DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to the Application filed on 03/07/2022. Claims 1-20 are pending in the case. Claims 1, 4, and 17 are independent claims.
Claim Rejections - 35 U.S.C. § 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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-3 are directed towards the statutory category of an article of manufacture. Claims 4-16 are directed towards the statutory category of a process. Claims 17-20 are directed towards the statutory category of a machine.
With respect to claim 1:
2A Prong 1: This claim is directed to a judicial exception.
determining one or more run-time relations between the run-time process model selections (mental process);
wherein the one or more run-time relations comprises a relation between natural language based semantic portions of the activity labels of the run-time process model selections (mental process);
obtaining a set of run-time logical formulas based on the run-time process model selections and the one or more run-time relations (mental process);
determining one or more process model recommendations by applying a plurality of rules to the set of run-time logical formulas (mental process); and
wherein at least one rule of the plurality of rules is applied based on a semantic similarity of the activity labels of the run-time process model selections (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
One or more non-transitory computer-readable media storing computer executable instructions that, when executed by a processor, perform a method for semantic-aware rule-based recommendation within a process modeling system, the method comprising (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
receiving a set of run-time process model selections from a user, each of the run- time process model selections comprising an activity label (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
One or more non-transitory computer-readable media storing computer executable instructions that, when executed by a processor, perform a method for semantic-aware rule-based recommendation within a process modeling system, the method comprising (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
receiving a set of run-time process model selections from a user, each of the run- time process model selections comprising an activity label (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 2:
2A Prong 1: This claim is directed to a judicial exception.
determining a recommendation confidence score for each of the one or more process model recommendations based on the applied rules of the plurality of rules (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
causing display of the recommendation confidence score to the user within a process modeling interface (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
causing display of an explanation of a recommendation based on an explanation of at least one of the applied rules of the plurality of rules (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
causing display of the recommendation confidence score to the user within a process modeling interface (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
causing display of an explanation of a recommendation based on an explanation of at least one of the applied rules of the plurality of rules (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 3:
2A Prong 1: This claim is directed to a judicial exception.
the run-time process model selections further comprise a plurality of process model connections between process model components and at least one of the one or more run-time relations is determined based at least in part on the plurality of process model connections (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 4:
2A Prong 1: This claim is directed to a judicial exception.
A method for semantic-aware rule-based recommendation within a process modeling system, the method comprising (mental process);
determining one or more run-time relations between the run-time process model selections (mental process);
wherein the one or more run-time relations comprises a relation between natural language based semantic portions of the activity labels of the run-time process model selections (mental process);
obtaining a set of run-time logical formulas based on the run-time process model selections and the one or more run-time relations (mental process);
determining one or more process model recommendations by applying a plurality of rules to the set of run-time logical formulas (mental process); and
wherein at least one rule of the plurality of rules is applied based on a semantic similarity of the activity labels of the run-time process model selections (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
receiving a set of run-time process model selections from a user, each of the run- time process model selections comprising an activity label (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
receiving a set of run-time process model selections from a user, each of the run- time process model selections comprising an activity label (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 5:
2A Prong 1: This claim is directed to a judicial exception.
determining one or more relations between the predefined process model selections, wherein the one or more relations comprises a relation between natural language based semantic portions of the activity labels of the predefined process model selections (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
retrieving a set of predefined process model selections from a process model repository, each predefined process model selection of the set of predefined process model selections comprising an activity label (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
retrieving a set of predefined process model selections from a process model repository, each predefined process model selection of the set of predefined process model selections comprising an activity label (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 6:
2A Prong 1: This claim is directed to a judicial exception.
obtaining a set of logical formulas by transforming each selection of the set of predefined process model selections into a respective logical formula that captures the respective activity label of each predefined process model selection and capturing the one or more relations between the predefined process model selections in logical formulas (mental process);
generating the plurality of rules based on the set of logical formulas, each rule of the plurality of rules comprising a confidence score (mental process); and
wherein each rule of the plurality of rules follows a predefined rule template of a set of predefined rule templates (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 7:
2A Prong 1: This claim is directed to a judicial exception.
identifying one or more rules of the plurality of rules corresponding to a process model recommendation of the one or more process model recommendations (mental process); and
responsive to a user input from the user, removing the one or more rules from the plurality of rules (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 8:
2A Prong 1: This claim is directed to a judicial exception.
the predefined process model selections from the process model repository further comprises a plurality of process model connections between process model components and at least one of the one or more relations between the predefined process model selections is determined based at least in part on the plurality of process model connections (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 9:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
causing display of the one or more process model recommendations to the user within a process modeling interface associated with a process modeling application based on a respective confidence score of each applied rule (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
causing display of the one or more process model recommendations to the user within a process modeling interface associated with a process modeling application based on a respective confidence score of each applied rule (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 10:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
storing the plurality of rules in a rule data store associated with the process modeling application (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
storing the plurality of rules in a rule data store associated with the process modeling application (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 11:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
training a machine learning algorithm associated with the process modeling application using a set of process modeling data from the process model repository, wherein the process model recommendations are based further on the application of the machine learning algorithm (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
training a machine learning algorithm associated with the process modeling application using a set of process modeling data from the process model repository, wherein the process model recommendations are based further on the application of the machine learning algorithm (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning).
With respect to claim 12:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the one or more process model recommendations are displayed within a user-actuatable recommendation prompt configured to automatically provide an activity label to an unknown process model component based on a user selection (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the one or more process model recommendations are displayed within a user-actuatable recommendation prompt configured to automatically provide an activity label to an unknown process model component based on a user selection (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 13:
2A Prong 1: This claim is directed to a judicial exception.
the run-time logical formulas are obtained by transforming each selection of the set of run-time process model selections into a respective run-time logical formula that captures the respective activity label of each run-time process model selection and capturing the one or more run-time relations between the run-time process model selections in run-time logical formulas (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 14:
2A Prong 1: This claim is directed to a judicial exception.
at least one subsequent rule of the plurality of rules is applied strictly without considering semantic similarity of the activity labels (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 15:
2A Prong 1: This claim is directed to a judicial exception.
automatically adding an activity label associated with a selected process model recommendation of the one or more process model recommendations to a process model under development (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 16:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the run-time process model selections are received within a process model interface during run-time of a process model application (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the run-time process model selections are received within a process model interface during run-time of a process model application (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 17:
2A Prong 1: This claim is directed to a judicial exception.
determining one or more run-time relations between the run-time process model selections (mental process);
wherein the one or more run-time relations comprises a relation between natural language based semantic portions of the activity labels of the run-time process model selections (mental process);
obtaining a set of run-time logical formulas based on the run-time process model selections and the one or more run-time relations (mental process);
determining one or more process model recommendations by applying a plurality of rules to the set of run-time logical formulas (mental process); and
wherein at least one rule of the plurality of rules is applied based on a semantic similarity of the activity labels of the run-time process model selections (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A process modeling system comprising: a process model repository; and a processor programmed to perform a method for semantic-aware rule-based recommendation within the process modeling system, the method comprising (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
receiving a set of run-time process model selections from a user, each of the run-time process model selections comprising an activity label (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A process modeling system comprising: a process model repository; and a processor programmed to perform a method for semantic-aware rule-based recommendation within the process modeling system, the method comprising (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
receiving a set of run-time process model selections from a user, each of the run-time process model selections comprising an activity label (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 18:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a machine learning algorithm associated with a process modeling application, the machine learning algorithm trained using historic process model data from the process model repository (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a machine learning algorithm associated with a process modeling application, the machine learning algorithm trained using historic process model data from the process model repository (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning).
With respect to claim 19:
2A Prong 1: This claim is directed to a judicial exception.
the one or more relations within the set of logical formulas from the set of run-time process model selections comprises a followed-by relation which defines an activity label which follows another activity label (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 20:
2A Prong 1: This claim is directed to a judicial exception.
the one or more relations within the set of logical formulas from the set of run-time process model selections comprises an in-same-process relation which defines an activity label which co-occurs with another activity label in a process model (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-9, 11-17, 19, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Sola et al. (Sola, Diana, Christian Meilicke, Han van der Aa, and Heiner Stuckenschmidt. "A rule-based recommendation approach for business process modeling." In International Conference on Advanced Information Systems Engineering, pp. 328-343. Cham: Springer International Publishing, 2021, hereinafter Sola) in view of Liu et al. (U.S. Pat. App. Pub. No. 2014/0310053, hereinafter Liu), Makni et al. (Makni, Lobna, Nahla Zaaboub Haddar, and Hanêne Ben-Abdallah. "Detection of Semantic Relations between Business Process Activity Labels." In ICEIS (3), pp. 273-277. 2012. hereinafter Makni), and Goldstein et al. (Goldstein, Maayan, and Cecilia González-Álvarez. "Augmenting modelers with semantic autocompletion of processes." In International Conference on Business Process Management, pp. 20-36. Cham: Springer International Publishing, 2021, hereinafter Goldstein).
As to independent claim 1, Sola teaches:
… receiving a set of run-time process model selections from a user, each of the run- time process model selections comprising an activity label (Page 2, "a business process model under development, activity recommendation sets out to suggest suitable activities to extend the model at a user-defined position." Page 6. User-defined reads on the claimed receiving selections from a user);
determining one or more run-time relations between the run-time process model selections (Section 4.1 discusses translating process graphs into relations between activities, including structural relations drawn from the process graph);…
obtaining a set of run-time logical formulas based on the run-time process model selections and the one or more run-time relations (Page 6, "determine the constants and predicates to be used to describe B in terms of logical formulas"); and
determining one or more process model recommendations by applying a plurality of rules to the set of run-time logical formulas (Section 4.1 discusses rule learning from process models and rule application to an unfinished process graph to recommend an activity label),….
Sola does not appear to expressly teach One or more non-transitory computer-readable media storing computer executable instructions that, when executed by a processor, perform a method for semantic-aware rule-based recommendation within a process modeling system, the method comprising.
Liu teaches One or more non-transitory computer-readable media storing computer executable instructions that, when executed by a processor, perform a method for semantic-aware rule-based recommendation within a process modeling system, the method comprising (Paragraph 32 et seq.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the process modeling of Liu for providing real-time business process suggestion and recommendation utilizing a business process modeler (see Liu at paragraph 8).
Sola does not appear to expressly teach wherein the one or more run-time relations comprises a relation between natural language based semantic portions of the activity labels of the run-time process model selections.
Makni teaches wherein the one or more run-time relations comprises a relation between natural language based semantic portions of the activity labels of the run-time process model selections (Page 275, "decomposing these activity labels into the verb-object grammatical structure" et seq. Decomposing activity labels into natural-language semantic portions, including action and business object, and comparing those portions to determine semantic relations among labels).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the detection of semantic relations between business process activity labels of Makni to use a linguistic comparison between activity labels to derive additional semantic relation types (see Makni at section 1).
Sola does not appear to expressly teach wherein at least one rule of the plurality of rules is applied based on a semantic similarity of the activity labels of the run-time process model selections.
Goldstein teaches wherein at least one rule of the plurality of rules is applied based on a semantic similarity of the activity labels of the run-time process model selections (Abstract, "autocompletion at design time, that is based on the semantic similarity of subprocesses").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the augmenting modelers with semantic autocompletion of processes of Goldstein to enable the autocompletion of the next element at design time (see Goldstein at page 2).
As to dependent claim 2, Sola further teaches determining a recommendation confidence score for each of the one or more process model recommendations based on the applied rules of the plurality of rules (Page 8, confidence aggregation); causing display of the recommendation confidence score to the user within a process modeling interface (Page 9, "assign the recommendation a single score and rank it accordingly"); and causing display of an explanation of a recommendation based on an explanation of at least one of the applied rules of the plurality of rules (Page 9, "Result explanation. One of the advantages of our approach is that the rules that serve as a basis for recommendations can also be used to explain provided recommendations").
As to dependent claim 3, Sola further teaches the run-time process model selections further comprise a plurality of process model connections between process model components and at least one of the one or more run-time relations is determined based at least in part on the plurality of process model connections (Page 6, section 4.1, rule learning with edges and nodes).
As to independent claim 4, Sola teaches:
… receiving a set of run-time process model selections from a user, each of the run- time process model selections comprising an activity label (Page 2, "a business process model under development, activity recommendation sets out to suggest suitable activities to extend the model at a user-defined position." Page 6. User-defined reads on the claimed receiving selections from a user);
determining one or more run-time relations between the run-time process model selections (Section 4.1 discusses translating process graphs into relations between activities, including structural relations drawn from the process graph);…
obtaining a set of run-time logical formulas based on the run-time process model selections and the one or more run-time relations (Page 6, "determine the constants and predicates to be used to describe B in terms of logical formulas"); and
determining one or more process model recommendations by applying a plurality of rules to the set of run-time logical formulas (Section 4.1 discusses rule learning from process models and rule application to an unfinished process graph to recommend an activity label),….
Sola does not appear to expressly teach A method for semantic-aware rule-based recommendation within a process modeling system, the method comprising.
Liu teaches A method for semantic-aware rule-based recommendation within a process modeling system, the method comprising (Title and abstract).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the process modeling of Liu for providing real-time business process suggestion and recommendation utilizing a business process modeler (see Liu at paragraph 8).
Sola does not appear to expressly teach wherein the one or more run-time relations comprises a relation between natural language based semantic portions of the activity labels of the run-time process model selections.
Makni teaches wherein the one or more run-time relations comprises a relation between natural language based semantic portions of the activity labels of the run-time process model selections (Page 275, "decomposing these activity labels into the verb-object grammatical structure" et seq. Decomposing activity labels into natural-language semantic portions, including action and business object, and comparing those portions to determine semantic relations among labels).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the detection of semantic relations between business process activity labels of Makni to use a linguistic comparison between activity labels to derive additional semantic relation types (see Makni at section 1).
Sola does not appear to expressly teach wherein at least one rule of the plurality of rules is applied based on a semantic similarity of the activity labels of the run-time process model selections.
Goldstein teaches wherein at least one rule of the plurality of rules is applied based on a semantic similarity of the activity labels of the run-time process model selections (Abstract, "autocompletion at design time, that is based on the semantic similarity of subprocesses").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the augmenting modelers with semantic autocompletion of processes of Goldstein to enable the autocompletion of the next element at design time (see Goldstein at page 2).
As to dependent claim 5, Sola further teaches retrieving a set of predefined process model selections from a process model repository, each predefined process model selection of the set of predefined process model selections comprising an activity label (Page 2, "A repository of available business process models").
Makni further teaches determining one or more relations between the predefined process model selections, wherein the one or more relations comprises a relation between natural language based semantic portions of the activity labels of the predefined process model selections (Page 278, "to use a linguistic comparison between activity labels to derive additional semantic relation types").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the detection of semantic relations between business process activity labels of Makni to use a linguistic comparison between activity labels to derive additional semantic relation types (see Makni at section 1).
As to dependent claim 6, Sola further teaches obtaining a set of logical formulas by transforming each selection of the set of predefined process model selections into a respective logical formula that captures the respective activity label of each predefined process model selection and capturing the one or more relations between the predefined process model selections in logical formulas (Page 6, "We translate each B = (N;E; ; ) 2 B as follows"); and generating the plurality of rules based on the set of logical formulas, each rule of the plurality of rules comprising a confidence score (Page 8, confidence aggregation), wherein each rule of the plurality of rules follows a predefined rule template of a set of predefined rule templates (Page 7, rule templates).
As to dependent claim 7, Sola further teaches identifying one or more rules of the plurality of rules corresponding to a process model recommendation of the one or more process model recommendations (Page 6, "rule-based recommendation". Page 9, "rules that serve as a basis for recommendations can also be used to explain provided recommendations").
Liu further teaches responsive to a user input from the user, removing the one or more rules from the plurality of rules (Paragraph 4, "The analyst can continue adding more edge(s) and/or component(s) if the suggestion does not meet the requirement of the analyst. The system can dynamically adjust suggestions considering the newly provided information").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the process modeling of Liu for providing real-time business process suggestion and recommendation utilizing a business process modeler (see Liu at paragraph 8).
As to dependent claim 8, Sola further teaches the predefined process model selections from the process model repository further comprises a plurality of process model connections between process model components and at least one of the one or more relations between the predefined process model selections is determined based at least in part on the plurality of process model connections (Page 6, "rule learning derives rules that capture activity inter-relations from the process graphs in a repository").
As to dependent claim 9, Sola further teaches causing display of the one or more process model recommendations to the user within a process modeling interface associated with a process modeling application based on a respective confidence score of each applied rule (Page 8, confidence aggregation).
As to dependent claim 12, Liu further teaches the one or more process model recommendations are displayed within a user-actuatable recommendation prompt configured to automatically provide an activity label to an unknown process model component based on a user selection (Page 14, "business process modeler can be configured with an auto-completion based recommendation").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the process modeling of Liu for providing real-time business process suggestion and recommendation utilizing a business process modeler (see Liu at paragraph 8).
As to dependent claim 13, Sola further teaches the run-time logical formulas are obtained by transforming each selection of the set of run-time process model selections into a respective run-time logical formula that captures the respective activity label of each run- time process model selection and capturing the one or more run-time relations between the run-time process model selections in run-time logical formulas (Section 4.1 discusses translating process graphs into relations between activities, including structural relations drawn from the process graph. Page 6, "For each pair of nodes m 6= n 2 N we add the formulas inSameProcess(m; n) and inSameProcess(n;m) to express that m and n appear in the same graph").
As to dependent claim 14, Sola further teaches at least one subsequent rule of the plurality of rules is applied strictly without considering semantic similarity of the activity labels (Page 6, rule learning).
As to dependent claim 15, Liu further teaches automatically adding an activity label associated with a selected process model recommendation of the one or more process model recommendations to a process model under development (Paragraph 14, "A business process modeler can be configured with an auto-completion based recommendation to display a potential service with respect to the business process as an icon and an expansion recommendation to display a major component and a relative sequence in the business process while creating a workflow").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the process modeling of Liu for providing real-time business process suggestion and recommendation utilizing a business process modeler (see Liu at paragraph 8).
As to dependent claim 16, Liu further teaches the run-time process model selections are received within a process model interface during run-time of a process model application (Paragraph 14, "dynamic input from a business analyst can be incorporated utilizing a real-time suggestion engine").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the process modeling of Liu for providing real-time business process suggestion and recommendation utilizing a business process modeler (see Liu at paragraph 8).
As to independent claim 17, Sola teaches:
… a process model repository (Page 2, "A repository of available business process models"); and…
receiving a set of run-time process model selections from a user, each of the run-time process model selections comprising an activity label (Page 2, "a business process model under development, activity recommendation sets out to suggest suitable activities to extend the model at a user-defined position." Page 6. User-defined reads on the claimed receiving selections from a user);
determining one or more run-time relations between the run-time process model selections (Section 4.1 discusses translating process graphs into relations between activities, including structural relations drawn from the process graph);…
obtaining a set of run-time logical formulas based on the run-time process model selections and the one or more run-time relations (Page 6, "determine the constants and predicates to be used to describe B in terms of logical formulas"); and
determining one or more process model recommendations by applying a plurality of rules to the set of run-time logical formulas (Section 4.1 discusses rule learning from process models and rule application to an unfinished process graph to recommend an activity label),….
Sola does not appear to expressly teach A process modeling system comprising; and a processor programmed to perform a method for semantic-aware rule-based recommendation within the process modeling system, the method comprising.
Liu teaches A process modeling system comprising (Title and abstract); and a processor programmed to perform a method for semantic-aware rule-based recommendation within the process modeling system, the method comprising (Figure 2, central processor 201).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the process modeling of Liu for providing real-time business process suggestion and recommendation utilizing a business process modeler (see Liu at paragraph 8).
Sola does not appear to expressly teach wherein the one or more run-time relations comprises a relation between natural language based semantic portions of the activity labels of the run-time process model selections.
Makni teaches wherein the one or more run-time relations comprises a relation between natural language based semantic portions of the activity labels of the run-time process model selections (Page 275, "decomposing these activity labels into the verb-object grammatical structure" et seq. Decomposing activity labels into natural-language semantic portions, including action and business object, and comparing those portions to determine semantic relations among labels).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the detection of semantic relations between business process activity labels of Makni to use a linguistic comparison between activity labels to derive additional semantic relation types (see Makni at section 1).
Sola does not appear to expressly teach wherein at least one rule of the plurality of rules is applied based on a semantic similarity of the activity labels of the run-time process model selections.
Goldstein teaches wherein at least one rule of the plurality of rules is applied based on a semantic similarity of the activity labels of the run-time process model selections (Abstract, "autocompletion at design time, that is based on the semantic similarity of subprocesses").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the augmenting modelers with semantic autocompletion of processes of Goldstein to enable the autocompletion of the next element at design time (see Goldstein at page 2).
As to dependent claim 19, Sola further teaches the one or more relations within the set of logical formulas from the set of run-time process model selections comprises a followed-by relation which defines an activity label which follows another activity label (Page 4, "This abstraction strategy only considers which activities may follow each other during process execution, captured in the followedBy relation").
As to dependent claim 20, Sola further teaches the one or more relations within the set of logical formulas from the set of run-time process model selections comprises an in-same-process relation which defines an activity label which co-occurs with another activity label in a process model (Page 6, "For each pair of nodes m 6= n 2 N we add the formulas inSameProcess(m; n) and inSameProcess(n;m) to express that m and n appear in the same graph").
Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over Sola in view of Liu, Makni, Goldstein, and Deng et al. (Deng, Shuiguang, Dongjing Wang, Ying Li, Bin Cao, Jianwei Yin, Zhaohui Wu, and Mengchu Zhou. "A recommendation system to facilitate business process modeling." IEEE transactions on cybernetics 47, no. 6 (2016): 1380-1394, hereinafter Deng).
As to dependent claim 10, the rejection of claim 9 is incorporated.
Sola does not appear to expressly teach storing the plurality of rules in a rule data store associated with the process modeling application.
Deng teaches storing the plurality of rules in a rule data store associated with the process modeling application (Page 1384, offline mining and online recommendation. An offline mining phase extracts relations/patters from repository processes and stores the extracted patterns in a database for later online recommendation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the process modeling of Deng to build business processes in an efficient and accurate way (see Deng at page 1580).
Claims 11 and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Sola in view of Liu, Makni, Goldstein, and Wang et al. (Wang, Huaqing, Lijie Wen, Li Lin, and Jianmin Wang. "RLRecommender: a representation-learning-based recommendation method for business process modeling." In International Conference on Service-Oriented Computing, pp. 478-486. Cham: Springer International Publishing, 2018, hereinafter Wang).
As to dependent claim 11, the rejection of claim 9 is incorporated.
Sola does not appear to expressly teach training a machine learning algorithm associated with the process modeling application using a set of process modeling data from the process model repository, wherein the process model recommendations are based further on the application of the machine learning algorithm.
Wang teaches training a machine learning algorithm associated with the process modeling application using a set of process modeling data from the process model repository, wherein the process model recommendations are based further on the application of the machine learning algorithm (Title, abstract, and introduction).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the process modeling of Wang to provide accurate and efficient business process recommendations (see Wang at page 478).
As to dependent claim 18, the rejection of claim 17 is incorporated.
Sola does not appear to expressly teach a machine learning algorithm associated with a process modeling application, the machine learning algorithm trained using historic process model data from the process model repository.
Wang teaches a machine learning algorithm associated with a process modeling application, the machine learning algorithm trained using historic process model data from the process model repository (Page 479, "process recommendation problem using representation learning". Page 481, training a representation learning model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the process modeling of Sola to include the process modeling of Wang to provide accurate and efficient business process recommendations (see Wang at page 478).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gerber et al. (U.S. Pat. App. Pub. No. 2022/0147842) teaches a business process modeling recommendation engine. A software-based process modeling tool is provided that labels and links a plurality of activities forming part of a process that is being modeled include a recommendation engine. With the modeling tool, the activities are each represented as a node in the graphical user interface. A recommendation engine is polled with information characterizing the labeled activities and their corresponding links to obtain a plurality of ranked recommendations for an unlabeled node representing a next activity in the process for selection by a user. The recommendation engine applies the information characterizing the labeled activities and their corresponding links to a plurality of rules each having a corresponding confidence value. The plurality of rules is generated using a plurality of rule templates as applied to a plurality of historical processes each comprising a plurality of labeled and linked activities.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time.
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/Casey R. Garner/Primary Examiner, Art Unit 2123
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