describes how this study makes use of the reasoner to classify and infer the necessary associations to produce the outputs”).
Regarding Claim 6, Okoye in view of Ying and Drury teach the method according to claim 1, wherein the support is used in automated decision making and/or predictive resource allocation for the process (Okoye, Pg. 146-147, Para. 5-3, “this work explores the technological potentials and prospects in using ontology as a core process mining and querying enabling tool . . . the conceptual method of analysis provides an easy way to analyse the datasets (i.e. the event logs and models) . . . in order to make available inference knowledge that could be utilized to determine useful patterns . . . to support the discovery, monitoring and enhancement of real-time processes”, where the “method” “utilize[s]” “inference knowledge” to “support . . . real-time processes”, which is used in “automated” decision making for “querying”, “inference”, and “discovery”, see Okoye, Pg. 1, Abstract, “The ontological schema enables for automated querying and inference of useful knowledge from the different domain processes . . . that leads to automated discovery of learning patterns and/or behaviour”; see also Okoye, Pg. 156, Fig. 4, where resources, such as those assigned to resource categories of “ApproveResourceNeeded”, “RecheckSamplePlan”, and “Milestone”, are predictively allocated within “Concept[s]”, like “ReviewLiterature”, when the process reasoning is executed, see for example Okoye, Pg. 163, Para. 3, “DQ1. Is DefineTopic an Activity of the first Milestone (DefineTopicArea)? DL Query: ActivityConcept and is ActivityType Of some DefineTopicArea == the DL query checks if the activity of the first Milestone equal to Define Topic, thus compares the activity of the first Milestone DefineTopicArea with Activity Concept (DefineTopic)”).
Regarding Claim 7, Okoye in view of Ying and Drury teach the method according to claim 1, wherein the method comprises (Okoye, Fig. 1, where the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Okoye, Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”)
combining an event flow graphifier (Okoye, Pg. 153-154, “Algorithm 1”, where the algorithm when executed as a computer program to produce the event flow graph, the “Semantic annotated graphs/labels & an ontology-driven search for process models and explorative analysis” depicted in Okoye, Pg. 156, Fig. 4, to convert “all process models M and event log EV” into a “semantic schema by adding the extracted relationships and individuals to the ontology” is within the broadest reasonable interpretation of an event flow graphifier)
with the knowledge base (KB) matcher (Okoye, Pg. 156-157, “Algorithm 2”, where the algorithm to convert the event flow graph into a common knowledge graph through “Reasoning over Ontologies and Classification of Entities and Outputs” is within the broadest reasonable interpretation of a knowledge base matcher, see also Okoye, Pg. 156, Para. 1, “the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process”).
Regarding Claim 8, Okoye in view of Ying and Drury teach the method according to claim 1, wherein computing the representation comprises a use of an event flow graphifier (Okoye, Fig. 1, where, as discussed above, the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes computing a representation by first generating an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”; Okoye, Pg. 153-154, “Algorithm 1”, where the algorithm when executed as a computer program to produce the event flow graph, the “Semantic annotated graphs/labels & an ontology-driven search for process models and explorative analysis” depicted in Okoye, Pg. 156, Fig. 4, to convert “all process models M and event log EV” into a “semantic schema by adding the extracted relationships and individuals to the ontology” is within the broadest reasonable interpretation of an event flow graphifier)
wherein events or raw events and a process model graph are represented as graph nodes (Okoye, Pg. 159, Para. 2, “ResearchProcessClass: DefineTopicArea, ReviewLiterature, AddressProblem and DefendSolution” and Okoye, Pg. 156, Fig. 4, where the “Class[es]” and subclasses, such as “ResearchProcess” and “DefineTopicArea”, are represented as nodes, and therefore, the events and process models are represented as graph nodes because they form the classes, see Okoye, Pg. 153-154, Algorithm 1, “Extract Classes C ← from M and EV”; see also Okoye, Pg. 148, Para. 2, “an ontological schema aims to transforms a process map into a bipartite graph (also referred to as Ontograph) to denote both the process models and its elements in a uniformed structure”, where the “process models” are themselves within the broadest reasonable interpretation of graphs because they are a “structure” of “link[ed]” “terms”).
Regarding Claim 9, Okoye in view of Ying and Drury teach the method according to claim 8, wherein the events or raw events and the process model graph are inter-linked via at least one common activity type and/or via a mapping of event sequences conforming to the process model (Okoye, Pg. 153-154, Algorithm 1, “Extract Classes C ← from M and EV”, where, as discussed above, the process model, “M”, and events, “EV”, are inter-linked within “Classes”; Okoye, Pg. 159, Para. 2, “ResearchProcessClass: DefineTopicArea, ReviewLiterature, AddressProblem and DefendSolution” and Okoye, Pg. 156, Fig. 4, where the “Class[es]”, such as “ResearchProcess”, are represented as nodes of common activity types, like “DefineTopicArea”, “ReviewLiterature”, “AddressProblem”, and “DefendSolution”; see also Okoye, Pg. 148, Para. 2, “an ontological schema aims to transforms a process map into a bipartite graph (also referred to as Ontograph) to denote both the process models and its elements in a uniformed structure”, where the “process models” are themselves within the broadest reasonable interpretation of graphs because they are a “structure” of “link[ed]” “terms”).
Regarding Claim 10, Okoye in view of Ying and Drury teach the method according to claim 1, wherein an event flow graph is created from the event log and a process model graph (Okoye, Fig. 1, where the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of a graph, first in the form of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156; Okoye, Pg. 153-154, “Algorithm 1”, where, the event flow graph, “Output: Semantic annotated graphs/labels & an ontology-driven search for process models and explorative analysis”, is calculated based on the event logs and the process model, “4: Developing ontology from process models and event logs . . . For all process models M and event log EV”; see also Okoye, Pg. 155, Para. 4, “a semantic annotated graph (see Figure 4) is defined”; Okoye, Pg. 156, Fig. 4, where the event flow aspect of the event flow graph, “semantic annotated graph”, is demonstrated by the flow arrows, such as “isPartOf”, that connect the events, such as “DefineTopicArea” and “DefendSolution”; see also Okoye, Pg. 150, Para. 4, “extraction of process models from event data logs: the derived models are represented as a set of annotated terms that links and relates to defined terms in an ontology, and in so doing, encodes the process logs and the deployed models in the formal structure of ontology” and Okoye, Pg. 148, Para. 2, “an ontological schema aims to transforms a process map into a bipartite graph (also referred to as Ontograph) to denote both the process models and its elements in a uniformed structure”, where the “process models” are themselves within the broadest reasonable interpretation of graphs because they are a “structure” of “link[ed]” “terms”).
Regarding Claim 11, Okoye in view of Ying and Drury teach the method according to claim 1, wherein the knowledge base (KB) matcher is applied to an event flow graph and context knowledge base to create the common knowledge graph or a knowledge and flow graph (KnFG) (Okoye, Fig. 1, where, as discussed above, the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Okoye, Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”; Okoye, Pg. 156-157, “Algorithm 2”, where the algorithm to convert the event flow graph into a common knowledge graph through “Reasoning over Ontologies and Classification of Entities and Outputs” is within the broadest reasonable interpretation of a knowledge base matcher, see also Okoye, Pg. 156, Para. 1, “the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process”, where the knowledge base includes the “underlying information”, such as the “instances (individuals)” available to the “reasoner”; Okoye, Pg. 150, Fig. 1, where the common knowledge graph is also within the broadest reasonable interpretation of a knowledge and flow graph because, as discussed above, it is an event flow graph, Okoye, Pg. 156, Fig. 4, augmented with common knowledge, “Conceptual Information & Enhanced Process”).
Regarding Claim 12, Okoye in view of Ying and Drury teach the method according to claim 11, wherein the event flow graph and the knowledge base are unified using the knowledge baser (KB) matcher (Okoye, Fig. 1, where, as discussed above, the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Okoye, Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”; Okoye, Pg. 156-157, “Algorithm 2”, where the algorithm to convert the event flow graph into a common knowledge graph through “Reasoning over Ontologies and Classification of Entities and Outputs” is within the broadest reasonable interpretation of a knowledge base matcher, see also Okoye, Pg. 156, Para. 1, “the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process”, where the knowledge base includes the “underlying information”, such as the “instances (individuals)” available, which are unified with the event flow graph by the knowledge base matcher in order to create the common knowledge graph, see Okoye, Fig. 1, “Conceptual Information & Enhanced Process”; see also Okoye, Pg. 154, “Algorithm 2:Reasoning over Ontologies and Classification of Entities and Outputs” and Okoye, Pg. 156, Para. 1-2, “the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”).
Regarding Claim 13, Okoye in view of Ying and Drury teach the method according to claim 11, wherein the knowledge graph or knowledge and flow graph (KnFG) is a machine-readable representation of recorded event flows (Okoye, Fig. 1, where, as discussed above, the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Okoye, Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”; Okoye, Pg. 150, Fig. 1, where the common knowledge graph is also within the broadest reasonable interpretation of a knowledge and flow graph because, as discussed above, it is an event flow graph, Okoye, Pg. 156, Fig. 4, augmented with common knowledge, “Conceptual Information & Enhanced Process”, which must be a machine-readable representation of recorded event flows, to generate and read the “flow” of “milestone” events, such as “Assessment Stage”, for the “process”, in response to a user “quer[ies]”, see Okoye, Pg. 158, Para. 4, “the flow . . . the work provides the four milestones; Establish Context → Learning Stage → Assessment Stage → Validation of Learning Outcome (as illustrated in Figure 4) in order to determine and explain the steps taken during the research process” and Okoye, Pg. 163, Para. 2-3, “automatic classification and/or retrieval of the process instances (entities) within the ontology . . . DQ1. Is DefineTopic an Activity of the first Milestone (DefineTopicArea)? DL Query: ActivityConcept and is ActivityType Of some DefineTopicArea == the DL query checks if the activity of the first Milestone equal to Define Topic, thus compares the activity of the first Milestone DefineTopicArea with Activity Concept (DefineTopic)”),
a generalization of the recorded event flows in the form of a process model and the semantic information (Okoye, Fig. 1, where, as discussed above, the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Okoye, Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”, where the underlying event flow diagram, Okoye, Pg. 156, Fig. 4, is a generalization of recorded event flows, see Okoye, Pg. 158, Para. 4, “the flow . . . the work provides the four milestones; Establish Context → Learning Stage → Assessment Stage → Validation of Learning Outcome (as illustrated in Figure 4), which is in the form of process models and semantic information classes, see Okoye, Pg. 153-154, Algorithm 1, where the event flow graph is output, “Output: Semantic annotated graphs/labels & an ontology-driven search for process models and explorative analysis”, which is calculated based on the events and the process model, “4: Developing ontology from process models and event logs . . . For all process models M and event log EV”, as well as semantic information contained in the “Classes”, “For each class C ← semantically analyse the extracted relationships (R) to state facts i.e. Axioms (A) . . . create the semantic schema by adding the extracted relationships and individuals to the ontology”, which are exploited from the event log, “Extract Classes C ← from M and EV”).
Regarding Claim 14, Okoye in view of Ying and Drury teach the method according to claim 11, wherein the knowledge graph or knowledge and flow graph (KnFG) admits application of the graph-based machine learning for link prediction, node classification and node attribute prediction in the graph (Okoye, Fig. 1, where, as discussed above, the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Okoye, Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”, from an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156; and, as a result, the common knowledge graph can reasonably be described as both a knowledge graph or a knowledge and flow graph, which can be utilized to perform graph-based “semantic reasoning” to support the process by performing “automatic classification and/or retrieval of the process instances (entities) within the ontology”, see Okoye, Pg. 163, Para. 2-3, “automatic classification and/or retrieval of the process instances (entities) within the ontology . . . DQ1. Is DefineTopic an Activity of the first Milestone (DefineTopicArea)? DL Query: ActivityConcept and is ActivityType Of some DefineTopicArea == the DL query checks if the activity of the first Milestone equal to Define Topic, thus compares the activity of the first Milestone DefineTopicArea with Activity Concept (DefineTopic)”; see also Okoye, Pg. 156, Para. 1-2, “the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”; see also Okoye, Pg. 155, Para. 4, “a semantic annotated graph (see Figure 4) is defined”; Okoye, Pg. 156, Fig. 4, where the underlying event flow graph components, “semantic annotated graph”, of the common knowledge graph are used for graph-based reasoning, such as entity “isPartOf” or “instanceOf” other entity, which predicts the resource attributes, like “ApproveResourceNeeded”, “RecheckSamplePlan”, and “Milestone”, that will be included in a node, such as “ReviewLiterature”, for the above mentioned “automatic classification and/or retrieval of the process instances (entities) within the ontology” and which, in view of Ying, the reasoning includes graph-based machine learning, Ying, Pg. 1, Para. 1, “Graphs are powerful data representations but are challenging to work with because they require modeling of rich relational information as well as node feature information . . . To address this challenge, Graph Neural Networks (GNNs) have emerged as state-of-the-art for machine learning on graphs, due to their ability to recursively incorporate information from neighboring nodes in the graph, naturally capturing both graph structure and node features”, for link prediction, node classification, Ying, Pg. 2, Para. 3, “Here we propose GNNEXPLAINER . . . the approach is model-agnostic and [can be applied to] . . .any machine learning task for graphs, including node classification, link prediction”, and node attribute prediction, Ying, Pg. 4, Para. 3, “A GNN’s prediction is given by yˆ = Φ(Gc(v), Xc(v)), meaning that it is fully determined by the model Φ, graph structural information Gc(v), and node feature information Xc(v)”, where the “node feature information” are node attributes, which are part of the “GNN’s prediction”, which correspond to the predicted attribute resource features of nodes, discussed above in regard to Okoye, Pg. 156, Fig. 4).
The reasons for obviousness were discussed in regard to the rejection of claim 1 above and remain applicable here.
Regarding Claim 15, Okoye in view of Ying and Drury teach a system for knowledge-based process support, wherein a process model is related to the process, the system comprising (Okoye, Pg. 150, Para. 3, “The design of the semantic-based process mining approach is primarily constructed on the following building blocks as shown in Figure 1”; Okoye, Pg. 150, Fig. 1, where the “Proposed Framework for . . . process mining and querying method” is a method requires a computer system running software to execute its functionality and that system comprises “Process Models”; see also Okoye, Pg. 146-147, Para. 5-3, “this work explores the technological potentials and prospects in using ontology as a core process mining and querying enabling tool . . . the conceptual method of analysis provides an easy way to analyse the datasets (i.e. the event logs and models) . . . in order to make available inference knowledge that could be utilized to determine useful patterns . . . to support the discovery, monitoring and enhancement of real-time processes”, where the “method” “utilize[s]” “inference knowledge” to “support . . . real-time processes”; and Okoye, Pg. 145, Abstract, “process models derived from mining the various data stored in many information systems . . . from the different domain processes”, where the “process models” are related to the “domain processes”)
one or more processors configured to (Okoye, Pg. 150, Fig. 1, where the “Proposed Framework for the semantic-based (ontology) process mining and querying method”, which as discussed above requires the computer system running software to execute its functionality, which requires one or more processors to provide the “Event Data Logs” to its internal system components and algorithms “Process Mining Algorithms”; see also Okoye, Pg. 153, Para. 2, “the study applies the process mining techniques in order to make available the process mappings for the learning process, and check its conformance with the event logs based on the Fuzzy Miner” and Okoye, Pg. 156, Fig. 4, where the processors must be comprised in computer system executing software to provide the “event logs” to the “Fuzzy Miner” functionality and display output on a windows monitor):
exploit semantic information contained in process events or an event log of event data by computing a representation of the events, the process model, and semantic information (Okoye, Pg. 150, Fig. 1, where the “Proposed Framework for the semantic-based (ontology) process mining and querying method” exploits data to compute a representation in the form of a “graph” of “Conceptual Information & Enhanced Process”, see Okoye, Pg. 155, Para. 3-4, “the defined concepts and process descriptions as explained in the steps above means that the semantic annotation is also another essential component in realizing such an ontology-based approach . . . Essentially, semantic annotation(SemAn) is defined formally as a function that returns a set of concepts from the ontology for each node or edge in the graph”; see also Okoye, Pg. 153-154, Algorithm 1, where a graph is output from using a computing means to execute “Algorithm 1” as a computer program, “Output: Semantic annotated graphs/labels & an ontology-driven search for process models and explorative analysis”, which is calculated based on the events, which have event data, and the process model, “4: Developing ontology from process models and event logs . . . For all process models M and event log EV”, as well as semantic information contained in the “Classes”, “For each class C ← semantically analyse the extracted relationships (R) to state facts i.e. Axioms (A) . . . create the semantic schema by adding the extracted relationships and individuals to the ontology”, which are exploited from the event log, “Extract Classes C ← from M and EV”; Okoye, Pg. 150, Fig. 1, where the event log of event data, “Event Data Logs”, which are related to process events, see Okoye, Pg. 146, Para. 3, “events log is capable of providing vital and valuable information . . . For example, revealing the underlying relationships the process elements or resources share”, are acted on by the “Process Mining Algorithms”)
as a common knowledge graph (Okoye, Fig. 1, where the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of a graph, first in the form of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Okoye, Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”);
modify the common knowledge graph to merge equivalent entities or to insert relations between pairs of equivalent entities (Okoye, Fig. 1, where, as discussed above, the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Okoye, Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”; Okoye, Pg. 156-157, “Algorithm 2”, where the algorithm to convert the event flow graph into a common knowledge graph through “Reasoning over Ontologies and Classification of Entities and Outputs” is within the broadest reasonable interpretation of a knowledge base matcher, see also Okoye, Pg. 156, Para. 1, “the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process”, where the knowledge base includes the “underlying information”, such as the “instances (individuals)” available to the “reasoner”, which, in view of Drury, modifies the graph to merge equivalent entities and insert relations between pairs of equivalent entities, see Drury, Pg. 1, Col. 1, Abstract, “This paper proposes to reduce sparseness by merging: equivalent nodes and their edges”; and Drury, Pg. 1, Col. 2, Para. 2, “The merged node process is demonstrated in Figures 1 and 2. The figures demonstrate two candidates nodes for merging B and B#. The two candidates have very similar node names as well as common neighbours C and A. The merge process joins the two nodes into one node B[B#] which combines the neighbours of the previous two graphs” where the “merging process” inserts a common “neighbour” relation, see Drury, Pg. 2, Col. 1, Fig. 1-2, “D”, “C”, and “E”, between pairs pf equivalent entries, “B[B#]”, and/or alternatively inserts an equivalence relation, “B[B#]”, between pairs of equivalent entries, “B” and “B#”, and where, these common “neighbours” are in turn equivalent pairs, see Okoye, Pg. 156, Para. 1, “the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process” and Okoye, Pg. 163, Para. 2-5, “DQ1. Is DefineTopic an Activity of the first Milestone (DefineTopicArea)? DL Query: ActivityConcept and is ActivityType Of some DefineTopicArea == the DL query checks if the activity of the first Milestone equal to Define Topic, thus compares the activity of the first Milestone DefineTopicArea with Activity Concept (DefineTopic)”, where the knowledge base populates the entities of inputs, such as the value for “DefineTopicArea”); and
use the common knowledge graph in graph-based machine learning for the support of the process (Okoye, Pg. 156, Para. 3, “Algorithm 2: Reasoning over Ontologies and Classification of Entities and Outputs”, where, as discussed above, the event flow diagram is converted into a common knowledge graph when algorithm 2 is executed as a computer program, “Output: classified classes, process instances and attributes”, which can be utilized to perform graph-based “semantic reasoning” to support the process by performing “automatic classification and/or retrieval of the process instances (entities) within the ontology”, see Okoye, Pg. 163, Para. 2-3, “automatic classification and/or retrieval of the process instances (entities) within the ontology . . . DQ1. Is DefineTopic an Activity of the first Milestone (DefineTopicArea)? DL Query: ActivityConcept and is ActivityType Of some DefineTopicArea == the DL query checks if the activity of the first Milestone equal to Define Topic, thus compares the activity of the first Milestone DefineTopicArea with Activity Concept (DefineTopic)”; see also Okoye, Pg. 156, Para. 1-2, “the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”, where its use requires a computing means, such as the “inference engine”, see Okoye, Pg. 151, Para. 1, “the Reasoner (inference engine)”, and where, in view of Ying, the graph-based reasoning is graph-based machine learning, Ying, Pg. 1, Para. 1, “Graphs are powerful data representations but are challenging to work with because they require modeling of rich relational information as well as node feature information . . . To address this challenge, Graph Neural Networks (GNNs) have emerged as state-of-the-art for machine learning on graphs, due to their ability to recursively incorporate information from neighboring nodes in the graph, naturally capturing both graph structure and node features”, which also requires a computing means to utilize the “GNN”, see Ying, Pg. 2, Para. 5, “We evaluate GNNEXPLAINER on synthetic as well as real-world graphs . . . using two real-world datasets we show how GNNEXPLAINER can provide important domain insights by robustly identifying important graph structures and node features that influence a GNN’s predictions”, where use of a “GNN” to make “predictions” from “real-world datasets” requires a computing means).
The reasons for obviousness were discussed in regard to the rejection of claim 1 above, and remain applicable here.
Regarding Claim 16, Okoye in view of Ying and Drury teach the method according to claim 12, wherein the knowledge base identifies equivalence relations between entities of inputs provided by the event flow graph and the knowledge base (Okoye, Pg. 156, Para. 1, “the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process”, where, as discussed above, the knowledge base includes the “underlying information”, such as the “instances (individuals)” available to the reasoner, which, in combination with the event flow graph and the reasoner, identifies equivalence relations between the entries of inputs provided by the event flow graph, “Person”, and the knowledge base, “Richard”, see Okoye, Pg. 163, Para. 2-5, “automatic classification and/or retrieval of the process instances (entities) within the ontology . . . DQ4. Does Person P Activity A? Example: Does Person (Richard) Activity Approve Research Proposal? DL Query: Person and hasActivityType value ApproveResearchProposal == the query computes and check persons related to the Approve Research Proposal and then compares if person (Richard) does the activity ApproveResearchProposal”; see also Okoye, Pg. 163, Para. 2-5, “DQ1. Is DefineTopic an Activity of the first Milestone (DefineTopicArea)? DL Query: ActivityConcept and is ActivityType Of some DefineTopicArea == the DL query checks if the activity of the first Milestone equal to Define Topic, thus compares the activity of the first Milestone DefineTopicArea with Activity Concept (DefineTopic)”, where the knowledge base populates the entities of inputs, such as the value for “DefineTopicArea”, which allows for the identifying of equivalence relations between the inputs, “the DL query checks if the activity of the first Milestone equal to Define Topic”, which provided by the event flow graph and the knowledge base because ethe event flow graph labels and organizes the data and the knowledge base provides the data).
Regarding Claim 17, Okoye in view of Ying and Drury teach the method according to claim 1, wherein the common knowledge graph is modified by the knowledge base (KB) matcher to merge equivalent entities (Okoye, Fig. 1, where, as discussed above, the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Okoye, Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”; Okoye, Pg. 156-157, “Algorithm 2”, where the algorithm to convert the event flow graph into a common knowledge graph through “Reasoning over Ontologies and Classification of Entities and Outputs” is within the broadest reasonable interpretation of a knowledge base matcher, see also Okoye, Pg. 156, Para. 1, “the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process”, where the knowledge base includes the “underlying information”, such as the “instances (individuals)” available to the “reasoner”, which, in view of Drury, modifies the graph to merge equivalent entities, see Drury, Pg. 1, Col. 1, Abstract, “This paper proposes to reduce sparseness by merging: equivalent nodes and their edges”; and Drury, Pg. 1, Col. 2, Para. 2, “The merged node process is demonstrated in Figures 1 and 2. The figures demonstrate two candidates nodes for merging B and B#. The two candidates have very similar node names as well as common neighbours C and A. The merge process joins the two nodes into one node B[B#] which combines the neighbours of the previous two graphs”).
The reasons for obviousness were discussed in regard to the rejection of claim 1 above, and remain applicable here.
Regarding Claim 18, Okoye in view of Ying and Drury teach the method according to claim 1, wherein the common knowledge graph is modified by the knowledge base (KB) matcher to insert relations between pairs of equivalent entities (Okoye, Fig. 1, where, as discussed above, the method, “Proposed Framework for the semantic-based (ontology) process mining and querying method” includes the generation of an event flow graph, comprised in the “Ontologies & Process Model with Semantic Concepts/Assertions” and shown in “Fig. 4”, see Okoye, Pg. 156, which is converted into a common knowledge graph, comprised in the “Conceptual Information & Enhanced Process” and further described in regard to Algorithm 2, see Okoye, Pg. 156, Para. 1, “the last essential component in realizing the ontology-based approach is the capability of performing semantic reasoning to classify and even more check for consistency for all the defined classes and relationships that exist within the model”; Okoye, Pg. 156-157, “Algorithm 2”, where the algorithm to convert the event flow graph into a common knowledge graph through “Reasoning over Ontologies and Classification of Entities and Outputs” is within the broadest reasonable interpretation of a knowledge base matcher, see also Okoye, Pg. 156, Para. 1, “the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process”, where the knowledge base includes the “underlying information”, such as the “instances (individuals)” available to the “reasoner”, which, in view of Drury, modifies the graph to insert relations between pairs of equivalent entities, see Drury, Pg. 1, Col. 1, Abstract, “This paper proposes to reduce sparseness by merging: equivalent nodes and their edges”; see also Drury, Pg. 1, Col. 2, Para. 2, “The merged node process is demonstrated in Figures 1 and 2. The figures demonstrate two candidates nodes for merging B and B#. The two candidates have very similar node names as well as common neighbours C and A. The merge process joins the two nodes into one node B[B#] which combines the neighbours of the previous two graphs”, where the “merging process” inserts a common “neighbour” relation, see Drury, Pg. 2, Col. 1, Fig. 1-2, “D”, “C”, and “E”, between pairs pf equivalent entries, “B[B#]”, and/or alternatively inserts an equivalence relation, “B[B#]”, between pairs of equivalent entries, “B” and “B#”, and where, these common “neighbours” are in turn equivalent pairs, see Okoye, Pg. 156, Para. 1, “the reasoner is able to use the underlying information to check if it is possible for any process instances (individuals) to become a member of a class, and to provide the necessary results or associations as requested based on the executed queries or information retrieval process” and Okoye, Pg. 163, Para. 2-5, “DQ1. Is DefineTopic an Activity of the first Milestone (DefineTopicArea)? DL Query: ActivityConcept and is ActivityType Of some DefineTopicArea == the DL query checks if the activity of the first Milestone equal to Define Topic, thus compares the activity of the first Milestone DefineTopicArea with Activity Concept (DefineTopic)”, where the knowledge base populates the entities of inputs, such as the value for “DefineTopicArea”).
The reasons for obviousness were discussed in regard to the rejection of claim 1 above, and remain applicable here.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Okoye in view of Ying, Drury, and Lautzenheiser et al. (hereinafter Lautzenheiser) (U.S. Pat. No. 6,023,572).
Regarding Claim 4, Okoye in view of Ying and Drury teach the method according to claim 3, wherein the at least one attribute is used . . . [for process support involving the attribute’s association with] a future event or future events (Okoye, Pg. 158, Para. 5, “as described in Figure 4 the work shows the Learning Activity concepts that are defined in the learning model ontology, and how they are mapped to the various milestones of the Research Process to ensure sequence of transitions during the entire learning process” and Okoye, Pg. 156, Fig. 4, where, where predicted future events in the “research process”, such as “Review Literature”, have attributes, such as “milestones”, which are predicted to be part of the future events, “comput[ed] and ascertain[ed]”, and used for process support “queries”, see Okoye, Pg. 163, Para. 1, “this work makes use of the syntax to compute and ascertain the inferred classes and individuals within the learning domain ontology [23]. The queries are implemented in order to check that all parameters (entities) within the defined classes are true and at least falls within the universal restriction of validity by definition, and that there are no inconsistency of data or repeatable contradicting discovery”, including conditional logic based on whether an event is associated with an attribute, see Okoye, Pg. 163, Para. 4, “DQ2. Is the Last Activity of the Research Process Award Certificate? DL Query: (i) ResearchProcess and hasEnd value AwardCertificate (ii) ActivityConcept and isEndOf some ResearchProcess == the query computes and checks the last Milestone of the research process and compares if the last activity is equal to Award Certificate. Hence, compares the activity of the last Milestone DefendSolution with AwardCertificate”).
However, Okoye in view of Ying and Drury do not explicitly disclose . . . to allocate resources prior to the usage of the resources by . . . .
However, Lautzenheiser teaches . . . [a method of process modeling] (Pg. 1, Abstract, “A system and method for modeling activities of people in an organization. The organization is modeled using definitions of processes performed by the organization, definitions of data elements generated by the entities performing the processes, and definitions of relationships between the data elements generated and the processes”)
[wherein an event attribute is used] to allocate resources prior to the usage of the resources by . . . [future events] (Pg. 24, Col. 6, Ln. 28-31, “The project planning process 212 includes an analysis process 402, a plan review process 404, a task organization and resource allocation process 406, and a constraint generation process 408” and Pg. 25, Col. 7, Ln. 8-16, “Each Task data element 404 includes a start date attribute, an end date attribute, and a planned effort attribute. It will also be appreciated that the Task data element 404 is connected to the Person Plan data element 408 via relationship line 410”, where “tasks” are future events, as demonstrated by the “planned effort”, which have “resource[s] allocated”, such as “Person” workers, which are allocated, “planned” “via relationship line 410”, through the “planned effort attribute” prior to usage of workers by the “tasks”; see also Pg. 6, Fig. 5, where the connections of “TASKS” to “Person Plan” by the relationship line “410” can itself be considered an attribute for allocation of the “EMPLOYEE” resources for use by the future “TASKS”).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the prediction of attributes for future events for use in process support involving the attribute’s association with future events of Okoye in view of Ying and Drury with the method of process modeling, wherein an event attribute is used to allocate resources prior to the usage of the resources by the future events of Lautzenheiser in order to utilize determined organizational patterns and predicted outcomes (Okoye, Pg. 145-146, Para. 1-1, “Ontologies has been proven to be one of the essential tools used for semantic-based process mining . . . to generate inference knowledge that could be used to determine useful patterns as well as predict future outcomes”) to streamline process modeling and human resource allocation, which improves onboarding of new contributors and auditing of existing functions (Lautzenheiser, Pg. 22, Col. 2, Ln. 3-10, “The computer based modeling of the organization allows persons new to the organization to easily see how their responsibilities relate to the organization, thereby assisting in training. In addition, auditing organizations can see how an organization functions and verify the compliance with various requirements. Persons within the organization can use the system to streamline processes and identify duplication of work”).
Response to Arguments
Applicant's arguments filed on March 13th, 2026 have been fully considered. Each argument is addressed in detail below.
I. Applicant argues the objections to the claims should be withdrawn (Applicant’s Remarks, 03/13/2026, Pg. 7, Section “Objections to the Claims”).
Applicant’s amendments have overcome the objection to claim 13, as previously set forth in the December 18th, 2025 Office Action. As a result, the objection has been withdrawn.
II. Applicant argues the claims, as amended, do not invoke 35 U.S.C. § 112(f) (Applicant’s Remarks, 03/13/2026, Pg. 7-8, Section “Claim interpretation under 35 U.S.C. § 112(f)”).
The claims, as amended, do not do not invoke 35 U.S.C. § 112(f). As a result, the amended claims are not interpreted under 35 U.S.C. § 112(f).
III. Applicant argues the rejections of the claims, under 35 U.S.C. § 112(b), should be withdrawn (Applicant’s Remarks, 03/13/2026, Pg. 8, Section “Rejections under 35 U.S.C. § 112(b)”).
Applicant’s amendments have overcome each and every rejection to the claims, under 35 USC §
112(b), previously set forth in the December 18th, 2025 Office Action. As a result, these rejections have
been withdrawn. However, as discussed in detail above, the amendments to the claims introduce additional indefiniteness which necessitates new grounds for rejection under 35 U.S.C. § 112(b).
IV. Applicant argues the rejections of the claims, under 35 U.S.C. § 101, should be withdrawn (Applicant’s Remarks, 03/13/2026, Pg. 8-13, Section “Rejections under 35 U.S.C. § 101”).
First, Applicant argues claims 1-16 are not directed to an abstract idea (Step 2A, Prong 1).
Specifically, Applicant argues the claims are not directed to mental processes because they “are directed to providing knowledge-based process support by exploiting semantic information included in process events or event logs by computing a representation of the events, the process model, and semantic information as a common knowledge graph, and then modifying the common knowledge graph by merging equivalent entities or to inserting relations between any pair of equivalent entities” (Pg. 9-10, Para. 3-1). Additionally, Applicant asserts the steps of “exploiting semantic information contained in the process events or event log by computing a representation of the events, the process model, and semantic information as a common knowledge graph” and “using the knowledge graph in graph-based machine learning for the support of the process” (Claim 1) “cannot be practically performed in the human mind” because “they require generating and maintaining a complex graph data structure derived from large event datasets and performing iterative computations over that graph” (Pg. 10, Para. 2). In support of this assertion, Applicant references the USPTO Subject Matter Eligibility Examples: Abstract Ideas to argue the claimed subject matter of graph-based machine learning using a common knowledge graph is similar to the machine learning subject matter considered eligible in Example 39.
According to MPEP 2106.04(a), “A Claim That Requires a Computer May Still Recite a Mental Process . . . examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process”.
Additionally, according to MPEP 2145, “Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims” (see also In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993)).
Here, as discussed in detail above, the claimed subject matter of generating a common knowledge graph, modifying the graph to address equivalent entities, and using the graph to perform reasoning are well within the capabilities of the human mind. As compared with the subject matter of Example 39, which comprises multiple stages of neural network training using specifically modified input data, the claims, as originally presented and as amended, amount to exercising judgement to form opinions related to a graph. For example, while applicant references a complex graph data structure derived from large event datasets and reasoning performed over multiple iterative computations, these limitations are not positively recited in the claims, and even if contained in the specification, are not read into the claims (see MPEP 2145).
To further differentiate the claimed subject matter from Example 39, the graph-based machine learning is merely referenced as a means of applying the graph and is entirely disconnected from the positively recited steps for generating and modifying the graph. As a result, while Applicant is correct that the recitation of a graph-based machine learning model requires a computer, the claim still recites a mental process because the graph-based machine learning model is merely used as a tool to perform the concept of reasoning based on a common knowledge graph (see MPEP 2106.04(a)).
As a result, the argument is not persuasive.
Next, Applicant argues the subject matter of the claims are integrated into a practical application (Step 2A, Prong 2). Specifically, Applicant argues the claims recite “exploiting semantic information contained in the process events or event log by computing a representation of the events, the process model, and semantic information as a common knowledge graph, where the common knowledge graph is modified by a knowledge base (KB) matcher to merge equivalent entities or to insert relations between any pair of equivalent entities” which “enhances the ability of machine learning models to provide accurate prediction of future process events and attributes, such as resource requirements” (Pg. 10-11, Para. 3-1). Additionally, Applicant argues the claims are directed to improvements to machine learning that allows for “graph-based machine learning to analyze richer relationships than traditional process-mining approaches, which enables a more accurate prediction of future process events and attributes” (Pg. 9, Para. 2; Pg. 12, Para. 2). In support of this assertion, Applicant references MPEP 2106.05, to argue the specification discloses, and the claim reflects, the above-asserted technical solution to a technical problem, and the Ex Parte Desjardins Memo, to argue the abstract ideas in the claims should not subsume the technical improvements (Pg. 11, Para. 2-3; Pg. 12, Para. 3).
According to MPEP 2106.04(d)(1), “A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field . . . The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement”.
Additionally, according to MPEP 2106.05(f), “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two . . . [is that] A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application”.
Here, as discussed in detail above, the recitations of computer components, like a graph-based machine learning model, are at a high level of generality which do not integrate the abstract ideas into a practical application. Instead, the computer components function merely to apply the reasoning based on a generated and modified common knowledge graph. While Applicant asserts the specification discloses a technical solution to a technical problem, the benefits are asserted in a conclusory manner such that it is not apparent to a person of ordinary skill in the art how the common knowledge graph is utilized by the graph-based machine learning model to enrich reasoning and improve accuracy (Compare Applicant’s Spec. Para. [0031] – [0039] with MPEP 2106.04(d)(1)).
Additionally, the claims, as currently formulated, fail to reflect the asserted technological improvement. For example, the steps of “receiving . . .” and “exploiting . . .” do not positively recite any limitations that reflect how the common knowledge graph is constructed so as to enrich model reasoning and improve accuracy (Claim 1). Similarly, the step of “using . . .” amounts to a cursory reference to a graph-based machine learning model, where no details are provided to reflect how the model is used to enrich model reasoning and improve accuracy (Claim 1). As a result, the claims fail to reflect a specific technical solution to a technical problem (see MPEP 2106.04(d)(1)).
Instead, the claimed subject matter amounts to representing a variety of information in an interconnected graph, which has broad applicability across many fields of machine learning and computer science, as well as many nontechnical domains of information organization. Therefore, the claim does not integrate the judicial exception into a practical application (MPEP 2106.05(f)).
As a result, the argument is not persuasive.
Finally, Applicant argues the claims are eligible because they constitute an inventive concept (Step 2B). Specifically, Applicant argues the claims “include unconventional features of modifying a common knowledge graph by a knowledge base matcher to merge equivalent entities or to insert relations between any pair of equivalent entities” (Pg. 13, Para. 2). In support of this assertion, Applicant references MPEP 2106.05, which stands for the position that lack of prevalence of an element is positive evidence for eligibility, and Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014), which stands for the position that non-obviousness is positive evidence in favor of eligibility (Pg. 13, Para. 2).
According to MPEP 2106.05, “An inventive concept cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself” (internal quotation marks omitted).
Here, as discussed in detail above, the modifying of the common knowledge graph is, itself, a mental process. As a result, it cannot furnish the inventive concept (see MPEP 2106.05). Additionally, as discussed in regard to Step 2A, Prong 2, the use of a modified interconnected graph to reason from is widely used across the domain of machine learning, the domains of computer science, and nontechnical domains of information organization (MPEP 2106.05(f)). As a result, the claimed subject matter, as currently formulated, cannot be accurately described as uncommon or not prevalent. Furthermore, as discussed in detail above, the claimed subject matter was found to be obvious over the prior art of record.
As a result, the argument is not persuasive.
V. Applicant argues the rejections of the claims, under 35 U.S.C. § 103, should be withdrawn (Applicant’s Remarks, 03/13/2026, Pg. 13-16, Section “Rejections under 35 U.S.C. § 103”).
In response to Applicant’s amendments, the previously communicated rejections under 35 U.S.C. § 103, have been withdrawn. However, Applicants arguments are not persuasive in light of the new grounds for rejection, under 35 U.S.C. § 103, discussed in detail above. The new grounds of rejection rely on new prior art of record to teach the new combination of elements in the amended independent claims, which were not presented in this arrangement in any of the previously presented claims. As a result, Applicant’s arguments are rendered moot.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW BRYCE GOLAN whose telephone number is (571)272-5159. The examiner can normally be reached Monday through Friday, 8:00 AM to 5:00 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHEW BRYCE GOLAN/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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