DETAILED ACTION
The following FINAL Office Action is in response to Applicant’s Response filed on 12/15/2025.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Status of Claims
Claims 1-20 were previously pending and subject to a non-final Office Action mailed 09/30/2025. Claims 1, 8, and 15 were amended. Claims 1-20 are currently pending and are subject to the final Office Action below.
Response to Arguments
35 USC § 101
Applicant’s arguments, see pages 7-12, filed 12/15/2025, with respect to the 35 U.S.C. 101 rejections of Claims 1-20 have been fully considered and are not persuasive.
Applicant argues that the present claims provide an improvement in computer-related technology similar to Desjardins. Examiner respectfully disagrees.
First, the challenge in the field of business process management is not a technical challenge but a business problem as it is difficult for businesses to optimize workflows, automate processes, and ensure repeatability when processes are unstructured/dynamic.
Next, Desjardin provided an improvement in computer functionality. For example, an improved way of training a machine learning model that protected the model’s knowledge about previous tasks while allowing it to learn new tasks or improvements to computer component/system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams. In contrast to Desjardin, Applicant’s claims do not improve the way a machine learning model is trained or improve a computer component or system performance by adjusting parameters of a machine learning model.
MPEP 2106.05(a)(II) states “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
Applicant’s claimed invention allows organizations/businesses to perform process implementations and benefit from efficiency, automation, and reliability gains. Such benefits/improvements are improvements to the abstract idea of business relations where a user or an organization is receiving recommendations on how to optimize their business process.
Examiner’s conclusion is further supported by specification paragraph 32 which states “An optimization, informed by the data analysis, AI insights, and heuristic assessments, allows for improved results. Structured recommendations emerged, including the removal of redundant or occasionally used activities, reassignment of resources, and the introduction of efficiency-improving measures. Resource allocation optimization was achieved, ensuring that resources were efficiently matched to the demands of each case or activity. Efficiency improvements includes the streamlining of complex decision-making, automation of repetitive tasks, and optimization of activity sequencing. The optimized process aligned with industry best practices, regulatory requirements, and organizational standards. The optimized unstructured process can be seamlessly transitioned into a structured model such as BPMN, facilitating efficient and well-defined implementation”.
Applicant argues that the claims are not directed to an abstract idea. Examiner respectfully disagrees as given broadest reasonable interpretation, the claims are directed to business relations where a user or an organization is receiving recommendations on how to optimize their business process. Further, MPEP 2106.04(a)(2) states “thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping.”
Applicant argues that the claims recite specific steps/components for achieving the technical solution not merely field of use or generic computer implementation. Examiner respectfully disagrees. Applicant cited Desjardin which used less storage capacity, reduced system complexity, and protected knowledge about previous tasks. However, such improvements are not recited within Applicant’s claims.
The additional elements are merely used to perform the limitations directed to the abstract idea and amount to no more than mere instructions to apply the exception using a generic computer or field of use.
Applicant argues that the amendment removes any ambiguity that the model could be a conventional prescriptive flow and instead recites a declarative, outcomes-based case model and further “narrows the claim to a specific class of computer implemented models that present concreate technical challenges (e.g., absent prescribed pathways, dynamic sentries/milestones) addressed by the claimed pipeline…reframes the claimed models as specific technical artifacts with defined semantics not abstract concepts”. Examiner respectfully disagrees.
Given broadest reasonable interpretation “accessing an unstructured process model defining outcomes to be achieved without providing prior to execution of the unstructured process model, steps to achieve the outcomes” may amount to a business process as described in paragraph 33 of Applicant’s specification where an unstructured process model may involve ticket handling and include stages, tasks, and milestones – “Case: Ticket Handling
Stage 1: Analysis
Task 1: Upon the arrival of a ticket, it is assigned to L1 support for initial analysis.
Milestone: Closure milestone is initiated to track the progress.
Stage 2: Resolution
Task 2: L1 support provides a solution to address the ticket.
Milestone: Closure milestone is achieved to signify the ticket's successful resolution.
Stage 3: Optional Actions (Discretionary Tasks)
Task 3: L1 support may choose to review previous tickets for reference.
Task 4: L1 support may consult experts for guidance.
Task 5: L1 support may contact the customer for clarification.
Task 6: After resolving the ticket, L1 support may follow up with the customer to assess satisfaction.”
Thus, the unstructured process model is not a “technical artifact” but rather a business process which is input into a machine learning model and a heuristic rules engine to derive recommendations to optimize the business process.
Regarding Applicant’s argument that the recitations of the machine learning model and heuristic rules engine convert the models from mere objects of observation into technical data structures that are actively analyzed, optimized and transformed, Examiner respectfully disagrees that such a feature would render the claim eligible. The model, as stated previously, is a business process and the abstract idea is the process of analysis, optimization, and transformation as the business process is optimized and improved.
Regarding Applicant’s argument that the unstructured process model is a computable representation with declarative semantics (discretionary tasks, sentries, milestones) processed by specific ML and heuristic components to produce optimization artifacts that are then applied to the model itself, Examiner respectfully disagrees that such features (declarative semantics, optimization artifacts) are recited within the claim.
Specification paragraphs 34-35 state “Examples of such insights may include:
1) L1 never resolved issues with “runtime module” and always involves “Person A” for consultation.
2) Even after finding product related queries in previous ticket-always manager is involved for consultation if the customer's contract is above 100 million.
3) Customer satisfaction was asked only in 2 cases, etc.
These insights can then be used to optimize the CMMN by creating several recommended ways to optimize the CMMN, such as removing the step of “customer satisfaction”, specifying an explicit mandatory task with century condition to involve the manager for big contracts, and always involve “Person A” if the ticket is related to “runtime module.””
Thus, the “optimization artifacts” are merely recommendations provided to the business for the business to modify their process.
Accordingly, the 35 U.S.C. 101 rejection is maintained.
35 USC § 103
Applicant’s arguments, see pages 12-18, filed 12/15/2025, with respect to the 35 U.S.C. 102/103 rejections of Claims 1-20 have been fully considered and are not persuasive.
Applicant argues that Wilk does not teach “converting”. Examiner respectfully disagrees. Wilk enriches processes with additional contextual data such that the activities within the process may be identified for automation (para. 12 and 33). Further, Wilk in paragraphs 42 and 52 teaches enabling an RPA bot to perform additional tasks or activities and thus, automating processes which were previously not automated. Examiner interprets the automated processes (processes with additional contextual data) as the structured process.
Applicant further argues that Wilk does not address technical challenges of mapping discretionary tasks, sentries or milestones to BPMN constructs and does not disclose handling, optimization, or conversion of unstructured process models (e.g. CMMN or other declarative paradigms). Examiner respectfully notes that the features upon which applicant relies (i.e., BPMN, CMMN, paradigms, etc.) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant further argues that enabling an RPA is not a semantic conversion of a declarative case model into prescriptive BPMN or similar constructs. Again, such features are not recited in the rejected claims. Given the broadest reasonable interpretation, Wilk teaches “converting” as a non-automated “manual” task may be converted into an automated task for performance by the RPA bot.
Applicant’s arguments with respect to reference Diaz have been considered but are moot because the new ground of rejection relies upon the combination of Wilk and Hooks.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 8, and 15 recite the limitation of “accessing an unstructured process model defining outcomes to be achieved without providing, prior to execution of the unstructured process model, steps to achieve the outcomes”.
The closest description of such a feature is in paragraph 11 and 46 of the specification “CMMN and Business Process Model and Notation (BPMN) represent two distinct paradigms in the domain of process modeling. CMMN, grounded in a declarative approach, excels in managing intricate and unstructured business cases, emphasizing flexibility through the definition of outcomes without prescribing granular steps” and “FIG. 3 is a flow diagram illustrating a method 300 for converting an unstructured process model to a structured process model, in accordance with an example embodiment. At operation 310, an unstructured process model defining a sequence of operations to be performed is accessed. At operation 320, context data and execution log regarding the unstructured process model, the context data including data gathered during past executions of the unstructured process model is accessed.”
The specification provides a brief overview of CMMN, but the specification does not support “accessing an unstructured process model defining outcomes to be achieved without providing, prior to execution of the unstructured process model, steps to achieve the outcomes”.
In contrast, specification paragraph 46 describes an unstructured process model defining a sequence of operations to be performed. To be clear, the specification supports providing steps and does not support “without providing, prior to execution of the unstructured process model, steps to achieve the outcomes”.
Dependent claims 2-7, 9-14, and 16-20 inherit the rejection as they do not cure the deficiencies of the independent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-7 are directed to a system (i.e., a machine), Claims 8-14 are directed to a method (i.e., a process), and Claims 15-20 are directed to a system (i.e., a machine). Therefore, the claims all fall within one of the four statutory categories of invention.
Step 2A Prong 1
Independent Claim 1, 8, and 15 recites:
accessing an unstructured process model defining outcomes to be achieved without providing, prior to execution of the unstructured process model, steps to achieve the outcomes;
accessing context data regarding the unstructured process model, the context data including data gathered during past executions of the unstructured process model;
passing the unstructured process model and the context data …to output one or more recommendations on how to optimize an input unstructured process model, based on the context data, … output a first set of one or more recommendations on how to optimize the unstructured process model;
passing the unstructured process model and the context data … to apply rules defining recommended process patterns and behaviors to generate a second set of one or more recommendations on how to optimize the unstructured process model or use a process stored … based on comparison of attributes in the unstructured process model and context data with attributes stored…;
optimizing the unstructured data model by applying one or more recommendations in the first and/or second sets; and
converting the optimized unstructured data model into a structured data model
The limitations stated above are processes that under broadest reasonable interpretation covers “certain methods of organizing human activity” (“commercial interactions” or “managing personal behavior or relations or interactions between people”). Specifically, business relations where a user or an organization is receiving recommendations on how to optimize their business process in light of Applicant’s specification paragraph 34-35 “The context data and runtime execution logs can then be used as input to an AI/ML analysis to provide one or more insights about the case. Examples of such insights may include: 1) L1 never resolved issues with “runtime module” and always involves “Person A” for consultation. 2) Even after finding product related queries in previous ticket-always manager is involved for consultation if the customer's contract is above 100 million. 3) Customer satisfaction was asked only in 2 cases, etc. These insights can then be used to optimize the CMMN by creating several recommended ways to optimize the CMMN, such as removing the step of “customer satisfaction”, specifying an explicit mandatory task with century condition to involve the manager for big contracts, and always involve “Person A” if the ticket is related to “runtime module””.
Further, specification paragraph 32 states “An optimization, informed by the data analysis, AI insights, and heuristic assessments, allows for improved results. Structured recommendations emerged, including the removal of redundant or occasionally used activities, reassignment of resources, and the introduction of efficiency-improving measures. Resource allocation optimization was achieved, ensuring that resources were efficiently matched to the demands of each case or activity. Efficiency improvements includes the streamlining of complex decision-making, automation of repetitive tasks, and optimization of activity sequencing. The optimized process aligned with industry best practices, regulatory requirements, and organizational standards. The optimized unstructured process can be seamlessly transitioned into a structured model such as BPMN, facilitating efficient and well-defined implementation”.
Accordingly, when given the broadest reasonable interpretation in light of specification paragraph 32, “optimizing” recites mathematical calculations. Thus, the claim will be considered as falling within the “mathematical concepts” grouping as well.
Accordingly, the claims recite an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. The independent claims also recite at least one hardware processor, a computer-readable medium, a first machine learning model, a heuristic rules engine, a repository, and a repository of processes.
The additional elements of at least one hardware processor, a computer-readable medium, a first machine learning model, a heuristic rules engine, a repository, and a repository of processes are all recited at a high-level of generality (generic computer/functions) such that when viewed as a whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components. See MPEP 2106.05(f).
Further, a first machine learning model “trained to output one or more recommendations” and a heuristic rules engine which “apply rules… to generate a second set of one or more recommendations” limits the abstract idea to a particular field of use. Specifically, limiting the claims to the field of process modelling/mining/enhancement where analysis of the unstructured process model and context data is performed by the first machine learning model and heuristic rules engine.
Thus, the claim as a whole, looking at additional elements individually and in combination, does not integrate the judicial exception into a practical application as the additional elements are mere instructions to apply the judicial exception using generic computer components and field of use which does not impose meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of at least one hardware processor, a computer-readable medium, a first machine learning model, a heuristic rules engine, a repository, and a repository of processes to perform the steps/functions recited above amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply the exception using a generic computer component cannot provide an inventive concept.
Again, a first machine learning model “trained to output one or more recommendations” and a heuristic rules engine which “apply rules….to generate a second set of one or more recommendations” limits the abstract idea to a particular field of use. Specifically, limiting the claims to the field of process modelling/mining/enhancement where analysis of the unstructured process model and context data is performed by the first machine learning model and heuristic rules engine.
None of the steps/functions of Claim 1, Claim 8, and Claim 15 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to the abstract idea and amount to no more than mere instructions to apply the exception using a generic computer or field of use, thus, the analysis does not change when considered as an ordered combination. Further, the additional elements do not meaningfully limit the claim. Accordingly, Claim 1, Claim 8, and Claim 15 are ineligible.
Dependent Claims 2, 9, and 16 further specify transforming the context data using “data normalization”. When considered as an additional element, data normalization amounts to extra-solution activity. Specifically, “selecting a particular data source or type of data to be manipulated”. See MPEP2106.05(g).
Applicant’s specification para. 16 “The collected data undergoes rigorous pre-processing, ensuring its quality and consistency. Data cleaning procedures systematically eliminate errors and inaccuracies, guaranteeing the reliability and accuracy of the dataset. Data normalization is then applied to standardize units of measurement, making it possible to compare and analyze different data elements effectively” demonstrates that data normalization is well-known, routine, and conventional as it need not be described in detail.
Dependent Claims 3, 10, and 17 recite “extracting”, dependent claims 4, 11, and 18 recite “calculate”, dependent Claims 5, 12, and 19 recite “group”, dependent claims 6, 13, and 20 recite “reduce” which are limitations that further narrow the abstract idea of organizing human activity. Further, feature extraction, Naïve Bayes classifier (used to calculate a probability), K-means clustering, and PCA are directed to mathematical calculations or relationships and thus, fall into the abstract idea grouping of “mathematical concepts” as well.
The feature extraction process, wherein the first machine learning model is a Naïve Bayes classifier, wherein the first machine learning model is a K-means clustering model, and Principal Component Analysis, when viewed as additional elements, amounts to limits the abstract idea to a particular field of use. Specifically, limiting the claims to the field of process modelling/mining/enhancement where analysis and processing of the context data is limited to execution by the feature extraction process, Naïve Bayes classifier, K-means clustering model, and/or PCA.
Dependent Claims 7 and 14 further specify suggesting the one or more recommendations to a user via a user interface prior to the optimizing. “Suggesting” is part of the abstract idea of organizing human activity in light of specification paragraph 50 where recommendations are displayed to the user allowing the user to choose to modify the model or not. The user interface is recited at a high-level of generality (generic computer/functions) such that when viewed as a whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components.
Again, such limitations are further directed towards mathematical concepts and/or organizing human activity as a user or an organization is receiving recommendations on how to optimize their business process and the additional elements are extra-solution activity, mere instructions to apply the judicial exception or field of use. Thus, taken alone and when viewed in combination, nothing in dependent claims 2-7, 9-14, and 16-20 adds additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-20 are ineligible.
Claim Rejections - 35 USC § 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.
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 is advised of the obligation under 37 CFR 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, 3, 7-8, 10, 14-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wilk et al. (US2025/0200485) in view Hooks et al. (US Patent No. 12,118,490).
As per independent Claim 1, Claim 8, and Claim 15,
Wilk teaches a system comprising: at least one hardware processor; and a computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising: (figure 1 and para. 20-21, 57)/ a method comprising:/ a system comprising: at least one hardware processor; and a computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising: (figure 1 and para. 20-21, 57)
accessing an unstructured process model defining outcomes to be achieved without providing, prior to execution of the unstructured process model, steps to achieve the outcomes (broadly figure 2 and para. 35-41 where in para. 36 the user selects a process to be analyzed and in para. 38 a process is a collection of activities/tasks in which a specific sequence produces a product/service for customers and further, unstructured data of the one or more processes is included – “The data associated with the one or more different processes may include… process outcomes”)
accessing context data regarding the unstructured process model, the context data including data gathered during past executions of the unstructured process model (para. 37 where the user may select process event logs corresponding to each process the user wishes for the module to analyze and the process event logs are a collection of time-stamped event records produced through execution of a process)
passing the unstructured process model and the context data into a first machine learning model, the first machine learning model trained to output one or more recommendations on how to optimize an input unstructured process model, based on the context data, thereby causing the first machine learning model to output a first set of one or more recommendations on how to optimize the unstructured process model (para. 37 where the module may utilize linguistic analysis techniques to analyze the process event logs and the linguistic analysis techniques may include a machine learning model; para. 42 the module identifies activities within the process which may be automated using a Robotic Process Automation (RPA) or other tool; para. 43-47 where scores are generated for the process or activity within the process; para. 48 the module updates the process within the user interface using the scores; para. 49-50 the module provides one or more recommendations to the user using the output of the machine learning model so that the user may select which of the recommendations to implement)
optimizing the unstructured data model by applying one or more recommendations in the first and/or second sets (para. 51 the module monitors the implementation of the recommendations and updates the scores after the implementation of each recommendation and/or change to the process; para. 52-53 requesting additional details and feedback)
converting the optimized unstructured data model into a structured data model (para. 54 the module updates the process with additional contextual data (“optimized unstructured data model”) – the additional contextual data is used to generate an enriched process (“structured data model”); para. 52 where the module uses the feedback to enable the RPA bot to perform additional tasks/activities (“structured”) within the process which may suffer from inefficiencies or bottlenecks and the RPA bot may perform activities which were previously not automated (“unstructured”); para. 42 the module may “recommend the replacement of existing RPAs utilized within the processes and/or updated and/or generate new instructions….to enable the RPA bot to perform additional activities…and/or perform existing activities”; para. 12 and 33 enrich processes with additional contextual data such that activities within the process may be identified for automation)
Wilk does not teach, but Hooks teaches:
passing the unstructured process model and the context data into a heuristic rules engine, causing the heuristic rules engine to apply rules defining recommended process patterns and behaviors to generate a second set of one or more recommendations on how to optimize the unstructured process model or use a process stored in a repository based on comparison of attributes in the unstructured process model and context data with attributes stored in a repository of processes (figure 5 and Col. 6 Line 66 to Col. 7 Line 22 monitoring workflow path information and step information (“unstructured process model”) and multiple instances may be monitored (“context data”), scoring the workflow with one or more heuristic rules and determining one or more workflow patterns based on the scoring (“heuristic rules”) and providing one or more insights about the workflow (“recommendations”); Col. 7 Line 46 to Col. 11 Line 61 for Scoring Algorithms specifically see Col. 7 Line 60 to Col. 8 Line 21 scoring based on heuristic algorithms or rules based on considerations of what types of workflow choices are likely to be productive or efficient choices; see also Col. 4 Line 42 to Col. 5 Line 64 for similar embodiment)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Wilk invention with Hooks with the motivation of improving workflows through insights.
See Col. 1 Line 15-20 “evaluating patterns in software workflows and providing insights”, Col. 1 Lines 40-60 “these user choices create potentials for inefficiency or errors…there haven’t been effective software tools to evaluate workflows of employees”, and Col. 1 Line 64 to Col. 2 Line 5 “generating insights for improving software workflows, where a workflow corresponds to a sequence of interactions of a user with one or more different user interface screens of software applications to perform a task. In one implementation, attributes of the workflow associated with quality, efficiency and other attributes are measured by scoring aspects of the workflow and generating reports. The reports may also provide insights on opportunities to automate workflows”.
As per dependent Claim 3, Claim 10, and Claim 17,
Wilk/Hooks teaches the system of claim 1, the method of claim 8, and the system of claim 15.
Wilk suggests this limitation in para. 37 where linguistic analysis techniques are used to analyze the process event logs (context data) and the linguistic analysis techniques include “a machine learning model with Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), speech-to-text, Hidden markov models (HMM), N-grams, Speaker Diarization (SD), Semantic Textual Similarity (STS), Keyword Extraction”.
Wilk does not teach, but Hooks teaches:
extracting one or more attributes from the context data using a feature extraction process that identifies attributes and their interrelationships that were pertinent in the past executions of the unstructured process model (Col. 7 Line 46 to Col. 11 Line 61 for Scoring Algorithms specifically see Col. 7 Line 60 to Col. 8 Line 21 scoring based on heuristic algorithms (to score attributes of a workflow) or rules based on considerations of what types of workflow choices are likely to be productive or efficient choices and further, “an insight is a recognition of a discoverable pattern in one or more workflows that can be identified using one or more algorithms applied to a set of structured data derived from the time-series data”; Col. 10 Line 34-40 efficiency score compared against a threshold efficiency score, historical efficiency scores for the same user, or efficiency scores of other users; see also Col. 4 Line 42 to Col. 5 Line 64 for similar embodiment specifically Col. 5 Lines 35-56 where generate metrics associated with workflow performance and considering attributes of workflows to identify trends regarding whether the metrics are improving, stable, or becoming worse)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Wilk invention with Hooks with the motivation of improving workflows through insights.
See Col. 1 Line 15-20 “evaluating patterns in software workflows and providing insights”, Col. 1 Lines 40-60 “these user choices create potentials for inefficiency or errors…there haven’t been effective software tools to evaluate workflows of employees”, and Col. 1 Line 64 to Col. 2 Line 5 “generating insights for improving software workflows, where a workflow corresponds to a sequence of interactions of a user with one or more different user interface screens of software applications to perform a task. In one implementation, attributes of the workflow associated with quality, efficiency and other attributes are measured by scoring aspects of the workflow and generating reports. The reports may also provide insights on opportunities to automate workflows”.
As per dependent Claim 7 and Claim 14,
Wilk/Hooks teaches the system of claim 1 and the method of claim 8.
Wilk further teaches:
suggesting the one or more recommendations to a user via a user interface, prior to the optimizing (para. 49-50 the module provides one or more recommendations to the user using the output of the machine learning model so that the user may select which of the recommendations to implement; see also para. 28 end user device can display the recommendation to the end user)
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wilk et al. (US2025/0200485) in view of Hooks et al. (US Patent No. 12,118,490) as applied to Claims 1, 8, and 15 above, further in view of non-patent literature Deokar et al. (article titled “Semantics-based event log aggregation for process mining and analytics” published on June 9, 2015; https://link.springer.com/article/10.1007/s10796-015-9563-4 ).
As per dependent Claim 2, Claim 9, and Claim 16,
Wilk/Hooks teaches the system of claim 1, the method of claim 8, and the system of claim 15.
Wilk/Hooks does not teach, but Deokar teaches:
transforming the context data using data normalization (page 1212 section 3 “A framework for event log pre-processing” and page 1213 normalizing event logs; page 1214-1216 section 4.1 log normalization module where the event logs are processed
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Wilk invention with Deokar with the motivation of improving efficiency of preprocessing context data.
See Page 1210 “We also propose an event log normalization method that improves concept-disambiguation and normalizes event logs, particularly focusing on event names. The framework also incorporates an event log aggregation approach based on phrase-based semantic similarity between normalized event names as the clustering metrics” and page 1214 “Given that an ontology is a formalized structure representing knowledge from a certain domain, it is imperative that the ontology classes representing key domain concepts be clearly specified. In other words, the classes in the ontology should be abstract (generalizable and formal), but not vague (accurate and concrete). Further, if ontology classes are to be constructed through automated learning from event logs, it is essential that the event names be standardized and normalized for machine understandability.”
Claims 4-5, 11-12, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wilk et al. (US2025/0200485) in view of Hooks et al. (US Patent No. 12,118,490) as applied to Claims 1, 8, and 15 above, further in view of non-patent literature Zhu et al. (article titled “Automatic Real-Time Mining Software Process Activities From SVN Logs Using a Naive Bayes Classifier” published on October 10, 2019; https://ieeexplore.ieee.org/abstract/document/8864026 ).
As per dependent Claim 4, Claim 11, and Claim 18,
Wilk/Hooks teaches the system of claim 1, the method of claim 8, and the system of claim 15.
Wilk teaches:
wherein the first machine learning model is a Classifier (para. 37 where linguistic analysis techniques are used to analyze the process event logs (context data) and the linguistic analysis techniques include “a machine learning model with Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), speech-to-text, Hidden markov models (HMM), N-grams, Speaker Diarization (SD), Semantic Textual Similarity (STS), Keyword Extraction, amongst other analysis techniques, such as those implemented in IBM Watson® (IBM Watson and all Watson-based trademarks are trademarks or registered trademarks of International Business Machines Corporation in the United States, and/or other countries), IBM Watson® Speech to Text, IBM Watson® Tone Analyzer, IBM Watson® Natural Language Understanding, IBM Watson® Natural Language Classifier, amongst other linguistic analysis techniques”)
Wilk/Hooks does not teach, but Zhu teaches:
wherein the first machine learning model is a Naive Bayes Classifier configured to calculate a probability of a particular activity in the context data being associated with a particular event in the unstructured process model (page 146414 Conclusion where the method extracts process activities based on correlation between events and activities specifically by extracting each record of events in the log and processing it; page 146405 process event log; page 146406 first paragraph extract activities from the case and classify them, second to last paragraph the trained classifier is used to discover activities based on a naïve Bayes approach, and last paragraph to page 146407 preprocessing the SVN log; page 146408-146409 section 5 creating a naïve bayes classification model in order to complete event to activity category mapping – the category is the target variable, X is the set of messages to be classified, C is the collection of categories, choosing the class with the largest posterior probability as the final classification of the message)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Wilk invention with Zhu with the motivation of improving the efficiency and accuracy of activity mining.
See Page 146404 “The purpose of this research is to improve existing methods of activity mining, to improve the efficiency and accuracy of activity mining, and to further discover software process models from development events” and page 146412 “The results show that training a data set based on our improved naive Bayesian classification algorithm for activity mapping of SVN log events is more accurate than the fuzzy clustering method.”
As per dependent Claim 5, Claim 12, and Claim 19,
Wilk/Hooks teaches the system of claim 1, the method of claim 8, and the system of claim 15.
Wilk/Hooks does not teach, but Zhu teaches:
wherein the first machine learning model is a K- means clustering model configured to group activities in the context data based on their similarity to one another (page 146406 second column k-means approach is used to cluster events as activities based on parts of datasets – extract relevant events and then relate them to the same activity; page 146407-146408 section B activity clustering based on K-means)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Wilk invention with Zhu with the motivation of improving the efficiency and accuracy of activity mining.
See Page 146404 “The purpose of this research is to improve existing methods of activity mining, to improve the efficiency and accuracy of activity mining, and to further discover software process models from development events” and page 146412 “The results show that training a data set based on our improved naive Bayesian classification algorithm for activity mapping of SVN log events is more accurate than the fuzzy clustering method.”
See also page 146406 “After training, we extract the most relevant events by clustering, using semantic mapping, and then relate them to the same activity. By extracting activities in this way, it is possible to represent the internal cohesion of events in the software process log and so discover the relationship between each event and activity.”
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wilk et al. (US2025/0200485) in view of Hooks et al. (US Patent No. 12,118,490) in view of non-patent literature Zhu et al. as applied to Claims 5, 12, and 19 above, further in view of Durvasula et al. (US2023/0177441).
As per dependent Claim 6, Claim 13, and Claim 20,
Wilk/Hooks/Zhu teaches the system of claim 5, the method of claim 12, and the system of claim 19.
Wilk/Hooks/Zhu does not teach, but Durvasula teaches:
using Principal Component Analysis (PCA) to reduce dimensionality of the context data (para. 44 where a PCA technique is applied to input data in order to reduce dimensionality)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Wilk invention with Durvasula with the motivation of reducing the amount of computational resources utilized. See Para. 44 “Reducing dimensionality may result in a substantial reduction in computational resources (e.g., memory and CPU cycles) required to train and/or analyze the input data.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Iver et al. (US 2022/0075605)
Sullivan et al. (US Patent No. 10,554,817)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lisa Ma whose telephone number is (571)272-2495. The examiner can normally be reached Monday to Thursday 7 AM - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shannon Campbell can be reached at (571)272-5587. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/L.M./Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628