DETAILED ACTION
Status of Claims
This communication is the final action on the merits in response to the amendments and arguments filed on May 15, 2026. Claims 1, 9, 14, 16, and 20 were amended. Claims 4-6, 10, 15, and 24 were canceled. Claims 26-28 were added. Claims 1, 3, 9, 11-14, 16-17, 20-23, and 25-28 are currently pending and have been examined.
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 .
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, 3, 9, 11-14, 16-17, 20-23, and 25-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1, 3, 9, 11-14, 21-23, and 26 are directed to a process. Claims 16-17, 25, and 27 are directed to a machine. Claims 20 and 28 are directed to an article of manufacture. As such, each claim is directed to a statutory category of invention.
Step 2A Prong 1
The examiner has identified independent Claim 16 as the claim that represents the claimed invention for analysis and is similar to independent Claims 1 and 20.
Independent Claim 16 recites the following abstract ideas: “stored internal representations of a plurality of automated processes comprising a plurality of tasks, task dependencies, and permissions associated with the plurality of tasks; with substitute approvers observed as assigned as substitute approvers for original approvers of the automated processes and predict one or more substitute approvers for an original approver; during execution of a given automated process instance specifying an original approver for a task in the given automated process instance, receiving an out-of-office message of the original approver; extracting features from text of the out-of-office message, wherein extracting features comprises the text of the out-of-office message, finding attributes for named entities, and building a knowledge graph of the named entities and attributes; converting the knowledge graph into a vector representation; incorporating an identifier of the original approver into the vector representation; determining, from the stored internal representations, an automated process definition identifier of the given automated process instance and a task definition identifier of the automated process instance, wherein the task definition identifies the task for seeking approval from the original approver; sending the vector representation, the automated process definition identifier, and the task definition identifier ; based on the vector representation, the automated process definition identifier, and the task definition identifier, predicting a substitute approver for the original approver and outputting an identifier of the predicted substitute approver; receivingthe identifier of the predicted substitute approver; determining, by consulting an access control list, lookup table, or configuration information, whether the predicted substitute approver has permissions to approve the task; responsive to determining that the predicted substitute approver has permissions to approve the task: assign the predicted substitute approver to the task and to reflect that the predicted substitute approver is authorized to approve the task; redirecting an original request for approval for the task to the identifier of the predicted substitute approver; and continuing execution of the given automated process instance responsive to receiving approval or rejection of the task from the predicted substitute approver.”
The limitations, as drafted, are a process that, under its broadest reasonable interpretation, relates to managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions (i.e., stored internal representations of a plurality of automated processes comprising a plurality of tasks, task dependencies, and permissions associated with the plurality of tasks; with substitute approvers observed as assigned as substitute approvers for original approvers of the automated processes and predict one or more substitute approvers for an original approver; during execution of a given automated process instance specifying an original approver for a task in the given automated process instance, receiving an out-of-office message of the original approver; extracting features from text of the out-of-office message, wherein extracting features comprises the text of the out-of-office message, finding attributes for named entities, and building a knowledge graph of the named entities and attributes; converting the knowledge graph into a vector representation; incorporating an identifier of the original approver into the vector representation; determining, from the stored internal representations, an automated process definition identifier of the given automated process instance and a task definition identifier of the automated process instance, wherein the task definition identifies the task for seeking approval from the original approver; sending the vector representation, the automated process definition identifier, and the task definition identifier; based on the vector representation, the automated process definition identifier, and the task definition identifier, predicting a substitute approver for the original approver and outputting an identifier of the predicted substitute approver; receiving the identifier of the predicted substitute approver; determining, by consulting an access control list, lookup table, or configuration information, whether the predicted substitute approver has permissions to approve the task; responsive to determining that the predicted substitute approver has permissions to approve the task: assign the predicted substitute approver to the task and to reflect that the predicted substitute approver is authorized to approve the task; redirecting an original request for approval for the task to the identifier of the predicted substitute approver; and continuing execution of the given automated process instance responsive to receiving approval or rejection of the task from the predicted substitute approver), but for the recitation of generic computer components (i.e., A computing system comprising at least one hardware processor, at least one memory coupled to the at least one hardware processor, a machine learning model trained and configured to predict data, one or more non-transitory computer-readable media having stored therein computer-executable instructions, an automatic electronic message, applying named entity recognition (NER), and updating a process system). If a claim limitation, under its broadest reasonable interpretation, relates to managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). In particular, the claim recites the additional elements of a computing system comprising at least one hardware processor, at least one memory coupled to the at least one hardware processor, a machine learning model trained and configured to predict data, one or more non-transitory computer-readable media having stored therein computer-executable instructions, an automatic electronic message, applying named entity recognition (NER), and updating a process system. The computer hardware is recited at a high level of generality (i.e., generic trained machine learning model predicting and outputting data, generic computers receiving, processing, and transmitting data, and applying NER to data in a generic manner) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application, since they do not involve improvements to the functioning of a computer or to any other technology or technical field (MPEP 2106.05(a)), they do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and they do not apply or use the abstract idea in some other meaningful way beyond generally linking its use to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claim is directed to an abstract idea without a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. The additional elements of using computer hardware (a computing system comprising at least one hardware processor, at least one memory coupled to the at least one hardware processor, a machine learning model trained and configured to predict data, one or more non-transitory computer-readable media having stored therein computer-executable instructions, an automatic electronic message, applying named entity recognition (NER), and updating a process system) amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the claim is not patent-eligible.
Dependent claim 17 recites “a user interface,” which is recited as a generic interface. Dependent claim 22 recites “retraining the machine learning model,” which is recited in a generic manner. The additional elements are generic technology used to implement the abstract idea, and they do not integrate the abstract idea into a practical application, nor are they sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination.
Dependent claims 3, 9, 11-14, 21, 23, and 25-28 do not include any additional elements beyond those identified above. They further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. As such, they do not integrate the abstract idea into a practical application, nor are they sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination.
Therefore, dependent claims 3, 9, 11-14, 17, 21-23, and 25-28 are directed to an abstract idea, and do not include additional elements that integrate the abstract idea into a practical application, or that are sufficient to amount to significantly more than the abstract idea. Thus, the aforementioned claims are not patent-eligible.
Allowable Subject Matter
Claims 1, 3-6, 9-17, and 20-25 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 101 set forth in this Office action.
Bresloff teaches receiving an out-of-office message of an original approver for a task in an automated process instance, extracting features from the message, sending features to a machine learning model trained to predict a substitute approver for the original approver, predicting a substitute approver, receiving an identifier of the substitute approver, and sending a message to the substitute approver seeking approval of the task. Meunier teaches applying NER to text of the out-of-office message, finding attributes for named entities, and building a knowledge graph of the named entities and attributes. However, the combination of references does not teach continuing execution of the given automated process instance responsive to receiving approval or rejection of the task from the substitute approver.
The closest NPL, “Create out-of-office approvals for expenses and fund requests,” teaches a system for selecting pre-defined business rules for colleagues when away from the office, such as assigning an approval to another colleague while away. However, it does not teach extracting features from an out-of-office message, sending features to a machine learning model trained to predict a substitute approver, or a machine learning model predicting the substitute approver.
Response to Arguments
Applicant’s Argument Regarding 35 USC 112(b) Rejection of Claim 23: Independent claim 1 is amended, and it provides proper antecedent basis for claim 23.
Examiner’s Response: Applicant’s amendments have been fully considered and they resolve the identified issue. As such, the rejection is withdrawn.
Applicant’s Argument Regarding 35 USC 101 Rejection of Claims 1, 3-6, 9-17, and 20-25:
Step 2A, Prong 1:
Using independent claim 16 as a claim that represents the claimed invention for analysis, the Action alleges that claim 16 falls within the "certain methods of organizing human activity" grouping of abstract ideas. Action, pp. 3-6. Applicant respectfully disagrees because amended claim 16 recites a technical solution to a technical problem arising in process automation systems, namely, how to keep execution of a running automated process instance moving when an approval task encounters an absent approver.
Just as Example 47 is not directed simply to the abstract idea of taking actions to improve network safety, amended claim 16 is not directed simply to "predicting a substitute approver." Instead, amended claim 16 recites particular technical details of how a process automation system operates on machine-readable process information and machine-generated features to control a running workflow instance.
In particular, amended claim 16 recites, inter alia: stored internal representations of automated processes including tasks, dependencies, and permissions; conversion of a knowledge graph into a vector representation; incorporation of the original approver into the vector representation; determination of process-definition and task-definition identifiers from stored internal representations; consultation of an access control list, lookup table, or configuration information to determine whether the predicted substitute approver is authorized; updating of a process system to reflect authorization and assignment; redirection of the approval request; and conditional continuation of execution of the automated process instance depending on the authorization determination. Such features are not directed to a fundamental economic practice, legal interaction, or social interaction between people, but rather to control of a machine-implemented workflow system using internally represented process state and authorization state. See, e.g., paragraphs [0034]-[0035], [0044]-[0056], [0075]-[0087], [0092]-[0100], [0112]-[0119], and [0199]-[0203] of the published Application.
The Action characterizes the current claims as steps that can be performed in the human mind or with pen and paper, such as extracting features from an out-of-office message, finding attributes, building a knowledge graph, predicting a substitute approver, and sending a message to the substitute approver. Action, pp. 19-20. Such a characterization is inconsistent with amended claim 16. Amended claim 16 recites features that are directed not merely to extracting information from a message, but to machine-executed operations on stored process representations and process-system authorization data, including consulting access control list, lookup table, or configuration information, updating process-system state, and automatically continuing execution of a running automated process instance based on updated authorization and assignment state. Dependent claims further recite blocking when permissions are lacking. Those operations are part of a computerized workflow-control mechanism and are not fairly characterized as mere human organization of behavior or as processes that could reasonably be performed in the human mind or with pen and paper. The specification confirms that internal process representations include tasks, dependencies, permissions, process identifiers, and task identifiers, and that execution of the process instance is either continued or blocked based on authorization of the substitute approver. See, e.g., paragraphs [0053]-[0056], [0078], [0081]- [0087], [0099]-[0100], and [0112]-[0119] of the published Application.
Step 2A, Prong 2:
Even assuming purely for the sake of argument that the claims are deemed to recite a judicial exception, the claims clearly integrate such alleged exception into a practical application. The present amendments directly address the rejections articulated in the Action, The Action states that the current claims recite only the idea of a solution or outcome, fail to recite details of how a solution to a problem is accomplished, use the machine learning model and computer in their ordinary capacity, and, unlike claim 3 of Example 47, do not recite an automatic technical follow-on step that reflects a technical improvement. Action, pp. 18-21. As discussed below, amended claim 16 provides such technical details.
Amended claim 16 now recites a particular way to achieve the desired outcome. Specifically, amended claim 16 recites: extracting structured information from the out-of-office message through named entity recognition and knowledge-graph construction; converting the knowledge graph into a vector representation and incorporating the original approver into that vector representation; determining process-definition and task-definition identifiers from stored internal process representations; using those workflow-specific features as inputs to the machine learning model; determining authorization by consulting an access control list, lookup table, or configuration information; updating the process system to reflect assignment and authorization of the substitute approver; redirecting the approval request; and continuing execution of the automated process instance only after the process system has updated assignment and authorization state for the predicted substitute approver. Rather than merely reflecting the idea of selecting a substitute, amended claim 16 thus recites a concrete workflow-control pipeline implemented in a process automation system. Support for these details appears throughout the specification, including in paragraphs [0046]-[0056], [0078], [0081]-[0087], [0094]-[0100], [0112]-[0119], and [0150]-[0151] of the published Application. Applicant submits that the amendments to claim 16 do not merely add further detail regarding feature extraction or prediction, but instead add post-prediction control logic that causes the process automation system to alter assignment state, authorization state, and execution state of the running automated process instance based on the model output.
The Action states that the current claims are not analogous to claim 3 of Example 47 because they do not provide a technically improved step and instead merely automate part of a manual process. Action, p. 20. Applicant respectfully submits that this criticism is directly addressed by the present amendments. In Example 47, the USPTO explained that claim 3 was eligible because the ANN output was not merely displayed, reported, or used as advice, but instead was used to trigger concrete computer-network remedial operations-namely, automatically dropping malicious packets and blocking future traffic-thereby changing the operational state of the protected network. Amended claim 16 similarly recites post-prediction technical control in the context of a process automation system. Specifically, the model output is not merely a recommendation regarding who should approve. Rather, the identifier of the predicted substitute approver is used by the process automation system to perform concrete machine-executed workflow-remediation operations within the running automated process instance: consulting an access control list, lookup table, or configuration information to determine whether the predicted substitute approver is authorized; updating the process system to assign the predicted substitute approver to the task and to reflect that the predicted substitute approver is authorized to approve the task; redirecting the approval request for the task to the predicted substitute approver; and then continuing execution of the automated process instance only after the process system has updated assignment and authorization state for the predicted substitute approver. Dependent claim 27 further recites blocking execution when the predicted substitute approver lacks permissions. These are not merely notification or presentation steps. Instead, these are machine-executed control steps that alter authorization state, assignment state, and execution state of the process automation system itself. Thus, as in Example 47 claim 3, the machine-learning output in amended claim 16 is an intermediate result used to trigger specific technical remedial actions in the relevant computing environment, rather than an endpoint or mere automation of a human decision. The specification expressly discloses these automatic follow-on operations. See, e.g., paragraphs [0051]-[0056], [0065]-[0066], and [0112] of the published Application.
Applicant additionally submits that Example 42 of the 2019 PEG further demonstrates the eligibility of amended claim 16. In Example 42, claim 1 was eligible because the claim as a whole integrated a method of organizing human activity into a practical application through a specific technical implementation, including converting non-standardized information into a standardized format, automatically generating a message whenever updated information was stored, and transmitting the message in real time, thereby improving prior systems. Here, amended claim 16 similarly recites a specific technical implementation: converting unstructured out-of-office message content into a machine-usable graph/vector representation, augmenting that representation with internal workflow metadata, automatically updating process-system authorization and assignment state, automatically redirecting the approval request, and automatically controlling whether workflow execution proceeds based on updated assignment and authorization state. Dependent claims 26-28 further recite blocking execution when permissions are lacking. As in Example 42, the amended claim reflects a specific improvement over prior systems, not merely generic computerization of a human interaction.
The technical improvement is also supported by the specification itself. The specification explains that, in conventional rule-based approaches, processes can halt when an approver is absent, substitution rules are difficult to maintain, organization changes make such rules stale, and it may be impossible for a given person to substitute due to organization policy or permissions. See, e.g., paragraphs [0003]-[0006], [0021]-[0026], and [0120] of the published Application. The specification then discloses a machine-learning-based workflow system that uses process metadata, process identifiers, task identifiers, permissions, and vector representations derived from knowledge graphs to determine a suitable substitute and control continued execution of the process. See, e.g., paragraphs [0034]-[0035], [0046]-[0056], [0075]- [0087], [0092]-[0100], and [0113]-[0119] of the published Application. The specification further identifies system-level advantages, including avoiding business disruption or delays, providing substitution assignments on the fly, saving storage by avoiding explicit rule storage, increasing performance because processes are completed more quickly, and providing a single point of sharing across backend task providers. See, e.g., paragraphs [0199]-[0203] of the published Application. These are precisely the types of improvements to the operation of a computer-based process automation system that are relevant under Step 2A, Prong Two.
The Action relies on introductory paragraphs [0004] and [0031]-[0033] of the specification to characterize the invention as improving only an abstract idea. Action, p. 19. Applicant respectfully disagrees. Those paragraphs explain the problem context and certain high- level benefits, but they do not exhaust the technical disclosure. The detailed description provides the concrete implementation that is reflected in the amended claims: internal workflow representations with tasks, dependencies, and permissions; process- and task-definition identifiers used as model features; process-instance correlation in the broker/orchestrator architecture; knowledge-graph and vector-representation generation; access control list /lookup/configuration authorization checks; process-system updates; and block-or-continue execution control. Indeed, the August 4, 2025 USPTO memorandum expressly reminds examiners to consider whether the specification describes an improvement to computer functionality or another technical field and whether the claim covers a particular solution or particular way to achieve an outcome. Amended claim 16 does so.
Step 2B:
The specific combination of features recited in amended claim 16 represents a non- conventional approach to controlling approval routing in a process automation system. As amended, claim 16 recites a specific ordered combination: receiving an automatic out-of-office message during execution of a running automated process instance; extracting message information through NER and knowledge-graph construction; converting the graph into a vector representation that incorporates the original approver; determining workflow identifiers from stored internal process representations; using those workflow-specific features to generate a substitute prediction; consulting access control list/lookup/configuration data to determine authorization; updating process-system assignment and authorization state; redirecting the approval request; and continuing execution of the process instance based on updated assignment and authorization state, with dependent claims 26-28 further reciting blocking execution when permissions are lacking. Such an ordered combination also represents a workflow-system analogue of the eligible ordered combination in Example 47 claim 3, because the model output is used to trigger concrete remedial control operations in the relevant technical environment, rather than merely being displayed or used as advice. The Action does not identify evidence that this ordered combination is well-understood, routine, and conventional.
Further, the specification describes this combination as an alternative to manually maintained substitution rules and as part of a specific process-automation architecture involving a process engine, mail client, intelligence orchestrator or broker, and machine learning model. See, e.g., paragraphs [0092]-[0100] and [0106]-[0119] of the published Application. The Application additionally explains that the approach yields on-the-fly substitute assignment, reduced maintenance burden, storage savings, improved process completion time, and a single integration point across backend systems. See, e.g., paragraphs [0199]-[0203] of the published Application. These disclosures support the conclusion that the claims, either individually or in ordered combination, amount to significantly more than any alleged abstract idea.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive.
Step 2A, Prong 1:
The stored internal representations, the conversion of a knowledge graph into a vector representation, incorporation of the original approver into the vector representation, the determination of process-definition and task-definition identifiers, the consultation of an access control list, lookup table, or configuration information to determine whether the predicted substitute approver is authorized, the redirection of the approval request, and conditional continuation of execution of a process instance are all part of the abstract idea, specifically Certain Methods of Organizing Human Activity. The automation and implementation of the processes on a computer is using the computer as a tool to implement the abstract idea. This also applies to the dependent claims reciting blocking when permissions are lacking, as this is also part of the abstract idea, automated and implemented using a computer.
Step 2A, Prong 2:
Extracting structured information from the out-of-office message, knowledge-graph construction, converting the knowledge graph into a vector representation and incorporating the original approver into that vector representation, determining process-definition and task-definition identifiers from stored internal process representations, determining authorization by consulting an access control list, lookup table, or configuration information, updating to reflect assignment and authorization of the substitute approver, redirecting the approval request, and continuing execution of the process instance only after updating assignment and authorization state for the predicted substitute approver, are all part of the abstract idea. Regarding the additional elements, the named entity recognition is recited in a generic manner to extract information, the machine learning model is also recited in a generic manner to receive inputs and generate outputs, and the process system is described in the specification paragraph [0054] “e.g., database, metadata, or the like” and is recited in a generic manner as being updated with information.
Regarding Applicant’s argument that the amendments to claim 16 “add post-prediction control logic that causes the process automation system to alter assignment state, authorization state, and execution state of the running automated process instance based on the model output,” altering assignment state, authorization state, and execution state based on an output are also part of the abstract idea, and the machine learning model providing the output is recited at a high level, as a tool to implement the abstract idea, without any technical improvement to machine learning models.
The presently amended claims are still not analogous to claim 3 of Example 47. Claim 3 of Example 47 provided a technical improvement, and the specification provided support for the technical improvement. The present claims recite the additional elements at a high level. The steps of consulting an access control list, lookup table, or configuration information to determine whether the predicted substitute approver is authorized, assigning the predicted substitute approver to the task and reflecting that the predicted substitute approver is authorized to approve the task, redirecting the approval request for the task to the predicted substitute approver, and then continuing execution of the process instance only after updating assignment and authorization state for the predicted substitute approver, are all part of the abstract idea. The automation and implementation of the process on a computer uses the computer as a mere tool to implement the abstract idea, without any improvement to the functioning of the computer itself. Regarding dependent claim 27, blocking execution when the predicted substitute approver lacks permissions is part of the abstract idea, and the computer is used as a mere tool to implement the abstract idea. However, even if the blocking was considered an additional element, it would still be reciting the idea of a solution, covering any solution with no restriction on how the result is accomplished. The remedial actions of Example 47 provide a technical improvement, whereas the remedial actions of the present claims are directed to the abstract idea, and use the computer as a tool to implement the abstract idea.
Paragraphs [0051]-[0052] of the specification recite that “a message can be sent to an administrator indicating that the substitute approver has been determined and that a seeking- approval message is to be sent to the substitute approver seeking approval of the task in the automated process instance,” and that an “administrator may assign the approval to a substitute and may need to alter permissions as appropriate. The administrator may take steps such as communicating with plural substitute candidates to find an appropriate one,” which further puts the invention into the abstract idea, as a person is assigning approval and altering permissions.
Regarding Example 42, the specification of Example 42 provided a technical solution to a technical problem, along with the details of how it is accomplished. The conversion of non-standardized information to a standardized format in that context was providing a technical solution to a technical problem. The specification of Example 42 recites “During a visit, each medical provider records information about the patient’s condition in their own local patient records. These records are often stored locally on a computer in a non-standard format selected by whichever hardware or software platform is in use in the medical provider’s local office” and “records in separate locations are not timely or readily-shared or cannot be consolidated due to format inconsistencies,” and further states “To solve this problem, applicant has invented a network-based patient management method that collects, converts and consolidates patient information from various physicians and health-care providers into a standardized format.” The conversion of message content into a machine-usable graph / vector representation, along with the other steps in the present claims, does not provide a technical solution, but rather uses technology in a generic manner.
The specification recites an improvement to the abstract idea itself, rather than an improvement to technology. Paragraph [0026] of the specification recites that a machine learning approach can be implemented. Paragraph [0034] describes the machine learning model being trained with previous data. Paragraph [0035] describes using new data input to the trained model to predict substitute approvers. The machine learning model is recited as a generic machine learning model, trained with the data, and then given inputs and generating outputs, without any technical improvement to machine learning models. Regarding Applicant’s stated advantage of “saving storage by avoiding explicit rule storage,” the saving of storage is not a result of a technical improvement. For example, it is not because the invention is making databases more efficient at storing data. As stated, the saving of storage is the result of avoiding explicit rule storage; it is simply the result of storing less data. Regarding “increasing performance because processes are completed more quickly,” the increased performance is not a result of a technical improvement. It is the result of using technology to automate a manual process. Regarding “providing a single point of sharing across backend task providers,” the specification paragraph [0203] states that “because email can be used as a common area for sharing information, the system need not be duplicated across different backend task providers (e.g., finance, ERP, HR, and the like). Thus, a single point of sharing can be supported.” The providing of a single point of sharing is simply using email for sharing information, which is using email in its ordinary capacity.
Step 2B:
Regarding Applicant’s argument that “the model output is used to trigger concrete remedial control operations in the relevant technical environment, rather than merely being displayed or used as advice,” the operations described are determining whether the substitute approver has permissions to approve the task. If the substitute approver has permissions, then assigning the substitute approver to the task and sending the request to the substitute approver. If the substitute approver lacks permissions, then blocking execution of the automated process instance, which could simply be not sending a message to the substitute approver, since this is not described in the specification beyond stating that the process is blocked.
For at least all the reasons above, the claims do not recite additional elements that amount to significantly more than the abstract idea.
Conclusion
The prior art made of record and not relied upon, considered pertinent to applicant’s disclosure or directed to the state of art, is listed on the enclosed PTO-892.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KARMA A EL-CHANTI/Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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