Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 19/305,434, filed on August 20, 2025. In response to Examiner’s Office Action of January 23, 2026, Applicant, on July 14, 2026, amended claims 1,9 and 17. Claims 1-18 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding the 35. U.S.C. § 101 rejection, Applicant’s arguments have been
considered but are insufficient to overcome the rejection. Please refer to the 35 U.S.C.§ 101 rejection for further explanation and rationale.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants’ amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended claims. Please refer to the § 103 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed July 14, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed July 14, 2026.
On Pg. 7-8 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the claims cannot, as a practical matter, be performed in the human mind. A
person cannot embed machine-readable state markers into recursive execution threads, maintain a persistent memory graph of thread lineage across otherwise stateless execution cycles, or gate the resumption of a recursive execution thread on a programmatic consistency check of that graph. See MPEP § 2106.04(a)(2)(III) (a claim that cannot practically be performed in the human mind does not recite a mental process). In response, the Examiner respectfully disagrees. The present claims are receiving and analyzing user input data. The claims primarily recite the additional element of using computer components to perform each step. The “computing device”, “application program”, “user interface module”, “server”, “data store”, “execution persistence module”, “memory structure”, “execution persistence layer”, “processor”, and “computer readable storage medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).. See 101 analysis below for further detail.
On Pgs. 8-9 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the claims similar to Enfish as a whole is directed to a specific improvement in computer functionality rather than to an abstract idea. In response, Examiner respectfully disagrees. The aforementioned procedures are not improvements to a problem in the software arts, a technology or technological field. The collection
of web content to organize and manage competency level learning maps is a judicial exception (i.e. abstract idea). The claimed invention is executed by computer elements performing computer functions. Enfish recited claims that asserted improvements to the
configuration of computer memory in accordance with a self-referential table with sufficient support in the specification that the claims were directed to a specific implementation of a solution to a problem in the software arts. Which shows the claimed invention made improvements in computer related technology. In contrast, the present claims recite computer elements to perform the functions. Examiner asserts, the additional element of the symbolic markers is a tool to apply the abstract idea.
On Pg. 9-11 of the Remarks, regarding 35 U.S.C. § 103 rejections, Applicant states prior art does not disclose amended claim language. In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Singh is now applied for Claims 1, 9 and 17. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-18 are directed to adaptive workflow management.
Claim 1 recites a system for adaptive workflow management, Claim 9 recites a method for optimizing workflow management and Claim 17 recites an article of manufacture for optimizing workflow management, which include enabling one or more users to input a plurality of user information, a plurality of preferences, and a plurality of workflow information; analyzing the user input and generating a plurality of personalized content and workflow automation tasks; an adaptive algorithm to interpret the plurality of user inputs and to adjust variables in real-time to produce customized outputs based on the plurality of user inputs; storing the plurality of user inputs; embed symbolic state markers within recursive execution threads, wherein the symbolic state markers are preserved in a persistent memory graph to enable continuity of a symbolic reasoning across multiple workflow sessions wherein the symbolic state markers comprise symbolic identifiers, parametric weights, and thread lineage markers that encode a relationship of a current execution path to an upstream symbolic context and a downstream symbolic context, and validate a symbolic integrity of the persistent memory graph prior to resuming a recursive execution thread of the recursive execution threads such that a continuation of the recursive execution thread occurs only upon the persistent memory graph passing a consistency check, thereby enabling a deterministic replay and a structured recovery of the recursive execution threads without a rehydration of raw execution logs, wherein the adaptive algorithm enables the continuous optimization of a user's workflow (Claim 1). Receiving a plurality of user inputs including user information, workflow data, and user preferences; storing the plurality of user inputs; processing the plurality of user inputs via an adaptive algorithm that continuously analyzes and adjusts one or more contextual variables in real-time; generating personalized workflow automation tasks and content based on dynamically adjusted contextual variables; providing customized workflow outputs to a user, wherein the adaptive algorithm refines the customization of workflow outputs over time by continuously learning from user interactions; embedding symbolic state markers into recursive workflow threads during execution, the symbolic state markers comprising symbolic identifiers, parametric weights, and thread lineage markers that encode a relationship of a current execution path to an upstream symbolic context and a downstream symbolic context; storing the symbolic state markers; and validating a symbolic integrity prior to resuming a subsequent recursive execution; and resuming subsequent recursive executions based on the preserved symbolic state markers only upon passing a consistency check, wherein the process is governed by an execution persistence layer that enables a deterministic replay and a structured recovery of the recursive workflow threads without a rehydration of raw execution logs; (Claim 9). Receiving a plurality of user inputs including user information, workflow data, and user preferences, and personality-based contextual data; storing the plurality of user inputs; processing the plurality of user inputs via an adaptive algorithm that continuously analyzes and adjusts one or more contextual variables in real-time; generating personalized workflow automation tasks and content based on dynamically adjusted contextual variables; providing customized workflow outputs to a user, wherein the adaptive algorithm refines the customization of workflow outputs over time by continuously learning from a plurality of previous user interactions; embedding symbolic state markers into recursive workflow threads during execution, the symbolic state markers comprising symbolic identifiers, parametric weights, and thread lineage markers; storing the symbolic state markers; validating a symbolic integrity prior to resuming a subsequent recursive execution; and resuming the subsequent recursive execution based on the preserved symbolic state markers only passing a consistency check, wherein the subsequent recursive execution is governed by an execution persistence layer that enables a deterministic replay and a structured recovery without a rehydration of raw execution logs (Claim 17).
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “computing device”, “application program”, “user interface module”, “server”, “data store”, “execution persistence module”, “memory structure”, “execution persistence layer”, “processor”, and “computer readable storage medium”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “computing device”, “application program”, “user interface module”, “server”, “data store”, “execution persistence module”, “memory structure”, “execution persistence layer”, “processor”, and “computer readable storage medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 9
and claim 17 recite using one or more embedding / recursive threading analysis techniques. The general use of a complex analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the embedding and recursive processing is solely used a tool to perform the instructions of the abstract idea.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in workflow management.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computing device”, “application program”, “user interface module”, “server”, “data store”, “execution persistence module”, “memory structure”, “execution persistence layer”, “processor”, and “computer readable storage medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Regarding Step 2B and the embedding and recursive processing- is solely used a tool to perform the instructions of the abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-8, 10-16 and 18 recite wherein the plurality of user inputs is comprised of one or more user personality metrics, one or more philosophical beliefs, and one or more cognitive biases; wherein a cloud-based server analyzes the plurality of user inputs; an automation engine in operable communication with the one or more data stores to automate, in real-time, the personalization of a plurality of tailored outputs; a workflow generation engine to receive the plurality of tailored outputs and autonomously generate a workflow in real-time; the workflow generation engine receives a plurality of contextual variables to refine the workflow; wherein the workflow generation engine receives a plurality of historical user data to refine the workflow; wherein the workflow generation engine receives one or more user interactions to refine the workflow; wherein the workflow is used to organize a plurality of tasks; detecting and mitigating user cognitive biases through automated recommendations; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 9 and 17. Regarding Claims,4-8, 11-15, and the additional elements of “automation engine” and “workflow engine” and “data store”, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 9 and 11-17 are rejected under 35 U.S.C. 103 as being unpatentable over Malecha, US Publication No. 20230060235A1, [hereinafter Malecha], in view of Singh, US Patent No. 12555008B1, [hereinafter Singh].
Regarding Claim 1,
Malecha teaches
An adaptive workflow management system, comprising: at least one computing device in operable communication an application program; a user interface module, accessible via the at least one computing device, the user interface module to enables one or more users to input a plurality of user information, a plurality of preferences, and a plurality of workflow information; (Malecha Par. 5; Par. 29-32; Par. 53-“In some implementations, the management engine 102 may automate step(s) within a workflow or protocol, such an intake procedure, by automatically triggering a next stage in the protocol or workflow, such as scheduling a patient meeting or appointment following completion of an intake procedure or part thereof, scheduling a supplement order following a lab result, etc. For instance, the management engine 102 may receive workflow data (e.g., test results) associated with a workflow element, such as a lab test element, and based on the workflow data, the management engine 102 may automatically trigger the next stage in the protocol or workflow, such as schedule an appointment, provide a notification, analyze result data in conjunction with other data to determine insights and request input, etc. As a further example, the management engine 102 may access a practitioner's calendar(s) (e.g., a Google™ Outlook™, or timeslots programmed into another accessible calendar), a patient's calendar(s), or a combination thereof, to identify an available time to schedule a meeting or appointment for the patient following a given stage. The management engine 102 may therefore provide the practitioner with the ability to automate certain tasks associated with the practice, such as using a workflow or protocol builder, as described further herein.’ Par. 74-83)
a server for analyzing the user input and generating a plurality of personalized content and workflow automation tasks; (Malecha Par. 29-32; Par. 53-“ For instance, the management engine 102 may receive workflow data (e.g., test results) associated with a workflow element, such as a lab test element, and based on the workflow data, the management engine 102 may automatically trigger the next stage in the protocol or workflow, such as schedule an appointment, provide a notification, analyze result data in conjunction with other data to determine insights and request input, etc. ; Par. 51)
an adaptive algorithm in operable communication with the application program to interpret the plurality of user inputs and to adjust variables in real-time to produce customized outputs based on the plurality of user inputs; (Malecha Par. 72-“ The machine learning engine 112 may include software or hardware that is adapted to train models, and/or analyze data using the models, for example, using various types of machine learning algorithms, as described below. In some implementations, the machine learning engine 112 may be incorporated with the management engine 102 or may be operable on a separate server and specially adapted to perform machine learning analysis of provided data. ; Par.84; Par. 91; Par.120-121)
one or more data stores in operable communication with the application program to store the plurality of user inputs(Malecha Par. 46-“ In some implementations, the management engine 102 may include a web server that processes content requests (e.g., to or from a client device 104). The web server may include an HTTP server, a REST (representational state transfer) service, or other suitable server type. The web server may receive content requests (e.g., product search requests, HTTP requests) from client devices 104, cooperate with the management engine 102 to determine the content, retrieve and incorporate data from the data storage device 110, format the content, and provide the content to the client devices 104. In some instances, the web server may format the content using a web language and provide the content to an application or engine for processing and/or rendering to the user for display.; Par. 73);
Malecha teaches workflow analysis and the feature is expounded upon by Singh:
an execution persistence module configured to embed symbolic state markers within recursive execution threads, wherein the symbolic state markers are preserved in a persistent memory graph to enable continuity of a symbolic reasoning across multiple workflow sessions, wherein the symbolic state markers comprise symbolic identifiers, parametric weights, and thread lineage markers that encode a relationship of a current execution path to an upstream symbolic context and a downstream symbolic context, and wherein the execution persistence module is further configured to validate a symbolic integrity of the persistent memory graph prior to resuming a recursive execution thread of the recursive execution threads such that a continuation of the recursive execution thread occurs only upon the persistent memory graph passing a consistency check, thereby enabling a deterministic replay and a structured recovery of the recursive execution threads without a rehydration of raw execution logs; (Singh Col 6; Col 15-“ With continued reference to FIG. 1 , encoder of transformer may include a residual connection. Residual connection may include adding the output from multi-headed attention to the positional input embedding. In some embodiments, the output from residual connection may go through a layer normalization. In some embodiments, the normalized residual output may be projected through a pointwise feed-forward network for further processing. The pointwise feed-forward network may include a couple of linear layers with a ReLU activation in between. The output may then be added to the input of the pointwise feed-forward network and further normalized.; Col 20-21 With continued reference to FIG. 1 , in one or more embodiments, the SmartDoc and simulation reentry layer may be configured to link to prior CI trails (as described in reference to at least FIG. 4 as well) for the purpose of simulation resumption, reasoning continuity, or governed rehydration of a paused interaction thread. In one or more embodiments, CI trail may include sequence of thematically or temporally linked CI units 124 that collectively represent a persistent record of a user-agent interaction over time. In one or more embodiments, the simulation reentry layer may utilize the presence of a simulation continuity flag within a CI unit 124 to locate a corresponding CI trail checkpoint, enabling the system to identify a precise position within the prior trail from which simulation may be resumed. Upon reentry, the system may access continuity datum, reconstruct the agent's prior cognitive state, and restore relevant simulation variables, thereby aligning the ongoing interaction with the user's historical trajectory. In one or more embodiments, simulation reentry layer may integrate directly with the simulation loop described therein. If the simulation has not ended, and the system is configured to continue with updated context or new user input, the reentry layer may act as a gatekeeper that ensures the resumed interaction begins from a verified and auditable point within a prior CI trail. This linkage may involve querying CI log 148 for the most recent simulation continuity flag tied to a given user and agent identifier pair, validating that the associated CI unit 124 satisfies ethical, memory, and compliance conditions, and initializing the simulation environment accordingly. In one or more embodiments, reentry into a CI trail checkpoint may also restore simulation state variables such as emotional tone, narrative objective, prior user concerns, or unresolved logic branches that were suspended at the time of the pause.; Col 34-35; Col 42-43”)
Malecha is directed to workflow management. Singh improves upon data processing supporting the workflow. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Malecha, as taught by Sing, by utilizing encoder modelling with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Malecha with the motivation of adapting dynamically to individual user interactions or retaining long-term contextual understanding. (Singh Background).
Regarding Claim 9,
Malecha teaches
A method for dynamically optimizing workflow management, the method comprising the steps of: receiving, via a user interface module, a plurality of user inputs including user information, workflow data, and user preferences; (Malecha Par. 5; Par. 29-32; Par. 53-“In some implementations, the management engine 102 may automate step(s) within a workflow or protocol, such an intake procedure, by automatically triggering a next stage in the protocol or workflow, such as scheduling a patient meeting or appointment following completion of an intake procedure or part thereof, scheduling a supplement order following a lab result, etc. For instance, the management engine 102 may receive workflow data (e.g., test results) associated with a workflow element, such as a lab test element, and based on the workflow data, the management engine 102 may automatically trigger the next stage in the protocol or workflow, such as schedule an appointment, provide a notification, analyze result data in conjunction with other data to determine insights and request input, etc. As a further example, the management engine 102 may access a practitioner's calendar(s) (e.g., a Google™ Outlook™, or timeslots programmed into another accessible calendar), a patient's calendar(s), or a combination thereof, to identify an available time to schedule a meeting or appointment for the patient following a given stage. The management engine 102 may therefore provide the practitioner with the ability to automate certain tasks associated with the practice, such as using a workflow or protocol builder, as described further herein.’ Par. 74-83)
storing the plurality of user inputs in a data store (Malecha Par. 46-“ In some implementations, the management engine 102 may include a web server that processes content requests (e.g., to or from a client device 104). The web server may include an HTTP server, a REST (representational state transfer) service, or other suitable server type. The web server may receive content requests (e.g., product search requests, HTTP requests) from client devices 104, cooperate with the management engine 102 to determine the content, retrieve and incorporate data from the data storage device 110, format the content, and provide the content to the client devices 104. In some instances, the web server may format the content using a web language and provide the content to an application or engine for processing and/or rendering to the user for display.; Par. 73);
processing the plurality of user inputs via an adaptive algorithm that continuously analyzes and adjusts one or more contextual variables in real-time; (Malecha Par. 72-“ The machine learning engine 112 may include software or hardware that is adapted to train models, and/or analyze data using the models, for example, using various types of machine learning algorithms, as described below. In some implementations, the machine learning engine 112 may be incorporated with the management engine 102 or may be operable on a separate server and specially adapted to perform machine learning analysis of provided data. ; Par.84; Par. 91; Par.120-121)
generating personalized workflow automation tasks and content based on dynamically adjusted contextual variables; (Malecha Par. 29-32; Par. 53-“ For instance, the management engine 102 may receive workflow data (e.g., test results) associated with a workflow element, such as a lab test element, and based on the workflow data, the management engine 102 may automatically trigger the next stage in the protocol or workflow, such as schedule an appointment, provide a notification, analyze result data in conjunction with other data to determine insights and request input, etc. ; Par. 51)
providing customized workflow outputs to a user via the user interface module, wherein the adaptive algorithm refines the customization of workflow outputs over time by continuously learning from user interactions (Malecha Par. 38; Par. 173-“ In some implementations, a feedback mechanism may be provided to the practitioner, patient, administrator, or a combination thereof in order to train the system or a machine learning model. For instance, a practitioner may review highlighted attributes and/or indicate to the system if the practitioner believes these attributes are noteworthy, helpful, etc. As such, the management engine 102 may store this information for later use, for example, in weighting which attributes to highlight, selecting additional data to display (e.g., scoring, such as 90% of practitioners use this attribute), or performing other training or analyses.”)
Malecha teaches workflow analysis and the feature is expounded upon by Singh:
embedding symbolic state markers into recursive workflow threads during execution, the symbolic state markers comprising symbolic identifiers, parametric weights, and thread lineage markers that encode a relationship of a current execution path to an upstream symbolic context and a downstream symbolic context; storing the symbolic state markers in a persistent symbolic memory structure; and validating a symbolic integrity of the persistent symbolic memory structure prior to resuming a subsequent recursive execution; and resuming subsequent recursive executions based on the preserved symbolic state markers only upon the persistent symbolic memory structure passing a consistency check, wherein the process is governed by an execution persistence layer that enables a deterministic replay and a structured recovery of the recursive workflow threads without a rehydration of raw execution logs. (Singh Col 6; Col 15-“ With continued reference to FIG. 1 , encoder of transformer may include a residual connection. Residual connection may include adding the output from multi-headed attention to the positional input embedding. In some embodiments, the output from residual connection may go through a layer normalization. In some embodiments, the normalized residual output may be projected through a pointwise feed-forward network for further processing. The pointwise feed-forward network may include a couple of linear layers with a ReLU activation in between. The output may then be added to the input of the pointwise feed-forward network and further normalized.; Col 20-21 With continued reference to FIG. 1 , in one or more embodiments, the SmartDoc and simulation reentry layer may be configured to link to prior CI trails (as described in reference to at least FIG. 4 as well) for the purpose of simulation resumption, reasoning continuity, or governed rehydration of a paused interaction thread. In one or more embodiments, CI trail may include sequence of thematically or temporally linked CI units 124 that collectively represent a persistent record of a user-agent interaction over time. In one or more embodiments, the simulation reentry layer may utilize the presence of a simulation continuity flag within a CI unit 124 to locate a corresponding CI trail checkpoint, enabling the system to identify a precise position within the prior trail from which simulation may be resumed. Upon reentry, the system may access continuity datum, reconstruct the agent's prior cognitive state, and restore relevant simulation variables, thereby aligning the ongoing interaction with the user's historical trajectory. In one or more embodiments, simulation reentry layer may integrate directly with the simulation loop described therein. If the simulation has not ended, and the system is configured to continue with updated context or new user input, the reentry layer may act as a gatekeeper that ensures the resumed interaction begins from a verified and auditable point within a prior CI trail. This linkage may involve querying CI log 148 for the most recent simulation continuity flag tied to a given user and agent identifier pair, validating that the associated CI unit 124 satisfies ethical, memory, and compliance conditions, and initializing the simulation environment accordingly. In one or more embodiments, reentry into a CI trail checkpoint may also restore simulation state variables such as emotional tone, narrative objective, prior user concerns, or unresolved logic branches that were suspended at the time of the pause.; Col 34-35; Col 42-43”)
Malecha is directed to workflow management. Singh improves upon data processing supporting the workflow. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Malecha, as taught by Sing, by utilizing encoder modelling with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Malecha with the motivation of adapting dynamically to individual user interactions or retaining long-term contextual understanding. (Singh Background).
Regarding Claim 11,
The system of Claim 9, further comprising an automation engine in operable communication with the one or more data stores to automate, in real-time, the personalization of a plurality of tailored outputs (Malecha Par. 28-29- Implementations of the technology described herein may include, but are not limited to, automation of lab data ingestion, presentation and/or display within a platform; the creation of workflows for automating certain workflow or protocol processes dealing with patient intake and patient treatment, progression, and tracking; and the identification of high impact data from automated analysis of large sets of patient data for use in virtual experiments.”).
Regarding Claim 12,
The system of Claim 9, further comprising a workflow generation engine to receive the plurality of tailored outputs and autonomously generate a workflow in real-time. (Malecha Par. 29- The technology may automate intake and scheduling, lab ordering, patient follow-up, and notifications, among other things. For instance, the technology may automate steps in an intake procedure or other patient workflow or protocol, such as automatically tracking patient progress, automatically scheduling appointments after a lab result or a certain analysis or lab result, among other operations. Some implementations allow a practitioner to manually generate automated tasks and workflows, provide access to a variety of sources for aggregation, analysis, and/or automation. For instance, as described in detail below, the automation may identify data fields, extract values, make the data available for display, and/or use the data (or data derived therefrom) in the automated workflows.; Par. 106; Par. 150).
Regarding Claim 13,
The system of Claim 12, wherein the workflow generation engine receives a plurality of contextual variables to refine the workflow. (Malecha Par. 38- In some instances, the platform may generate control group(s) and simulate trials, treatments, and outcomes using the data for the plurality of patients. For instance, the platform may run data-based experimentation using data sets, models, and/or computer learning to surface relationships and test causal theories in complex, multi-variable contexts.; Par. 49; Par. 111).
Regarding Claim 14,
The system of Claim 13, wherein the workflow generation engine receives a plurality of historical user data to refine the workflow. (Malecha Par. 149- As illustrated, the management engine 102 may combine information from one or more sources, such as biometric data, patient survey data, and lab report data. In some implementations, the graphical user interface 1502 may include individual display regions 1504 a, 1504 b, 1504 c, 1504 d, and 1504 n, which display patient data or indications divided by category. For instance, the display regions may be divided into client reported data, medical and health history; Par. 144).
Regarding Claim 15,
The system of Claim 14, wherein the workflow generation engine receives one or more user interactions to refine the workflow. (Malecha Par. 173- In some implementations, a feedback mechanism may be provided to the practitioner, patient, administrator, or a combination thereof in order to train the system or a machine learning model. For instance, a practitioner may review highlighted attributes and/or indicate to the system if the practitioner believes these attributes are noteworthy, helpful, etc. As such, the management engine 102 may store this information for later use, for example, in weighting which attributes to highlight, selecting additional data to display (e.g., scoring, such as 90% of practitioners use this attribute), or performing other training or analyses.).
Regarding Claim 16,
The system of Claim 15, wherein the workflow is used to organize a plurality of tasks. (Malecha Par. 111- In some instances, the management engine 102 (e.g., using the method 200 or other operations herein) may automatically extract and highlight data of interest to a patient or practitioner from disparate lab reports while allowing the user to stay in the context of a particular dashboard or interface, thereby avoiding the need of the user to navigate the interface to an underlying report or data. The displays or dashboards may format and organize data in a quick and efficient manner and may allow a practitioner to customize the formatting or display of the data output, for example, from a laboratory's own reporting format to a practitioner preferred format. For instance, a user may select various dashboard modules for data visualization, reporting, and practice automation.).
Regarding Claim 17,
Malecha teaches
A computer-readable storage medium having stored thereon instructions that, when executed by at least one processor, cause the processor to perform a method for adaptive workflow optimization, the method comprising the steps of; receiving, via a user interface module, a plurality of user inputs including user information, workflow data, and user preferences, and personality-based contextual data; (Malecha Par. 5; Par. 29-32; Par. 53-“In some implementations, the management engine 102 may automate step(s) within a workflow or protocol, such an intake procedure, by automatically triggering a next stage in the protocol or workflow, such as scheduling a patient meeting or appointment following completion of an intake procedure or part thereof, scheduling a supplement order following a lab result, etc. For instance, the management engine 102 may receive workflow data (e.g., test results) associated with a workflow element, such as a lab test element, and based on the workflow data, the management engine 102 may automatically trigger the next stage in the protocol or workflow, such as schedule an appointment, provide a notification, analyze result data in conjunction with other data to determine insights and request input, etc. As a further example, the management engine 102 may access a practitioner's calendar(s) (e.g., a Google™ Outlook™, or timeslots programmed into another accessible calendar), a patient's calendar(s), or a combination thereof, to identify an available time to schedule a meeting or appointment for the patient following a given stage. The management engine 102 may therefore provide the practitioner with the ability to automate certain tasks associated with the practice, such as using a workflow or protocol builder, as described further herein.’ Par. 74-83)
storing the plurality of user inputs in a data store; (Malecha Par. 46-“ In some implementations, the management engine 102 may include a web server that processes content requests (e.g., to or from a client device 104). The web server may include an HTTP server, a REST (representational state transfer) service, or other suitable server type. The web server may receive content requests (e.g., product search requests, HTTP requests) from client devices 104, cooperate with the management engine 102 to determine the content, retrieve and incorporate data from the data storage device 110, format the content, and provide the content to the client devices 104. In some instances, the web server may format the content using a web language and provide the content to an application or engine for processing and/or rendering to the user for display.; Par. 73);
processing the plurality of user inputs via an adaptive algorithm that continuously analyzes and adjusts one or more contextual variables in real-time; (Malecha Par. 72-“ The machine learning engine 112 may include software or hardware that is adapted to train models, and/or analyze data using the models, for example, using various types of machine learning algorithms, as described below. In some implementations, the machine learning engine 112 may be incorporated with the management engine 102 or may be operable on a separate server and specially adapted to perform machine learning analysis of provided data. ; Par.84; Par. 91; Par.120-121)
generating personalized workflow automation tasks and content based on dynamically adjusted contextual variables; (Malecha Par. 29-32; Par. 53-“ For instance, the management engine 102 may receive workflow data (e.g., test results) associated with a workflow element, such as a lab test element, and based on the workflow data, the management engine 102 may automatically trigger the next stage in the protocol or workflow, such as schedule an appointment, provide a notification, analyze result data in conjunction with other data to determine insights and request input, etc. ; Par. 51)
providing customized workflow outputs to a user via the user interface module, wherein the adaptive algorithm refines the customization of workflow outputs over time by continuously learning from user interactions (Malecha Par. 38; Par. 173-“ In some implementations, a feedback mechanism may be provided to the practitioner, patient, administrator, or a combination thereof in order to train the system or a machine learning model. For instance, a practitioner may review highlighted attributes and/or indicate to the system if the practitioner believes these attributes are noteworthy, helpful, etc. As such, the management engine 102 may store this information for later use, for example, in weighting which attributes to highlight, selecting additional data to display (e.g., scoring, such as 90% of practitioners use this attribute), or performing other training or analyses.”)
Malecha teaches workflow analysis and the feature is expounded upon by Singh:
embedding symbolic state markers into recursive workflow threads during execution, the symbolic state markers comprising symbolic identifiers, parametric weights, and thread lineage markers; storing the symbolic state markers in a persistent symbolic memory structure; validating a symbolic integrity of the persistent symbolic memory structure prior to resuming a subsequent recursive execution; and resuming the subsequent recursive execution based on the preserved symbolic state markers only upon the persistent symbolic memory structure passing a consistency check,wherein the subsequent recursive execution is governed by an execution persistence layer that enables a deterministic replay and a structured recovery without a rehydration of raw execution logs. (Singh Col 6; Col 15-“ With continued reference to FIG. 1 , encoder of transformer may include a residual connection. Residual connection may include adding the output from multi-headed attention to the positional input embedding. In some embodiments, the output from residual connection may go through a layer normalization. In some embodiments, the normalized residual output may be projected through a pointwise feed-forward network for further processing. The pointwise feed-forward network may include a couple of linear layers with a ReLU activation in between. The output may then be added to the input of the pointwise feed-forward network and further normalized.; Col 20-21 With continued reference to FIG. 1 , in one or more embodiments, the SmartDoc and simulation reentry layer may be configured to link to prior CI trails (as described in reference to at least FIG. 4 as well) for the purpose of simulation resumption, reasoning continuity, or governed rehydration of a paused interaction thread. In one or more embodiments, CI trail may include sequence of thematically or temporally linked CI units 124 that collectively represent a persistent record of a user-agent interaction over time. In one or more embodiments, the simulation reentry layer may utilize the presence of a simulation continuity flag within a CI unit 124 to locate a corresponding CI trail checkpoint, enabling the system to identify a precise position within the prior trail from which simulation may be resumed. Upon reentry, the system may access continuity datum, reconstruct the agent's prior cognitive state, and restore relevant simulation variables, thereby aligning the ongoing interaction with the user's historical trajectory. In one or more embodiments, simulation reentry layer may integrate directly with the simulation loop described therein. If the simulation has not ended, and the system is configured to continue with updated context or new user input, the reentry layer may act as a gatekeeper that ensures the resumed interaction begins from a verified and auditable point within a prior CI trail. This linkage may involve querying CI log 148 for the most recent simulation continuity flag tied to a given user and agent identifier pair, validating that the associated CI unit 124 satisfies ethical, memory, and compliance conditions, and initializing the simulation environment accordingly. In one or more embodiments, reentry into a CI trail checkpoint may also restore simulation state variables such as emotional tone, narrative objective, prior user concerns, or unresolved logic branches that were suspended at the time of the pause.; Col 34-35; Col 42-43”)
Malecha is directed to workflow management. Singh improves upon data processing supporting the workflow. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Malecha, as taught by Sing, by utilizing encoder modelling with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Malecha with the motivation of adapting dynamically to individual user interactions or retaining long-term contextual understanding. (Singh Background).
Claims 2-8 and 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Malecha, US Publication No. 20230060235A1, [hereinafter Malecha], in view of Singh, US Patent No. 12555008B1, [hereinafter Singh] and in further view of Neumann, US Publication No. 20240346029A1, [hereinafter Neumann].
Regarding Claim 2 and Claim 10, Malecha in view of Singh teach The system of Claim 1,… and The method of Claim 9,…
Malecha in view of Singh teach metrics and the feature is expounded upon by Neumann:
wherein the plurality of user inputs is comprised of one or more user personality metrics, one or more philosophical beliefs, and one or more cognitive biases (Neumann Par. 22; Par. 114; Par. 139- Continuing to refer to FIG. 10 , physiological state data may include psychological data. Psychological data may include any data generated using psychological, neuro-psychological, and/or cognitive evaluations, as well as diagnostic screening tests, personality tests, personal compatibility tests, or the like; such data may include, without limitation, numerical score data entered by an evaluating professional and/or by a subject performing a self-test such as a computerized questionnaire. Psychological data may include textual, video, or image data describing testing, analysis, and/or conclusions entered by a medical professional such as without limitation a psychologist, psychiatrist, psychotherapist, social worker, a medical doctor, or the like. Psychological data may include data gathered from user interactions with persons, documents, and/or computing devices 1004; for instance, user patterns of purchases, including electronic purchases, communication such as via chat-rooms or the like, any textual, image, video, and/or data produced by the subject, any textual image, video and/or other data depicting and/or describing the subject, or the like. Any psychological data and/or data used to generate psychological data may be analyzed using machine-learning and/or language processing module as described in this disclosure.)
Malecha is directed to workflow management. Singh and Neumann improves upon the input analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Malecha in view of Singh, as taught by Neumann, by utilizing additional input analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Malecha in view of Singh with the motivation of determining one or more impact factors. (Neumann Par. 198).
Regarding Claim 3,
The system of Claim 2, wherein a cloud-based server analyzes the plurality of user inputs. (Malecha Par. 58- Sources of data for the management engine 102 include but are not limited to patient intake data, patient survey or feedback response data (e.g., received from a client device 104), scheduling data (e.g., derived from an external system such as a calendaring database), lab data (e.g., obtained from an external server of a lab provider, such as the laboratory server 108), and data analytics (e.g., from the output of a cloud-based machine learning provider 112). In some examples, the management engine 102 includes software configured to execute via a processor to implement certain acts or functions, such as automatic or semi-automatic processing of data, accessing and ingesting data, data analytics, etc.”).
Regarding Claim 4,
The system of Claim 3, further comprising an automation engine in operable communication with the one or more data stores to automate, in real-time, the personalization of a plurality of tailored outputs. (Malecha Par. 28-29- Implementations of the technology described herein may include, but are not limited to, automation of lab data ingestion, presentation and/or display within a platform; the creation of workflows for automating certain workflow or protocol processes dealing with patient intake and patient treatment, progression, and tracking; and the identification of high impact data from automated analysis of large sets of patient data for use in virtual experiments.”).
Regarding Claim 5,
The system of Claim 4, further comprising a workflow generation engine to receive the plurality of tailored outputs and autonomously generate a workflow in real-time. (Malecha Par. 29- The technology may automate intake and scheduling, lab ordering, patient follow-up, and notifications, among other things. For instance, the technology may automate steps in an intake procedure or other patient workflow or protocol, such as automatically tracking patient progress, automatically scheduling appointments after a lab result or a certain analysis or lab result, among other operations. Some implementations allow a practitioner to manually generate automated tasks and workflows, provide access to a variety of sources for aggregation, analysis, and/or automation. For instance, as described in detail below, the automation may identify data fields, extract values, make the data available for display, and/or use the data (or data derived therefrom) in the automated workflows.; Par. 106; Par. 150).
Regarding Claim 6,
The system of Claim 5, wherein the workflow generation engine receives a plurality of contextual variables to refine the workflow. (Malecha Par. 38- In some instances, the platform may generate control group(s) and simulate trials, treatments, and outcomes using the data for the plurality of patients. For instance, the platform may run data-based experimentation using data sets, models, and/or computer learning to surface relationships and test causal theories in complex, multi-variable contexts.; Par. 49; Par. 111).
Regarding Claim 7,
The system of Claim 6, wherein the workflow generation engine receives a plurality of historical user data to refine the workflow. (Malecha Par. 149- As illustrated, the management engine 102 may combine information from one or more sources, such as biometric data, patient survey data, and lab report data. In some implementations, the graphical user interface 1502 may include individual display regions 1504 a, 1504 b, 1504 c, 1504 d, and 1504 n, which display patient data or indications divided by category. For instance, the display regions may be divided into client reported data, medical and health history; Par. 144).
Regarding Claim 8,
The system of Claim 7, wherein the workflow generation engine receives one or more user interactions to refine the workflow. (Malecha Par. 173- In some implementations, a feedback mechanism may be provided to the practitioner, patient, administrator, or a combination thereof in order to train the system or a machine learning model. For instance, a practitioner may review highlighted attributes and/or indicate to the system if the practitioner believes these attributes are noteworthy, helpful, etc. As such, the management engine 102 may store this information for later use, for example, in weighting which attributes to highlight, selecting additional data to display (e.g., scoring, such as 90% of practitioners use this attribute), or performing other training or analyses.).
Regarding Claim 18, Malecha in view of Fletcher teach The method of Claim 17,,…
Malecha in view of Singh teach metrics and the feature is expounded upon by Neumann:
further comprising the step of detecting and mitigating user cognitive biases through automated recommendations. (Neumann Par. 23; Par. 114;Par. 139- Continuing to refer to FIG. 10 , physiological state data may include psychological data. Psychological data may include any data generated using psychological, neuro-psychological, and/or cognitive evaluations, as well as diagnostic screening tests, personality tests, personal compatibility tests, or the like; such data may include, without limitation, numerical score data entered by an evaluating professional and/or by a subject performing a self-test such as a computerized questionnaire. Psychological data may include textual, video, or image data describing testing, analysis, and/or conclusions entered by a medical professional such as without limitation a psychologist, psychiatrist, psychotherapist, social worker, a medical doctor, or the like. Psychological data may include data gathered from user interactions with persons, documents, and/or computing devices 1004; for instance, user patterns of purchases, including electronic purchases, communication such as via chat-rooms or the like, any textual, image, video, and/or data produced by the subject, any textual image, video and/or other data depicting and/or describing the subject, or the like. Any psychological data and/or data used to generate psychological data may be analyzed using machine-learning and/or language processing module as described in this disclosure.)
Malecha is directed to workflow management. Singh and Neumann improves upon the input analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Malecha in view of Singh, as taught by Neumann, by utilizing additional input analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Malecha in view of Singh with the motivation of determining one or more impact factors. (Neumann Par. 198).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20190129762A1 to Stevens et al.- Abstract-“ Technical solutions are described for interactively executing a workflow that includes multiple workflow steps. An example method includes pulling a preliminary update for the workflow from a data source and modifying the workflow dynamically. The method further includes selecting a workflow step, and pulling updated information from the data source and modifying the workflow step dynamically. Further, the method includes retrieving, from the data source, first results information, indicative of results of executing the updated workflow step by other users, and based on the first results information, executing the updated workflow step. The method further includes accumulating a second results information based on the execution of the workflow step. Further, the execution includes pushing the second results information to the data source upon completion of the workflow step.”
THIS ACTION IS MADE FINAL. 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 extension fee 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 Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624
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