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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 11/7/2025, Applicant, on 2/3/2026, amended claims 1, 8 and 15. Claims 1, 5-8, 12-15, and 19-21 are pending in this application and have been rejected below.
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has been withdrawn pursuant to 37 CFR 1.114.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
2/3/2026 has been entered.
Response to Arguments
Applicant’s arguments filed February 3, 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 February 3, 2026.
On Pg. 9-10 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states claims do not fall within the "mental processes" grouping described in the Office Action. The claims recite operations tied to a computing environment and to execution of automated data flows by an automation platform that are not practically performable as mental evaluations. The claims require: (i) simulation of a plurality of data flows by an automation platform using iterative parameter changes and calculation of key output metrics;(ii) selection of an optimized data flow based on computed metrics; and
(iii) causing the automation platform to implement the selected optimized data flow by executing the one or more data flow steps identified to be automated.. In response, as stated in the 101 analysis below - The claims primarily recite the additional element of using computer components to perform each step. The “system”, “memory device”, “communication device” “processing device”, “data flow optimizer tool”, “automation platform”, “computer program product”, “computer”, “computing system” , “computer processing device”, and “computer readable 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). The machine learning processing is solely used a tool to perform the instructions of the abstract idea.
On Pg. 10 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the ML subsystem is not claimed as a standalone mathematical exercise. The claim ties the ML subsystem to optimization of electronic data flows over time to increase efficiency of the data flow optimizer tool similar to Desjardins (PTAB Sept. 26, 2025) . Examiner finds Applicants arguments in relation to this matter are not persuasive. Specifically, in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”, and the claims reflect the improvement identified in the specification. The improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. Examiner finds no similar improvements to take into consideration here. Examiner maintains the claims are directed to an abstract idea of scheduling available drivers in which computer components are used as a tool to perform the instructions of the scheduling process. Applicant has not presented an argument that alters this analysis. For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
On Pg. 11 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the claims apply any alleged abstract operations in a robotic process automation and automation platform execution context. Consistent with recent USPTO guidance addressing AI and ML innovations, the claims apply ML processing in a manner that improves the functioning of a computer-based system, specifically, by increasing efficiency of future optimized data flow determinations. The ML platform, automation platform, and optimized data flow execution are all integral to achieving this technical result. In response, Examiner respectfully disagrees. The aforementioned procedures are not improvements to a problem in the software arts, a technology or technological field. Receiving data and optimizing data flow is a judicial exception (i.e. abstract idea). The claimed invention is executed by computer elements performing generic
computer functions (see par. 0033-0035). (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 contain improvements to the data analysis of an existing business process and not one of a technology or technological field.
Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Applicants have not identified anything in the claimed invention that shows or even submits the technology is being improved or there was a problem in the technology that the claimed invention solves..
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, 5-8, 12-15, and 19-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 5-8, 12-15, and 19-21 are directed to data flow optimization using machine learning.
Claim 1 recites a system for optimizing electronic data flow using intelligent machine learning, Claim 8 recites an article of manufacture for optimizing electronic data flow using intelligent machine learning and Claim 15 recites a method for optimizing electronic data flow using intelligent machine learning, which include receive a data flow into an automation platform of a data flow optimizer tool, wherein the data flow comprises one or more data flow steps, wherein each data flow step is separate from other data flow steps; receive, for each data flow step of the one or more data flow steps, an indication of current cycle time, touch time, rework time, or wait time; perform a value assessment of the one or more data flow steps by classifying each of the one or more data flow steps as one of (i) value-add, wherein the data flow step directly transforms a request into a fulfilled request with no rework, (ii) non-value-add, wherein the data flow step does not directly transform a request into a fulfilled request, and (iii) required non-value-add, wherein the data flow step is mandatory for enterprise or regulatory purposes; determine, based on the value assessment of each of the one or more data flow steps, corresponding indicators of optionality, level of automation, and opportunity for simultaneous execution; simulate, based on the indicators of optionality, level of automation, and opportunity for simultaneous execution, a plurality of simulated data flows, wherein simulating comprises iteratively changing at least one parameter of at least one data flow step of the one or more data flow steps and calculating predetermined key output metrics for each iteration; determine one or more optimized data flow options from the plurality of simulated data flows based on a comparison of the predetermined key output metrics amongst the plurality of simulated data flows, wherein each optimized data flow option comprises a representation of the one or more data flow steps to be at least one of: automated, eliminated, or reorganized; cause to be displayed the predetermined key output metrics corresponding to the one or more optimized data flow options; receive a selection of a selected optimized data flow of the one or more optimized data flow options; implement the selected optimized data flow by executing the one or more data flow steps identified to be automated in the selected optimized data flow.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes”- evaluation and “Methods of Organizing Human Activity” – managing interactions. The recitation of “system”, “memory device”, “communication device”, “user device”, “processing device”, “data flow optimizer tool”, “automation platform”, “computer program product”, “computer”, “computing system” , “computer processing device”,” computer-readable program code “ and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Mental Processes” – evaluation and “Methods of Organizing Human Activity”- managing interactions. 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 “system”, “memory device”, “communication device”, “user device”, “processing device”, “data flow optimizer tool”, “automation platform”, “computer program product”, “computer”, “computing system” , “computer processing device”,” computer-readable program code “ and “computer readable 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 8 and claim 15 recite using one or more machine learning platform/techniques. The specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning 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 customer data flow analysis.
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 “system”, “memory device”, “communication device”, “user device”, “processing device”, “data flow optimizer tool”, “automation platform”, “computer program product”, “computer”, “computing system” , “computer processing device”,” computer-readable program code“ and “computer readable 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).
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 5-7, 12-14 and 19-21 recite wherein the optimization request is autonomously provided to the data flow optimizer tool by the processing device, the processing device providing the optimization request for a previously optimized data flow; the machine learning platform is further configured to output a tutorial for optimized data flow design, the tutorial indicating to a user instructions for data flow layout; wherein the one or more key output metrics provided to the automation platform are prioritized, the prioritized key output metrics indicating to the automation platform the weight to be given to the one or more key output metrics during simulation of a plurality of data flows 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, 8 and 15. Regarding Claims, 5,7, 12, 14, 19, 21 and the additional elements of “processing device”, “automation platform”, “data flow optimizer tool”, - 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). Regarding claims 6, 13, 20 and the additional element of machine learning platform - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1, 5-8, 12-15, and 19-21 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 8 and Claim 15), the prior art does not teach or fairly suggest:
“… receiving, for each data flow step of the one or more data flow steps, an indication of current cycle time, touch time, rework time, or wait time; performing a value assessment of the one or more data flow steps by classifying each of the one or more data flow steps as one of (i) value-add, wherein the data flow step directly transforms a request into a fulfilled request with no rework, (ii) non-value- add, wherein the data flow step does not directly transform a request into a fulfilled request, and (iii) required non-value-add, wherein the data flow step is mandatory for enterprise or regulatory purposes; determining, based on the value assessment of each of the one or more data flow steps, corresponding indicators of optionality, level of automation, and the opportunity for simultaneous execution; simulating, based on the indicators of optionality, level of automation, and opportunity for simultaneous execution, a plurality of simulated data flows, wherein simulating comprises iteratively changing at least one parameter of at least one data flow step of the one or more data flow steps and calculating predetermined key output metrics for each iteration; determining one or more optimized data flow options from the plurality of simulated data flows based on a comparison of the predetermined key output metrics amongst the plurality of simulated data flows, wherein each optimized data flow option comprises a representation of the one or more data flow steps to be at least one of: automated, eliminated, or reorganized; causing to be displayed, on a user device, the predetermined key output metrics corresponding to the one or more optimized data flow options; receiving, from the user device, a selection of a selected optimized data flow of the one or more optimized data flow options; and providing the selected optimized data flow to the machine learning platform for increased efficiency of future optimized data flow option determinations.”.
Examiner finds that Gupta et al. (U.S. Publication 20210004711 A1) teaches a system and method automatically generating, using machine learning, a data structure that stores a knowledge graph for a decision making process that is to be automated. The knowledge graph includes one or more entities, one or more states of each of the entities, and transitions for each of the states (see Abstract). In particular, Gupta discloses facilitates not only automation of a process, but also facilitates optimizes the process and decision-making that is part of executing the method using artificial intelligence (AI). The system 100 derives actionable, real-time insights from operations intelligence to augment the formulation, orchestration, and automation of an adaptive process. The system 100 further facilitates a cognitive RPA that formulates and orchestrates processes that reshape themselves as they run. These processes are data driven, adaptive, and intelligent, determining and executing a next action based on context formation from data, instead of the same repeatable sequence of actions (see par. 0063) (see par. 0077-0080).
Linde et al. (U.S. Publication 20190132351 A1) teaches method and apparatus useful for data risk monitoring and management includes configuration and analysis of data flows to identify and assess risk and compliance to various regulatory standards and business practices. The evaluation of monitored data flows are then further used to identify potential security risks based on deviation from expected flows or compliant handling methods (see par. Abstract). In particular, Linde discloses the data flow platform 101 enables the grouping and sequencing of individual data flow steps (i.e., “processing nodes”) into distinct business processes. By coupling the timestamps and the business processes together, in the positive case, the data flow platform 101 can reveal which steps in the data flow were recently matched, and how many times a match was identified in a given span of time. In the negative case, the data flow platform 101 shows for a given business process the steps in the data flow that were never matched or rarely matched over a given span of time (see par. 0084).
Although Linde and Gupta teach the dataflow elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the data flow analysis and classification.
Additionally, Examiner finds Venkateswarulu et al. (U.S. Publication 20180165336A1) teaches methods, systems and computer program products associated Work data flow, a step-by-step procedure by which data is manipulated in order to analyze the actual data, resulting from user requests is created by interactions with one or more representations of a domain-specific language. (see Abstract). In particular, Venkateswarulu discloses a user of the tool may perform an action to create, update, or modify a work data flow using a user interface. A flow processor may look up access permissions and retrieve metadata about the work data flow from a flow store. A flow validator may check the dependencies between steps in the work data flow. For example, the flow validator may be able to validate that no cyclical dependencies exist between the steps and may be able to confirm that all inputs that are needed by each step are accessible. If the flow validator determines that there is an error in the dependencies in the work data flow, it may notify the user interface and cause it to display an error notification to the user. A user may save a work data flow using the user interface and the work data flow may be stored in the flow store with any required access permissions. (see par. 0011).
However Venkateswarulu, individually and in combination, fails to teach the specific classification of the data flow. Therefore, for at least these reasons, Claim 1 (similarly Claim 8 and Claim 15) is eligible over the prior art.
The dependent claims 5-7, 12-14 and19-21 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1 ( claim 8 and claim 15) that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20180336493A1 to Hayes et al.- Abstract-“ A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.”.
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”).
Another resource that is available to applicants is the Patent Application Information Retrieval (PAIR). Information regarding the status of an application can be obtained from the (PAIR) system. Status information for published applications may be obtained from either Private PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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