DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 23 September 2026 has been entered.
Status of Claims
This is a non-final office action in response to the request for continued examination filed. 23 September 2026. Claims 1 and 11 have been amended, claims 5-6, 8, 15-16, and 18 have been canceled. Claims 1-4, 7, 9-14, 17, and 19-20 remain pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1 and 11 has been entered.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the claims are not directed to a mental process or certain methods of organizing human activity abstract idea because “a person cannot mentally implement a software listener bound to a graphical user interface event, generate and pass an event object to an associated software event handler, convert raw input into structured numerical encodings using an encoder component of a trained machine-learning model, or dynamically update a graphical user interface in real time in response to the resulting machine-generated status and execution operations,” and further because the recited claims “recite how the claimed computing system detects an interface event, processes resulting user input, generates a machine-determined status, uses that status to control generation of additional execution operations, and modifies the graphical user interface in response.” Examiner respectfully disagrees.
The claimed invention is centered on improving workflow for a regulated process (see Spec. @ [0021, 0024]), not the underlying technologies used to automate the workflow management process that was previously performed manually. The claims recites steps for matching governance data with entity data; assign the matched governance data entity data to one or more action items; generate a set of execution operations; receive user input sanitize training data (filtering data to reduce/remove computational noise); training a machine learning model; determine, as a function of user input, the assigned status corresponding to the set of execution operations; generate a second set of execution operations as a function of the assigned status; display the results; tracking progress of the second set of execution operations; and generating contextual data for subsequent execution operations. Each of these workflow creation, tracking, and updating steps could be performed mentally or through use of a pen and paper through user observation, evaluation, and judgement and fall within the mental processes grouping of abstract ideas. See MPEP § 2106.04(a)(2)(III)The step for generating a graphical user interface merely describes how data is processed for display in an interface. Creating a workflow, monitoring task completion, and creating follow-up or additional workflow tasks is a form of managing human activity that falls within the certain methods of human activities grouping of abstract ideas. See MPEP § 2106.04(a)(2)(II); see also Spec. at para. [0043] (process ensures that any outstanding issues are addressed through an additional round of tasks, thereby improving the overall quality and consistency of the operations. Furthermore, the second set of execution operations 172 may be automatically assigned to relevant personnel, ensuring prompt action and efficient workflow management). Merely automating a process is not enough. See Customedia, 951 F.3d at 1365 (generic speed or efficiency increases that result from applying a computer to a task do not improve computer functioning); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (“But relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”); Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Can. (U.S.), 687 F.3d 1266, 1279 (Fed. Cir. 2012) (“Using a computer to accelerate an ineligible mental process does not make that process patent-eligible.”). The claimed invention merely uses a general purpose computer and computing technology to automate steps of an otherwise manual/abstract process and thus the claimed invention is not directed to an improvement in the functioning of the computer itself or computer technology (MPEP 2106.04(a)(I): “The Federal Circuit has also indicated that mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality.”). Therefore, the claims recite an abstract idea.
Applicant further asserts that considered as a whole, the claims integrate any alleged judicial exception into a practical application because “claim 1 recites a computer-implemented sequence in which a graphical user interface includes visual elements respectively associated with execution operations and input event handlers. Each input event handler includes a listener configured to listen for an event associated with a respective visual element and a binding process associating the event with the input event handler, and, upon occurrence of the event, an event object containing details of the event is passed to the input event handler.” Examiner respectfully disagrees.
While these additional element provide technical tools that implement the abstract concept and automate the workflow management process, the claim language merely explains how the additional elements function to implement the abstract idea, and do not recite an improvement to event handler, listener, or interface technology. See MPEP § 2106.05(f). In computer programming, event-driven programming is a programming paradigm in which the flow of the program is determined by external events. User interface (UI) events from keyboards and mice, touchpads and touchscreens, and external sensor inputs are common cases. Here, the claim does not improve computer functionality, improve another field of technology, utilize a particular machine, or effect a particular physical transformation. Therefore, nothing in the claim imposes a meaningful limit on the judicial exception. The additional elements recited as the “input event handler comprises: a listener … and a binding process” are described in the Specification as generic computer components (see Spec. @ para. [0036-0037]). The claimed limitations for generating a graphical user interface upon receiving an input or trigger event do not purport to improve the functioning of the computer itself, do not improve the technology of the technical field, and do not require a particular machine. Because the limitations merely describe how user input or another programmed trigger is processed to implement the abstract idea, these additional elements do not integrate the abstract idea into a practical application.
Applicant asserts that the additional elements are not accounted for as a combination, and that the claimed system does not simply collect data, analyze the data, and display the result, but implements an event-driven feedback architecture in which the output of one stage controls the subsequent operation and interface state of the computing system such that the claimed graphical user interface, event handlers, machine-learning components, assigned status, and subsequently generated execution operations cooperate to implement the disclosed feedback loop. Examiner respectfully disagrees.
Examiner has made the requisite prima facie case showings for satisfying: Step 2a Prong One –the directed to findings, Prong Two –findings supporting additional elements that do not integrate the abstract idea into a practical application; and Step 2B –findings of not significantly more. The recited limitations merely use the claimed computer elements as a tools to perform the abstract idea. Considering the elements of the claim both individually and as “an ordered combination” the functions performed by the computer system at each step of the process are generic. Each step of the claimed method does no more than require a generic computer to perform a generic computer function related to processing and analyzing input, manipulating data to assign a status, and adjusting the graphical output of the data analysis and manipulation using specific computing elements for monitoring progress or assigning follow-up tasks. Thus, the claimed elements do not integrate the judicial exception into a practical application as set forth in MPEP §§ 2106.04(d) and 2106.05(a)–(c) and (e)–(h).
Applicant further asserts that, similar to McRo, claim 1 does not simply recite training a model using unspecified information, but requires identifying tonality training data having a signal-to-noise ratio below a threshold value and removing that training data to create sanitized tonality training data used to train the tonality machine-learning model and the sanitized data is employed within the larger claimed event-driven feedback architecture, such that any alleged abstract idea is applied through a specific computer-implemented feedback architecture that imposes meaningful limits on its use and the additional limitations and their ordered combination integrate that exception into a practical application under MPEP § 2106.04(d). Examiner respectfully disagrees.
The claims in McRO were directed to “a specific asserted improvement in computer animation, i.e., the automatic use of rules of a particular type.” McRO, 837 F.3d at 1314. Claim 1 here is different from those determined to be patent eligible in McRO. The claims in McRO were directed to the creation of something physical—namely, the display of “lip synchronization and facial expressions” of animated characters on screens. McRO, 837 F.3d at 1313. Unlike McRO, claim 1 here is not directed to an improvement to computer technology. Rather, claim 1 merely uses generic computer components to collect and analyze information in a particular environment, namely generating workflows and monitoring the status of tasks. Automating regulated workflow creation and status monitoring with generic machine learning steps is not a meaningful limitation. See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1214 (Fed. Cir. 2025) (“[T]he claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. We have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity.”); Spec. at para. [0007] (a computing device configured to generate a list of execution operations as a function of an assigned status); Spec at para. [0041] (a status machine-learning model 164 may be used to assign and track assigned statuses 156 by incorporating various techniques to analyze and manage data related to compliance regulations, liability metrics, tasks, projects and/or resources); see also Recentive, 134 F.4th at 1214 (rejecting arguments that the patents are eligible because they introduce machine learning techniques to the fields of event planning and creating network maps as conflicting with § 101 jurisprudence). Therefore, the machine learning and data analysis and manipulation steps do not impose meaningful limits on the recited abstract idea.
Applicant asserts that the Office has not established that this claimed combination constitutes well-understood, routine, and conventional activity and identifies no evidence establishing that it was conventional to arrange graphical-interface event handling, machine-learning-based tone classification, status-controlled generation of subsequent execution operations, and real- time graphical-interface modification in the manner presently claimed – thus not satisfying the 2B inquiry. Examiner respectfully disagrees.
Claim 1 recites generic elements (processor, memory, matching model, graphical user interface comprising visual elements, input event handlers, event object, tonality machine learning model, classifier, and display device) performing functions at a high level of generality to link the abstract idea to a technological environment. These features are claimed using result-based, functional language without details of how the features improve computers or technology. The Specification’s generic description of these features confirms the broad scope of the claim language. A generic machine learning model is applied to manipulate, filter, and classify data to determine whether tasks have been completed or whether additional tasks should be assigned with no technological implementation details of how it improves computers or technology. The management of user task status information does not represent a “technical field,” but is, instead, a basic business practice of workflow management. While the claimed subject matter represents an improved business process that is more efficient than manual processing of workflow management steps, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing a technological improvement. See MPEP §§ 2106.04(d)(1), 2106.05(a). Claim 1 merely recites various computer-based elements along with no more than mere instructions to implement the identified abstract idea using the computer-based elements or, alternatively, merely uses a computer as a tool to perform the abstract idea. The ordered combination recites no more than the individual elements do. Alice, 573 U.S. at 225 (using computers to create and maintain accounts, adjust account balances, and issued instructions is well-understood, routine, and conventional activity); Elec. Power Grp., 830 F.3d at 1355 (using off-the-shelf computers, networks, and displays to gather, analyze, send, and present data is not inventive); Recentive, 134 F.4th at 1214 (iterative training and dynamic adjustment of parameters based on real-time changes are incident to the nature of machine learning). Here, using a generic artificial intelligence (machine learning) to collect and analyze user data and output generic digital workflow management information is not an inventive concept that is significantly more.
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 1-4, 7, 9-14, 17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 recites a system and independent claim 11 recites a process for generating user-sensitive operations using artificial intelligence. The claims recite an abstract idea of collecting data, analyzing it, and outputting certain results of the collection and analysis. Independent claims 1 and 11 recite substantially similar limitations.
Taking independent claim 1 as representative, amended independent claim 1 recites the following limitations:
generate a task module using a matching model, wherein the matching model is configured to:
match governance data with entity data;
and assign the matched governance and entity data to one or more action items;
generate a first set of execution operations as a function of the task module;
generate a graphical user interface, wherein the graphical user interface comprises a plurality of visual elements, comprising:
an execution operation of the first set of execution operations and an input event handler of a plurality of input event handlers,
wherein the input event handler comprises: a listener configured to listen for an event associated with a respective visual element; and a binding process associating the event with the input event handler, and wherein, upon occurrence of the event, an event object containing details of the event is passed to the input event handler;
receive user input corresponding to at least one of the first set of execution operations as a function of one of the plurality of input event handlers;
sanitize tonality training data comprising exemplary user inputs correlated to exemplary user input tones, wherein sanitizing the tonality training data comprises:
determining that at least one training data entry of the tonality training data has a signal to noise ratio below a threshold value; and
removing the at least one training data entry from the tonality training data to create sanitized tonality training data;
train a tonality machine-learning model using the sanitized tonality training data, which increases a training efficiency and predictive accuracy of the tonality machine-learning model, and output tone data which is part of an assigned status, wherein the tonality machine-learning model comprises: an encoder component for generating a plurality of textual encodings as a function of the user input, wherein the encoder component converts raw, unstructured user input into a plurality of structured numerical representations configured to capture at least contextual and semantic features of the user input;
and a classifier configured to classify the plurality of textual encodings into a tone classification;
determine, as a function of the user input, the assigned status corresponding to the first set of execution operations using a machine-learning model including at least a tonality machine-learning model,
and generate a second set of execution operations as a function of the assigned status including the tone data, wherein the tone classification controls generation of the second set of execution operations; and the assigned status triggers generation of the second set of execution operations; and
display, at a display device, the first set of execution operations and the second set of execution operations, wherein the second set of execution operations is configured to update the graphical user interface by at least one of:
dynamically adjusting the graphical user interface based on a status of the execution operations, wherein dynamically adjusting the graphical user interface comprises dynamically updating the graphical user interface in real time to reflect a change in the assigned status and initiation of the second set of execution operations;
tracking progress of the second set of execution operations; and
generating contextual data for subsequent execution operations.
Under Step 1 independent claims 1 and 11 recite at least one step or act, including determining an assigned status corresponding to the first set of execution operations. Thus, the claims fall within one of the statutory categories of invention. See MPEP 2106.03.
Under Step 2A Prong One the limitations recited in claim 1 for generating a task module using a matching model, matching governance data with entity data, assigning the matched data to action items, generating a first set of execution operations, receiving user input, sanitizing tonality training data, determining that at least one training data entry of the tonality training data has a signal to noise ratio below a threshold value, removing the at least one training data entry, training a tonality machine-learning model, determining the assigned status, generating a second set of execution operations, tracking progress of the second set of execution operations, and generating contextual data for subsequent execution operations, as drafted, illustrates a process that, under its broadest reasonable interpretation covers performance of the limitation in the mind (collecting, analyzing, and displaying certain results of the collection and analysis; observations, evaluations, and judgement related to generating execution operations).
None of the additional elements preclude the steps from practically being performed in the human mind, or by a human using a pen and paper. A facilities auditor could use a checklist and mentally determine whether an established workflow for a particular operation or facility (such as the healthcare environment – patient rooms detailed in paragraph [0017] of the Specification, and paragraph [0019] patient feedback and or survey data as input) is in compliance with regulatory and governance protocol, assign action items based on the observations, and determine the status of the assigned tasks. Applicant’s specification at [para. 0041] states: “… in an embodiment, a status machine-learning model 164 may be used to assign and track assigned statuses 156 by incorporating various techniques to analyze and manage data related to compliance regulations, liability metrics, tasks, projects and/or resources. Status machine-learning model 164 may be trained using status training data 168 such as historical data on execution operation progress and status changes. In an embodiment, status features may be defined, such as categorical variables (e.g., assigned, in-progress, completed), time-related features (e.g., time spent in each status, deadlines), and/or user-specific features (e.g., user experience, past performance).” The steps for matching action items with governance data, processing user input status data and classifying the data into the categorical variables exemplified in paragraph [0041] of the Specification for updating a task/project progress visualization and presentation of task/project operations for execution could be performed mentally or through use of a pen and paper as observation, judgments, or evaluations of a task/project based on information from a user. Therefore, the claim limitations fall within the mental processes grouping of abstract ideas. See MPEP § 2106.05(a)(2)(III).
The limitations recite an abstract idea of processing project/task data for monitoring progress and assigning additional instructions to a user for completion of a task or project. The recited claim limitations fall within the abstract concept grouping of certain methods of organizing human activities because the limitations track user completion or progress towards completion of a task based on analysis of user input data, which is a form of managing personal behavior. See MPEP § 2106.05(a)(2)(II). Therefore, the limitations fall into the mental processes grouping and certain methods of organizing human activity abstract concept groupings, and accordingly the claims recite an abstract idea.
Under Step 2A Prong Two the judicial exception of claim 1 is not integrated into a practical application. In particular, the claims recite a processor, graphical user interface, machine learning model comprising an encoder component, and storage device for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0010] states: “system 100 includes at least a processor 108 and a memory 112 communicatively connected to the at least a processor 108 and containing instructions 116 configuring the at least a processor 108 to generate user-sensitive operations using AI.” Adding generic computer components to perform generic functions, such as data gathering, performing calculations/comparisons, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h). Claim 1 merely recites various computer-based elements along with no more than mere instructions to implement the identified abstract idea using the computer-based elements or, alternatively, merely uses a computer as a tool to perform the abstract idea of managing workflow.
The limitation for “generating a graphical user interface, wherein the graphical user interface comprises a plurality of visual elements … input event handlers; and event object containing details of the event” is broadly and generically claimed and construed as using graphical user interface technology to display processed data, without an improvement thereto. The claim limitations fail to detail any functional improvement or modification to the functioning of the graphical user interface that goes beyond generic data input and output functions, therefore the steps for displaying generated execution operations or other information upon a trigger event detected by an event handler (such as “a mouse click, keyboard input, and/or a change in a form field”; see Spec at para. [0036-0037]) fail to integrate the abstract idea into a practical application.
The claimed matching model, task module, input event handlers, and a machine learning model are additional elements but do not transform the recited abstract idea into eligible subject matter. The matching model and task model are construed as computing modules programmed to perform a data correlation function based on pre-determined business rules, in a manner that could be performed by a human mentally. The input event handler, per paragraph [0037] of the Specification is defined as “functions or methods designed to respond to specific events in a program, particularly in user interface contexts.” An “event” is “an occurrence that is detected by the program. For example, this may include a mouse click, keyboard input, and/or a change in a form field. In an embodiment, event handlers 184 may include a listener and or binding process. A listener is a function that listens for specific events on an element. For instance, a button or an input field.” As defined, the input event handlers are broadly and generically claimed, and as a result are reasonably construed as generic graphical user interface components for receiving user input, without a technical improvement thereto or significantly more than the recited abstract idea. The claim lacks sufficient technical details regarding how the claimed input event handlers cause the graphical user interface to perform any functions beyond generic interface functions of receiving, transmitting, and displaying data. The claims herein are not focused on an improvement to interface technology, but on providing information to users in a way that helps them process information more quickly. The limitations do not include limitations for improving computers or technology and the tool for presentation is a generic computer. As a result, the claimed interface, input event handlers, and event object do not transform the recited abstract idea into patent eligible subject matter.
While the claim includes steps for sanitizing tonality training data, and determining an assigned status corresponding to the first set of execution operations using a machine learning model trained, the training of specific data is construed as pre-processing data does not make a claim patent eligible. Further the Specification at [para. 0071] states that sanitization or noise ratio determination could be a mathematical operation. The specification at paragraph [0025-0027] supports generic machine learning. The machine learning models, as claimed, include the application of mathematical models (for example linear regression) that are abstract ideas applied using generic computing technology, and thus cannot provide a practical application of the recited abstract idea. Training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a).
The recited machine-learning model is broadly and generically claimed, such that it is construed as a tool to process the data and generate an output, without significantly more. While the recitation of machine learning models may provide a practical application for a recited judicial exception, there must be an improvement in the application of machine learning technology that goes beyond a general link. The recited claim limitations herein for updating the data used to classify the tone of the user input and determine an assigned status corresponding to a set of execution operations (per Spec. at [para. 0040]: completed, in progress, overdue) is insufficient to transform the recited abstract idea into a practical application. While the claimed machine learning model includes an encoder, which is defined in the Specification as “a component within a machine-learning model that converts raw input data, such as text, into a set of structured numerical representations, known as encodings” and a “classifier may be configured to use the textual encodings to categorize the user response data into various tone classifications (e.g. positive, negative, neutral)” (See Specification [para. 0042]), the trained machine learning model is used as a tool to implement the underlying abstract idea. The model improves the data used for analysis by reducing the dataset, the machine learning model itself is not improved. Further, the output of the data analysis step is not used in a meaningful way that improves or alters the functioning of the graphical user interface or the computer processor.
The claimed “set of execution operations” is broadly and generically claimed to include the display of workflow data or a list of tasks to be completed by the assigned user (see Spec. at para.[0053]. The display of data is insignificant extra solution activity that cannot transform an abstract idea into patent eligible subject matter. The same rules for character analysis may be mentally applied using the human mind to compare phrases or terms and determine the “underlying tone or sentiment of a user.” The limitation for “generating a second set of execution operations as a function of the assigned status including the tone data,” is broadly and generically claimed to include generating a set of steps or “action items” for task completion or business decision making to be performed by a human per the Specification at [para. 0035], which states “’execution operations’ refer to the processes or actions that are carried out to implement a specific function, task, or operation within a system. …Task module 124 may encapsulate the logic needed to perform certain actions, therefore first set of execution operations 140 are automatically generated or defined within task module 124. When system 100 processes a request a specific set of action items may be generated without being manually chosen.” As described in the specification the output of the data analysis could be the presentation of a list of action required to perform a task. This is not a technical solution to a technical problem, nor a practical application of the recited abstract idea – but a business solution for managing workflow. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea because they do not impose any meaningful limits on practicing the abstract idea beyond generally linking the abstract idea to generic data processing and visualization technology.
Under 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 the integration of the abstract idea into a practical application, the additional elements of a processor and storage device amount to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept. See MPEP 2106.05. The focus of Applicant’s invention is not to improve the performance of computers or any underlying technology; instead, the focus is to use generic computer components as a tool to gather and analyze business data to administer, create, or modify task/project progress data based on a user input. The additional elements, when considered individually or in combination, do not amount to significantly more than the recited abstract idea because the additional elements recited in the claims merely improve the analysis and output of known and input data for human visualization and decision-making using machine learning and computer components as data processing tools without technological improvement.
Dependent claims 2-4, 7, 9-14, 17, and 19-20 include the abstract ideas of the independent claims. The limitations of the dependent claims merely narrow the mental process abstract idea by describing how the data is processed, analyzed, and displayed. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly, independent claim 11 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis applied to claim 1 above. Therefore, claims 1-4, 7, 9-14, 17, and 19-20 are ineligible under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gupta (US 12,423,121) - monitoring online activity of users within a collaboration environment, ranking work unit records managed within the collaboration environment based on the user activity, and customizing a user interface based on the rankings.
Robbins et al. (US 10,930,400) - generating and providing a checklist, and for providing updates to a checklist based on event data. In some aspects, a checklist is provided to a terminal device by receiving a notification of a trigger event, which is used, at least in part, to obtain a checklist template and to generate a checklist from the checklist template, which is then provided to the terminal device. In some aspects, a server device provides a checklist to a terminal device, the checklist comprising a sequence of prompts, each prompt indicating one or more actions, and the server device provides an update to the checklist based on received event data.
Oliveira et al. (US 2022/0037003) - real-time, interactive development and testing of workflows for patient care access and selection may include receiving, by a guided workflow builder user interface (UI) engine, user input including a selection of at least one user interface element, the selected user interface element representing a step in a workflow and associated with at least one state. The guided workflow builder UI engine generates a file including pseudocode providing at least one configuration instruction for configuring the workflow to include the step and transmits the generated file to a parser. The parser parses the generated file and converts the at least one configuration instruction into a state chart including at least one state machine associated with at least one instruction for providing functionality during execution of the workflow. The previewer user interface executes the state chart generated by the parser and displaying an output of executing the state chart.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao WU can be reached at 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
