DETAILED ACTION
Claims 1-20 have been examined and are pending.
Claims 1-20 are rejected (Non-Final Rejection).
Notice of AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 26 June 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the examiner.
Claim Objections
Claims 2-9, 11-14 and 16-20 are objected to for informalities.
Claim 2 recites “… A method as in claim 1 further comprising: …”, which appears to be missing a comma between “1” and “further”, and “A method” should be replaced with “The method”. Claims 3-9, 11-14 and 16-20 have similar deficiencies and are objected to for the same/similar reasons. Appropriate correction is required.
Claim 11 also depends from “claim 11”, which appears to be a clear typographical error. Claim 11 is being interpreted as depending from claim 10. Appropriate correction is required.
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.
The following is an analysis based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
1. Determining if the claim falls within a statutory category;
2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and
2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception.
(See MPEP 2106).
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed inventions are directed to an abstract idea without significantly more. The claim(s) recite a mental process. See MPEP § 2106.04(a)(2)(III).
Claims 1-20 Step 1, Statutory Category?:
Yes: Claims 1-9 are directed to the statutory category of a process. See MPEP § 2106.03.
Yes: Claims 10-14 are directed to the statutory category of a manufacture. See MPEP § 2106.03.
Yes: Claims 15-20 are directed to the statutory category of a machine. See MPEP § 2106.03.
Step 2A:
Step 2A is a two-prong inquiry. See MPEP § 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. See MPEP § 2106.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. See MPEP § 2106.04(d).
Claim 1 Step 2A Prong One: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
A method comprising:
receiving an incomplete process model comprising a first graph having an unlabeled node;
extracting a sequence from the incomplete process model;
verbalizing the sequence to create an input sequence;
processing the input sequence as an activity-recommendation to a fine-tuned language model, the fine-tuned language model trained from a process model repository having a first vocabulary;
receiving from the processing, an output sequence including a term outside of the first vocabulary;
storing the output sequence in a non-transitory computer readable storage medium; and
providing the output sequence as a label recommendation for the unlabeled node.
The limitations “extracting a sequence from the incomplete process model” and “verbalizing the sequence to create an input sequence” are abstract ideas because they are directed to mental processes, observations, evaluations, judgments, and/or opinions. The limitations, as drafted and under broadest reasonable interpretation, “can be performed in the human mind or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). For example, a human could extract a sequence of nodes from a graph/model and string together (concatenate) types and labels of nodes. See Para. [0023] of specification indicating incomplete process model is in graph form, comprising nodes and edges, and Para. [0051] of specification reciting “verbalization strings together the types and (cleaned) labels of the nodes”. Thus, claim 1 recites an abstract idea(s).
Claim 1 Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “receiving an incomplete process model comprising a first graph having an unlabeled node”, “receiving from the processing, an output sequence including a term outside of the first vocabulary” and “providing the output sequence as a label recommendation for the unlabeled node” (insignificant extra-solution activity – mere data gathering/inputting and/or outputting – see MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data gathering/inputting in conjunction with the abstract idea (see MPEP § 2106.05(g)).
• “processing the input sequence as an activity-recommendation to a fine-tuned language model, the fine-tuned language model trained from a process model repository having a first vocabulary” and “storing the output sequence in a non-transitory computer readable storage medium” (mere instructions to apply an exception to a computer – see MPEP 2106.04(d) referencing MPEP 2106.05(f); these limitations can be viewed as nothing more than high level recitations of generic computer components or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a generic computer (see MPEP 2106.05(f)).
Claim 1 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The Examiner must consider whether each claim limitation individually or as an ordered combination amount to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there are two types of additional element. The first type of additional element (“receiving an incomplete process model”, “receiving … an output sequence” and “providing an output sequence”), as explained previously, are insignificant extra-solution activity (mere data inputting/gathering and/or data outputting). These recitations are recited at a high level of generality, and are also well-known. These limitations therefore remain insignificant extra-solution activity even upon reconsideration. Thus, these limitations do not amount to significantly more.
The second type of additional element is the generic computer components (“processing … to the fine-tuned language model”, “storing the output”), which are high level recitations of generic computer component(s) or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer. See MPEP § 2106.05(f). Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. See MPEP § 2106.05(f).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or data gathering, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 1 does not include significantly more than the abstract idea. The claim 1 is not patent subject matter eligible. Dependent claims 2-9 are further addressed below after addressing each independent claim.
Claim 10 Step 2A Prong One: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
10. A non-transitory computer readable storage medium embodying a computer program for performing a method, said method comprising:
training a fine-tuned language model by, extracting a training sequence from a process model comprising a graph in a process model repository, the process model repository having a first vocabulary, verbalizing the training sequence, and providing the verbalized training sequence to a pre-trained language model;
receiving an incomplete process model comprising another graph having an unlabeled node;
extracting a sequence from the incomplete process model;
verbalizing the sequence to create an input sequence;
processing the input sequence as an activity-recommendation to the fine-tuned language model;
receiving from the processing, an output sequence including a term outside of the first vocabulary;
storing the output sequence in a non-transitory computer readable storage medium; and
providing the output sequence as a label recommendation for the unlabeled node.
The limitations “extracting a training sequence from a process model comprising a graph in a process model repository, the process model repository having a first vocabulary”, “verbalizing the training sequence”, “extracting a sequence from the incomplete process model” and “verbalizing the sequence to create an input sequence” are abstract ideas because they are directed to mental processes, observations, evaluations, judgments, and/or opinions. The limitations, as drafted and under broadest reasonable interpretation, “can be performed in the human mind or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). For example, a human could extract a sequence of nodes from a graph/model and string together (concatenate) types and labels of nodes. See Para. [0023] of specification indicating incomplete process model is in graph form, comprising nodes and edges, and Para. [0051] of specification reciting “verbalization strings together the types and (cleaned) labels of the nodes”. Thus, claim 1 recites an abstract idea(s).
Claim 10 Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “non-transitory computer readable storage medium embodying a computer program for performing a method”, “training a fine-tuned language model”, “providing the verbalized training sequence to a pre-trained language model”, “processing the input sequence as an activity-recommendation to the fine-tuned language model” and “storing the output sequence in a non-transitory computer readable storage medium” (mere instructions to apply an exception to a computer – see MPEP 2106.04(d) referencing MPEP 2106.05(f); these limitations can be viewed as nothing more than high level recitations of generic computer components or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a generic computer (see MPEP 2106.05(f)).
• “receiving an incomplete process model comprising another graph having an unlabeled node”, “receiving from the processing, an output sequence including a term outside of the first vocabulary” and “providing the output sequence as a label recommendation for the unlabeled node” (insignificant extra-solution activity – mere data gathering/inputting and/or outputting – see MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data gathering/inputting in conjunction with the abstract idea (see MPEP § 2106.05(g)).
Claim 10 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The Examiner must consider whether each claim limitation individually or as an ordered combination amount to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there are two types of additional element. The first type of additional element is the generic computer components (“non-transitory … medium”, “training a fine-tuned language model”, “providing … sequence to … model”, “processing … to the fine-tuned language model”, “storing the output”), which are high level recitations of generic computer component(s) or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer. See MPEP § 2106.05(f). Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. See MPEP § 2106.05(f).
The second type of additional element (“receiving an incomplete process model”, “receiving … an output sequence” and “providing an output sequence”), as explained previously, are insignificant extra-solution activity (mere data inputting/gathering and/or data outputting). These recitations are recited at a high level of generality, and are also well-known. These limitations therefore remain insignificant extra-solution activity even upon reconsideration. Thus, these limitations do not amount to significantly more.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or data gathering, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 10 does not include significantly more than the abstract idea. The claim 10 is not patent subject matter eligible. Dependent claims 11-14 are further addressed below after addressing each independent claim.
Claim 15 Step 2A Prong One: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
15. A computer system comprising: one or more processors; a software program, executable on said computer system, the software program configured to:
train a fine-tuned language model by, extracting a training sequence from a process model comprising a graph in a process model repository stored in a database, the process model repository having a first vocabulary, verbalizing the training sequence, and providing the verbalized training sequence to a pre-trained language model;
receive an incomplete process model comprising another graph having an unlabeled node;
extract a sequence from the incomplete process model;
verbalize the sequence to create an input sequence;
process the input sequence as an activity-recommendation to the fine-tuned language model;
receive from the processing, an output sequence including a term outside of the first vocabulary;
store the output sequence in the database; and
provide the output sequence as a label recommendation for the unlabeled node.
The limitations “extracting a training sequence from a process model comprising a graph in a process model repository stored in a database, the process model repository having a first vocabulary”, “verbalizing the training sequence”, “extract a sequence from the incomplete process model” and “verbalize the sequence to create an input sequence” are abstract ideas because they are directed to mental processes, observations, evaluations, judgments, and/or opinions. The limitations, as drafted and under broadest reasonable interpretation, “can be performed in the human mind or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). For example, a human could extract a sequence of nodes from a graph/model and string together (concatenate) types and labels of nodes. See Para. [0023] of specification indicating incomplete process model is in graph form, comprising nodes and edges, and Para. [0051] of specification reciting “verbalization strings together the types and (cleaned) labels of the nodes”. Thus, claim 1 recites an abstract idea(s).
Claim 15 Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “computer system comprising: one or more processors; a software program, executable on said computer system”, “train a fine-tuned language model”, “providing the verbalized training sequence to a pre-trained language model”, “process the input sequence as an activity-recommendation to the fine-tuned language model” and “store the output sequence in the database” (mere instructions to apply an exception to a computer – see MPEP 2106.04(d) referencing MPEP 2106.05(f); these limitations can be viewed as nothing more than high level recitations of generic computer components or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a generic computer (see MPEP 2106.05(f)).
• “receive an incomplete process model comprising another graph having an unlabeled node”, “receive from the processing, an output sequence including a term outside of the first vocabulary” and “provide the output sequence as a label recommendation for the unlabeled node” (insignificant extra-solution activity – mere data gathering/inputting and/or outputting – see MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data gathering/inputting in conjunction with the abstract idea (see MPEP § 2106.05(g)).
Claim 15 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The Examiner must consider whether each claim limitation individually or as an ordered combination amount to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there are two types of additional element. The first type of additional element is the generic computer components (“processors … software program”, “training a fine-tuned language model”, “providing … sequence to … model”, “process … to the fine-tuned language model”, “store the output”), which are high level recitations of generic computer component(s) or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer. See MPEP § 2106.05(f). Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. See MPEP § 2106.05(f).
The second type of additional element (“receiving an incomplete process model”, “receive … an output sequence” and “provide an output sequence”), as explained previously, are insignificant extra-solution activity (mere data inputting/gathering and/or data outputting). These recitations are recited at a high level of generality, and are also well-known. These limitations therefore remain insignificant extra-solution activity even upon reconsideration. Thus, these limitations do not amount to significantly more.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or data gathering, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 15 does not include significantly more than the abstract idea. The claim 15 is not patent subject matter eligible. Dependent claims 16-20 are further addressed below.
Dependent Claims 2-9, 11-14 and 16-20
Regarding claims 2-9 and 11, claim 2 depends from claim 1 and further recites: “further comprising: prior to receiving the incomplete process model, training the fine-tuned language model by: extracting a training sequence from a process model comprising a second graph in the process model repository, verbalizing the training sequence, and providing the verbalized training sequence to a pre-trained language model”, claim 3 depends from claim 1 and further recites: “wherein the first graph comprises a directed attributed graph”, claim 4 depends from claim 1 and further recites: “wherein the first graph is in the Business Process Modeling Notation (BPMN) format”, claim 5 depends from claim 1 and further recites: “further comprising: also receiving from the processing, another output sequence; ranking the output sequence and the another output sequence; and providing the another output sequence as another label recommendation”, claim 6 depends from claim 5 and further recites: “wherein the ranking comprises aggregating”, claim 7 depends from claim 5 and further recites: “wherein the ranking comprises a maximum strategy”, claim 8 depends from claim 1 and further recites: “wherein the processing further comprises beam search”, claim 9 depends from claim 7 and further recites: “wherein the processing further comprises calculating an n gram penalty” and claim 11 depends from claim [10] and further recites: “wherein the graph and the another graph comprise directed attributed graphs”.
These features have been considered in combination with the features required by the claim(s) from which these claims depend. The bolded portion of the additional features are considered to further clarify the details of the mathematical concepts and/or the human’s mental activity (e.g., with pen and paper). See MPEP §§ 2106.04(a)(2)(I) and (III). The not bolded features of claims 2 and 8 are considered to be generic computer component(s) or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer. See MPEP § 2106.05(f). In addition, the not bolded features of claim 5 are considered to be insignificant extra-solution activity of data gathering/inputting and/or outputting, which cannot provide an inventive concept, and is well-understood, routine and conventional. See MPEP 2106.05(g); See also MPEP § 2106.05(d)(II) (“The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”)). Therefore, these features are considered to be drawn to the abstract idea without adding significantly more, and hence claims 2-9 and 11 are considered to be ineligible under 35 U.S.C. § 101.
Claims 12 and 17 have substantially similar limitations as recited in claim 8; therefore, they are rejected under 35 U.S.C. § 101 for the same reasons.
Claims 13 and 18 have substantially similar limitations as recited in claim 9; therefore, they are rejected under 35 U.S.C. § 101 for the same reasons.
Claims 14 and 19 have substantially similar limitations as recited in claim 5; therefore, they are rejected under 35 U.S.C. § 101 for the same reasons.
Claim 16 has substantially similar limitations as recited in claim 11; therefore, it is rejected under 35 U.S.C. § 101 for the same reasons.
Claim 20 has substantially similar limitations as recited in claim 7; therefore, it is rejected under 35 U.S.C. § 101 for the same reasons.
For the foregoing reasons, claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to patent ineligible subject matter.
Claim Rejections - 35 U.S.C. § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 10, 11, 14-16, 19 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over GERBER et al. (U.S. Patent Application Publication No. 2022/0147842 A1) in view of BARLEW et al. (U.S. Patent Application Publication No. 2023/0252006 A1).
Regarding claim 1, GERBER discloses a method comprising: receiving an incomplete process model comprising a first graph having an unlabeled node (given an incomplete business process graph B with its unlabeled node n, Para. [0065] of GERBER; See also given incomplete model B, Para. [0069] of GERBER); processing the input sequence as an activity-recommendation (the assistance approach is context-aware, which means that it takes the current progress of modeling as a context for the recommendation into account, Para. [0022] of GERBER; See also one possible recommendation approach in business process modeling is activity recommendation … given the business process model being worked on, the recommendation system makes suggestions regarding suitable activities to extend the model at a user-defined position, Para. [0023] of GERBER; [user defined is interpreted as input]) to a fine-tuned language model (the current rule learner is based on a top-down search implemented in association rule mining systems such as WARMR and AMIE, Para. [0027] of GERBER; [AMIE is interpreted as Articulate Medical Intelligence Explorer, which is large language model–based AI agent]), the fine-tuned language model trained from a process model repository having a first vocabulary (offline rule learning process 420 can include applying a plurality of rule templates to historical business process models within the repository 410 so that the (instantiated) rules with confidence values 430 can be generated, Para. [0072] of GERBER; [rule learning by applying rule templates to historical business process models within the repository is interpreted as training a language model from a process model repository]; See also modeling a domain-specific process can be challenging in that such processes may require a specialized and sometimes technical vocabulary, Para. [0002] of GERBER; See also rules learned from B can be used and applied, Para. [0065] of GERBER); receiving from the processing, an output sequence including a term outside of the first vocabulary (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 … at least a portion of the ranked activity recommendations is displayed in the graphical user interface, Para. [0003] of GERBER); storing the output sequence in a non-transitory computer readable storage medium (transmit data and instructions to, a storage system, Para. [0077] of GERBER; See also non-transitory processor-readable storage medium, Para. [0074] of GERBER); and providing the output sequence as a label recommendation for the unlabeled node (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 … at least a portion of the ranked activity recommendations is displayed in the graphical user interface, Para. [0003] of GERBER).
GERBER does not appear to explicitly disclose extracting a sequence from the incomplete process model; verbalizing the sequence to create an input sequence.
BARLEW, however, is in the field of creating graphs (Para. [0001] of BARLEW) and teaches extracting a sequence from the incomplete process model (even if “Extracted Entity” relationship is not a component shown in visual input area 1304, it can still be recorded in the graph database … assuming that the segments and the unit of input data have representations in the graph database, then an “Extracted Entity” relationship is represented in the database and thus in the underlying annotation graph, Para. [0104] of BARLEW); verbalizing the sequence to create an input sequence (the graph database can also be converted into a universal or proprietary text-based format that can then be consumed by other graph visualizing tools to visualize the relationships between the nodes and edges of the current graph, inclusive of the relationships defined by the nodes and edges of its imported subgraphs, Para. [0055] of BARLEW; [converting the graph (nodes/edges) into text sequence/string is interpreted as verbalizing based on Applicant’s specification at Para. [0051]).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the method of Business Process Modeling Recommendation of GERBER with the text-based method of BARLEW for the purpose of user-friendliness for creating graphs (Para. [0001] of BARLEW).
Regarding claim 2, GERBER as modified by BARLEW discloses a method as in claim 1 further comprising: prior to receiving the incomplete process model, training the fine-tuned language model by: extracting a training sequence from a process model comprising a second graph in the process model repository (even if “Extracted Entity” relationship is not a component shown in visual input area 1304, it can still be recorded in the graph database … assuming that the segments and the unit of input data have representations in the graph database, then an “Extracted Entity” relationship is represented in the database and thus in the underlying annotation graph, Para. [0104] of BARLEW; See also obtain multiple sets of annotations (annotation graphs) submitted from different annotator devices corresponding to the same unit of input data and then determine an aggregated annotation result corresponding to that unit of input data by comparing the annotations to each other, Para. [0027] of BARLEW), verbalizing the training sequence (the graph database can also be converted into a universal or proprietary text-based format that can then be consumed by other graph visualizing tools to visualize the relationships between the nodes and edges of the current graph, inclusive of the relationships defined by the nodes and edges of its imported subgraphs, Para. [0055] of BARLEW; [converting the graph (nodes/edges) into text sequence/string is interpreted as verbalizing based on Applicant’s specification at Para. [0051] of BARLEW), and providing the verbalized training sequence to a pre-trained language model (annotations of the input data are collected and to be used as training data into a new or existing machine learning model to teach the model to better programmatically annotate subsequent input data, Para. [0019] of BARLEW).
Regarding claim 3, GERBER as modified by BARLEW discloses a method as in claim 1 wherein the first graph comprises a directed attributed graph (directed attributed graph, Para. [0028] of GERBER).
Regarding claim 4, GERBER as modified by BARLEW discloses a method as in claim 1 wherein the first graph is in the Business Process Modeling Notation (BPMN) format (various modeling notations as Petri nets or BPMN are available to capture business processes and the current subject matter can utilize any such notations, Para. [0028] of GERBER).
Regarding claim 5, GERBER as modified by BARLEW discloses a method as in claim 1 further comprising: also receiving from the processing, another output sequence (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 … at least a portion of the ranked activity recommendations is displayed in the graphical user interface, Para. [0003] of GERBER); ranking the output sequence and the another output sequence (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 … at least a portion of the ranked activity recommendations is displayed in the graphical user interface, Para. [0003] of GERBER); and providing the another output sequence as another label recommendation (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 … at least a portion of the ranked activity recommendations is displayed in the graphical user interface, Para. [0003] of GERBER).
Regarding claim 6, GERBER as modified by BARLEW discloses a method as in claim 5 wherein the ranking comprises aggregating (if several rules make the same recommendation (in other words: predict the same label), the maximum confidence can be assigned to this recommendation … instead of taking the maximum confidence, other aggregation methods are possible … for example, it is possible to use an aggregation method that is based on a noisy-or or an aggregation method that also takes the interrelations between the rule templates illustrated in diagram 300 in FIG. 3 into account, Para. [0069] of GERBER).
Regarding claim 7, GERBER as modified by BARLEW discloses a method as in claim 5 wherein the ranking comprises a maximum strategy (if several rules make the same recommendation (in other words: predict the same label), the maximum confidence can be assigned to this recommendation, Para. [0069] of GERBER).
Claim 10 has substantially similar limitations as recited in method claim 2 in terms of a nontransitory medium; therefore, it is rejected under 35 U.S.C. § 103, mutatis mutandis, for the same reasons. See also GERBER teaches non-transitory computer readable storage medium embodying a computer program for performing a method (non-transitory computer program products (i.e., physically embodied computer program products) are also described that store instructions, which when executed by one or more data processors of one or more computing systems, cause at least one data processor to perform operations herein., Para. [0072] of GERBER).
Regarding claim 11, GERBER as modified by BARLEW discloses a non-transitory computer readable storage medium as in claim 11 wherein the graph and the another graph comprise directed attributed graphs (Business Process Models and Business Process Graphs [plural] … business process model can correspond to a directed attributed graph, Para. [0028] of GERBER).
Claim 15 has substantially similar limitations as recited in method claim 1 in terms of a computer system; therefore, it is rejected under 35 U.S.C. § 103, mutatis mutandis, for the same reasons. See also GERBER teaches computer system comprising: one or more processors; a software program, executable on said computer system (non-transitory computer program products (i.e., physically embodied computer program products) are also described that store instructions, which when executed by one or more data processors of one or more computing systems, cause at least one data processor to perform operations herein., Para. [0072] of GERBER).
Claims 14 and 19 have substantially similar limitations as recited in claim 5; therefore, they are rejected under 35 U.S.C. § 103 for the same reasons.
Claim 16 has substantially similar limitations as recited in claim 11; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claim 20 has substantially similar limitations as recited in claim 7; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claims 8, 12 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over GERBER et al. (U.S. Patent Application Publication No. 2022/0147842 A1) in view of BARLEW et al. (U.S. Patent Application Publication No. 2023/0252006 A1), and further in view of BARLEW et al. (U.S. Patent Application Publication No. 2012/0253783 A1).
Regarding claim 8, GERBER as modified by BARLEW discloses a method as in claim 1 (as shown above) but appears to fail to explicitly disclose wherein the processing further comprises beam search.
CASTELLI, however, teaches wherein the processing further comprises beam search (beam-search decoder is used to generate final translations, Para. [0003] of CASTELLI).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the method of Business Process Modeling Recommendation of GERBER as modified by BARLEW with the NLP method of CASTELLI for the purpose of optimizing results output by a natural language processing system (Abstract of CASTELLI).
Claims 12 and 17 have substantially similar limitations as recited in claim 8; therefore, they are rejected under 35 U.S.C. § 103 for the same reasons.
Claims 9, 13 and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over GERBER et al. (U.S. Patent Application Publication No. 2022/0147842 A1) in view of BARLEW et al. (U.S. Patent Application Publication No. 2023/0252006 A1), and further in view of BARLEW et al. (U.S. Patent Application Publication No. 2008/0243481 A1).
Regarding claim 9, GERBER as modified by BARLEW discloses a method as in claim 7 (as shown above) but appears to fail to explicitly disclose wherein the processing further comprises calculating an n gram penalty.
BRANTS, however, is in the field of large language models (title of BRANTS) teaches wherein the processing further comprises calculating an n gram penalty (the backoff factor applies a penalty to the relative frequency of the backoff n-gram (e.g., to compensate for the n-gram not being present), Para. [0036] of BRANTS).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the method of Business Process Modeling Recommendation of GERBER as modified by BARLEW with the n-gram penalty method of BRANTS for the purpose of increasing language model accuracy (Para. [0036] of BRANTS).
Claims 13 and 18 have substantially similar limitations as recited in claim 9; therefore, they are rejected under 35 U.S.C. § 103 for the same reasons.
Conclusion
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/JOHN P HOCKER/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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