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 .
DETAILED ACTION
1. This Office Action is in response to the Amendment filed on November 12, 2025, which paper has been placed of record in the file.
2. Claims 1-20 are pending in this application.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Regarding independent claim 1, which is analyzing as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a system for predicting scenarios for a structure of entities. Thus, the claim is to a machine, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim recites a system for predicting scenarios for a structure of entities. The claim recites the steps: receive a data set indicative of resource utilization of the structure of entities, receive criteria indicating a change in the data set, generate a scenario comprising impacts to a performance of the structure of entities, determine that the impacts are above a threshold impact for a first node of the nodes, and execute, responsive to the impacts being above the threshold impact, an action associated with the scenario for the structure of entities, the action comprising an update to the resource utilization to control an impact on the first node, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of fundamental economic principles or practices including hedging, insurance, mitigating risk. See MPEP 2106.04(a)(2), subsection III.
Moreover, the claim recites the steps: receive a data set indicative of resource utilization of the structure of entities, receive criteria indicating a change in the data set, generate a scenario comprising impacts to a performance of the structure of entities, determine that the impacts are above a threshold impact for a first node of the nodes, and execute, responsive to the impacts being above the threshold impact, an action associated with the scenario for the structure of entities, the action comprising an automatic update to the resource utilization to control an impact on the first node, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Accordingly, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of “receive, from a client device”, “receive, from a second client device” and “using the criteria and the data set as inputs to a machine learning model.” The claim also recites that the steps of “receive a data set indicative of resource utilization of the structure of entities, receive criteria indicating a change in the data set, generate a scenario comprising impacts to a performance of the structure of entities, determine that the impacts are above a threshold impact for a first node of the nodes, and execute, responsive to the impacts being above the threshold impact, an action associated with the scenario for the structure of entities to control an impact on the first node”, are performed by one or more processor coupled with memory.
The additional element “receive, from a client device” and “receive, from a second client device” are mere data gathering and transmitting, recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitation amounts to necessary data gathering and receiving. See MPEP 2106.05. Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, improvements to the client device, they just merely used as general means for gathering and transmitting data.
The additional element “using the criteria and the data set as inputs to a machine learning model” provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional element “using the criteria and the data set as inputs to a machine learning model” is used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, this limitation only recites the outcome of “generate a scenario comprising impacts to a performance of the structure of entities” and does not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional element “using the criteria and the data set as inputs to a machine learning model” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element ““using the criteria and the data set as inputs to a machine learning model” limits the identified judicial exceptions “generate a scenario comprising impacts to a performance of the structure of entities”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Further, the limitations “receive a data set indicative of resource utilization of the structure of entities, receive criteria indicating a change in the data set, generate a scenario comprising impacts to a performance of the structure of entities, determine that the impacts are above a threshold impact for a first node of the nodes, and execute, responsive to the impacts being above the threshold impact, an action associated with the scenario for the structure of entities to control an impact on the first node”, are recited as being performed by the processors.” The processors are recited at a high level of generality. In limitations “receive a data set indicative of resource utilization of the structure of entities, receive criteria indicating a change in the data set”, the processors are used as a tool to perform the generic computer function of gathering and transmitting data. See MPEP 2106.05(f). In limitations “generate a scenario comprising impacts to a performance of the structure of entities, determine that the impacts are above a threshold impact for a first node of the nodes, and execute, responsive to the impacts being above the threshold impact, an action associated with the scenario for the structure of entities to control an impact on the first node”, the processors are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the processors, memory, and software component, that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as 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 -- See MPEP 2106.05(f).
Thus, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A, Prong One: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, Prong Two, the additional element of ““using the criteria and the data set as inputs to a machine learning model” is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
The additional element “receive, from a client device” and “receive, from a second client device”, were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data transmitting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the additional element of receive, from a client device” and “receive, from a second client device” are recited at a high level of generality. This element amounts to gathering and transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
As discussed in Step 2A, Prong Two above, the recitation of the one or more processors to perform limitations “receive a data set indicative of resource utilization of the structure of entities, receive criteria indicating a change in the data set, generate a scenario comprising impacts to a performance of the structure of entities, determine that the impacts are above a threshold impact for a first node of the nodes, and execute, responsive to the impacts being above the threshold impact, an action associated with the scenario for the structure of entities to control an impact on the first node”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claims 13 and 19, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 12 directed to a system, independent claim 18 directed to a medium, are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1.
Regarding dependent claims 2-12, 14-18, and 20, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea.
Regarding dependent claims 2, 14, and 20, the claims recite execute a second action associated with the second scenario for the structure of entities, that fall under the category of Organizing Human Activity groupings of abstract ideas as described above in the independent claim 1. The claims recite the additional element receive, from the first node, responsive to presenting the scenario to the first node, additional criteria from the client devices associated with the first node, which are mere data gathering and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and transmitting. See MPEP 2106.05 (See claim 1 above). The claims recite the additional element generate a second scenario by providing the criteria, the data set, and the additional criteria as inputs to the machine learning model, which are used to generally apply the abstract idea without placing any limits on how the machine learning model function. Rather, these limitations only recite the outcome of “generate a second scenario” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (See claim 1 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 3 and 15, the claims recite determine that the one or more impacts are above the threshold impact for the first node for each scenario of the first plurality of scenarios, that fall under the category of Mental process grouping of abstract ideas as described above in the independent claim 1. The claims recite the additional element generate the scenario comprising a first plurality of scenarios by providing the criteria and the data set as inputs to the machine learning model, which are used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome of “generate the scenario” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (See claim 1 above). The claims recite the additional element present each scenario of the first plurality of scenarios that are above the threshold impact to the first node via the client devices associated with the first node, which are mere data gathering and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05 (See claim 1 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 4 and 16, the claims recite the additional element generate a plurality of second scenarios, responsive to presenting the scenario to the first node, by providing the criteria, the data set, and additional criteria from the first node as inputs to the machine learning model, which are used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome of “generate a plurality of second scenarios” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (See claim 1 above). The claims recite the additional element receive, from the first entity, a selection of the scenario from the plurality of second scenarios., which are mere data gathering and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and transmitting. See MPEP 2106.05 (See claim 1 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 5 and 17, the claims recite determine a corresponding node of the nodes for each scenario of the plurality of scenarios with the one or more impacts above the threshold impact, and execute the action associated with the scenario for the structure of entities based on the selected scenario, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1. The claims recite the additional element generate a plurality of scenarios comprising the one or more impacts for each scenario of the plurality of scenarios by providing the criteria and the data set as inputs to the machine learning model, which are used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome of “generate a plurality of scenarios” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (See claim 1 above). The claims recite the additional elements present each scenario of the plurality of scenarios to its corresponding node via the client devices associated with each corresponding node; receive, from the client devices associated with each corresponding node, a selection of the scenario from the plurality of scenarios, which are mere data gathering, outputting, and transmitting, recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering outputting, and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering outputting, and transmitting. See MPEP 2106.05 (See claim 1 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 6, the claim recites identify a subset of the data set based on the criteria, that fall under the category of Mental process grouping of abstract ideas as described above in the independent claim 1. The claim recites the additional elements provide the subset of the data set as input to the machine learning model to determine the one or more impacts, which are used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome of “determine the one or more impacts” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (See claim 1 above). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 7 and 18, the claims recite the additional elements present a confirmation of the action to the first entity; and execute the action responsive to receiving an indication confirming execution of the action from the first entity, which are mere data gathering, outputting, and transmitting, recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering outputting, and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering outputting, and transmitting. See MPEP 2106.05 (See claim 1 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 8, the claim simply refines the abstract idea by further reciting wherein the structure of entities comprises entities related to one or more of: time entry, payroll, human resources, compensation, benefits, real estate, or finance, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 9, the claim simply refines the abstract idea by further reciting to select the action from a list of predetermined actions associated with the one or more impacts, that fall under the category of Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 10, the claim simply refines the abstract idea by further reciting the criteria to include a first change to the one or more parameters, that fall under the category of Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 11, the claim simply refines the abstract idea by further reciting wherein the first node includes the first entity, that fall under the category of Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 12, the claim recites the additional elements determine a suggestion of the criteria using historical data associated with the first entity as input to a second machine learning model using natural language learning, which are used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome of “determine the one or more impacts” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (See claim 1 above). The claim recites the additional elements present, via the client device associated with the first entity, the suggestion of the criteria; and receive a selection of the criteria from the suggestion of the criteria, which are mere data gathering, outputting, and transmitting, recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering outputting, and transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering outputting, and transmitting. See MPEP 2106.05 (See claim 1 above). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Accordingly, claims 1-20 are not draw to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
Claim Rejections - 35 USC § 102
5. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
6. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mohanty et al. (hereinafter Mohanty, US 2023/0061264).
Regarding to claim 1, Mohanty discloses a system, comprising:
one or more processors coupled with memory to (figure 3 and para [0051], the computing hardware 303 may include one or more processors 307, one or more memories 308):
receive, from a client device associated with a structure of entities, a data set indicative of resource utilization of the structure of entities, wherein the structure of entities comprises nodes, wherein a first node of the nodes defines a relationship between a first entity of the structure of entities and related entities of the structure of entities, the relationship indicating impacts to a performance of the structure of entities based on changes in the resource utilization using one or more parameters correlated with the data set (para [0015], the process composer system may receive scenario data identifying a scenario for enterprise resource planning that includes a base solution and processes. For example, a user may provide data identifying processes (e.g., business processes, manufacturing processes, finance processes, and/or the like) and a base solution for enterprise resource planning to the user device. The data identifying the processes and the base solution may constitute the scenario data… the process composer system may receive, from the user device, scenario data identifying a plurality of scenarios for enterprise resource planning; para [0017], The process composer system may generate the heatmap user interface based on the identified scenario, base solution, processes, other scenarios, and/or the like. The heatmap user interface may display scenarios associated with the base solution (e.g., a base industry) first, followed by scenarios of other industries. For example, the heatmap user interface may display the scenario first since the scenario is associated with the base solution. The process composer system may provide the heatmap user interface for display to the user device, and the user device may display the heatmap user interface to the user. In this way, the user may easily identify which scenarios are associated with which industries);
receive criteria indicating a change in the data set from a second client device associated with the first entity of the structure of entities (para [0042], As shown by reference number 230, the machine learning system may apply the trained machine learning model 225 to a new observation, such as by receiving a new observation and inputting the new observation to the trained machine learning model 225. As shown, the new observation may include a first feature of configurations X, a second feature of test scripts Y, a third feature of master data Z, and so on, as an example);
generate, using the criteria and the data set as inputs to a machine learning model, a scenario comprising the impacts to the performance of the structure of entities (para [0013], the process composer system utilizes a machine learning model to identify a risk severity for an enterprise resource planning scenario. The process composer system may generate an output file that includes a list of scenarios (e.g., enterprise resource planning scenarios) selected by a user, and may identify configurations and test scripts for the scenarios; para [0024], the machine learning model includes a classification machine learning model. When processing the configurations, the test scripts, and the master data, with the machine learning model, to predict the risk severity associated with the scenario, the process composer system (e.g., via the machine learning model) may execute the configurations and the test scripts with the master data to generate execution results and may predict the risk severity associated with the scenario based on the execution results. The risk severity may include a complexity associated with the scenario, an impact analysis (e.g., dependencies) associated with the scenario, and/or the like);
determine that one or more of the impacts are above a threshold impact for the first node of the nodes (para [0046], the recommendation and/or the automated action associated with the new observation may be based on a target variable value having a particular label (e.g., classification, categorization, and/or the like), may be based on whether a target variable value satisfies one or more thresholds (e.g., whether the target variable value is greater than a threshold, is less than a threshold, is equal to a threshold, falls within a range of threshold values, and/or the like), may be based on a cluster in which the new observation is classified, and/or the like); and
execute, responsive to the one or more impacts being above the threshold impact, an action associated with the scenario for the structure of entities, the action comprising an automatic update to the resource utilization to control an impact on the first node (para [0026], the process composer system may perform one or more actions based on the risk severity associated with the scenario… For example, the chief information security officer may present the risk severity associated with the scenario to executives of the entity so that the executives may be convinced to allocate resources for the scenario associated with the risk severity. In this way, the process composer system may conserve computing resources, networking resources, and/or the like that would otherwise have been consumed in generating defective or inoperable enterprise resource planning systems, utilizing the defective or inoperable enterprise resource planning systems, correcting the defective or inoperable enterprise resource planning systems, and/or the like; para [0027], the one or more actions include the process composer system causing a new scenario to be selected based on the risk severity associated with the scenario. For example, the process composer system may determine that the risk severity fails to satisfy a threshold for the scenario to not be at risk of being defective or rendered inoperable. The process composer system may determine a new scenario so that the risk severity associated with the new scenario satisfies the threshold. The process composer system may cause the new scenario to be implemented (e.g., generating a consolidated enterprise resource system). In this way, the process composer system may conserve computing resources, networking resources, and/or the like that would otherwise have been consumed in generating defective or inoperable enterprise resource planning systems, utilizing the defective or inoperable enterprise resource planning systems, correcting the defective or inoperable enterprise resource planning systems after implementation, and/or the like; para [0028], the one or more actions include the process composer system causing the scenario to be modified based on the risk severity associated with the scenario. For example, the process composer system may determine that the risk severity fails to satisfy the threshold for the scenario to not be at risk of being defective or rendered inoperable. The process composer system may determine one or more features associated with the scenario that may be modified so that the risk severity associated with the modified scenario satisfies the threshold. The process composer system may cause the one or more features of the scenario to be modified so that the scenario may be successfully implemented. In this way, the process composer system may conserve computing resources, networking resources, and/or the like that would otherwise have been consumed in generating defective or inoperable enterprise resource planning systems, utilizing the defective or inoperable enterprise resource planning systems, correcting the defective or inoperable enterprise resource planning systems after implementation, and/or the like).
Regarding to claim 2, Mohanty discloses the system of claim 1, comprising the one or more processors to:
receive, from the first node, responsive to presenting the scenario to the first node, additional criteria from the client devices associated with the first node (para [0017], the process composer system may identify and provide for display the scenario, the base solution, and the processes via a heatmap user interface and based on the output file. For example, the process composer system may read (e.g., via an XML reader) the output file and may identify the scenario, the base solution, and the processes based on reading the output file. In some implementations, the process composer system may identify the other scenarios selected by the user via the user device);
generate a second scenario by providing the criteria, the data set, and the additional criteria as inputs to the machine learning model (para [0027], the one or more actions include the process composer system causing a new scenario to be selected based on the risk severity associated with the scenario. For example, the process composer system may determine that the risk severity fails to satisfy a threshold for the scenario to not be at risk of being defective or rendered inoperable). and
execute a second action associated with the second scenario for the structure of entities (para [0028], the one or more actions include the process composer system causing the scenario to be modified based on the risk severity associated with the scenario. For example, the process composer system may determine that the risk severity fails to satisfy the threshold for the scenario to not be at risk of being defective or rendered inoperable).
Regarding to claim 3, Mohanty discloses the system of claim 1, comprising the one or more processors to:
generate the scenario comprising a first plurality of scenarios by providing the criteria and the data set as inputs to the machine learning model (para [0013], the process composer system utilizes a machine learning model to identify a risk severity for an enterprise resource planning scenario. The process composer system may generate an output file that includes a list of scenarios (e.g., enterprise resource planning scenarios) selected by a user);
determine that the one or more impacts are above the threshold impact for the first node for each scenario of the first plurality of scenarios (para [0017], the process composer system may generate the heatmap user interface based on the identified scenario, base solution, processes, other scenarios, and/or the like. The heatmap user interface may display scenarios associated with the base solution (e.g., a base industry) first, followed by scenarios of other industries); and
present each scenario of the first plurality of scenarios that are above the threshold impact (para [0018], the process composer system may generate a hierarchy for the scenario based on the output file and may identify prerequisites for the scenario based on the output file. For example, the process composer system may generate a hierarchy for the scenario based on mobile solutions, addons, business functions, and/or the like associated with the scenario).
Regarding to claim 4, Mohanty discloses the system of claim 1, comprising the one or more processors to:
generate a plurality of second scenarios, responsive to presenting the scenario to the first node, by providing the criteria, the data set, and additional criteria from the first node as inputs to the machine learning model (para [0032], the process composer system utilizes a machine learning model to identify a risk severity for an enterprise resource planning scenario); and
receive, from the first entity, a selection of the scenario from the plurality of second scenarios (para [0032], the process composer system may generate an output file that includes a list of scenarios (e.g., enterprise resource planning scenarios) selected by a user);
Regarding to claim 5, Mohanty discloses the system of claim 1, comprising the one or more processors to:
generate a plurality of scenarios comprising the one or more impacts for each scenario of the plurality of scenarios by providing the criteria and the data set as inputs to the machine learning model (para [0012], a process composer system that utilizes a machine learning model to identify a risk severity for an enterprise resource planning scenario. For example, the process composer system may receive scenario data identifying a scenario for enterprise resource planning that includes a base solution and processes, and may generate an output file based on the scenario data);
determine a corresponding node of the nodes for each scenario of the plurality of scenarios with the one or more impacts above the threshold impact (para [0028], the one or more actions include the process composer system causing the scenario to be modified based on the risk severity associated with the scenario. For example, the process composer system may determine that the risk severity fails to satisfy the threshold for the scenario to not be at risk of being defective or rendered inoperable);
present each scenario of the plurality of scenarios to its corresponding node via the client devices associated with each corresponding node (para [0025], If the machine learning model processes multiple scenarios, the machine learning model may provide one or more recommended scenarios from the multiple scenarios based on risk severities determined for the multiple scenarios);
receive, from the client devices associated with each corresponding node, a selection of the scenario from the plurality of scenarios (para [0025], The user of the user device may accept or reject one or more of the recommended scenarios. If the user accepts one or more of the recommended scenarios, the process composer system may add corresponding test scripts for testing the one or more of the recommended scenarios); and
execute the action associated with the scenario for the structure of entities based on the selected scenario (para [0026], the process composer system may perform one or more actions based on the risk severity associated with the scenario. In some implementations, the one or more actions include the process composer system providing the risk severity associated with the scenario for display).
Regarding to claim 6, Mohanty discloses the system of claim 1, comprising the one or more processors to:
identify a subset of the data set based on the criteria (para [0036], the set of observations includes a feature set. The feature set may include a set of variables, and a variable may be referred to as a feature. A specific observation may include a set of variable values (or feature values) corresponding to the set of variables); and
provide the subset of the data set as input to the machine learning model to determine the one or more impacts (para [0036], the machine learning system may determine variables for a set of observations and/or variable values for a specific observation based on input received from the process composer system. For example, the machine learning system may identify a feature set (e.g., one or more features and/or feature values) by extracting the feature set from structured data, by performing natural language processing to extract the feature set from unstructured data, by receiving input from an operator, and/or the like).
Regarding to claim 7, Mohanty discloses the system of claim 1, comprising the one or more processors to:
present a confirmation of the action to the first entity (para [0026], For example, the process composer system may provide the risk severity associated with the scenario for display to an operator of the process composer system, to a chief information officer of the entity, to information operators of the entity, and/or the like. Such parties may utilize the risk severity associated with the scenario for different purposes); and
execute the action responsive to receiving an indication confirming execution of the action from the first entity (para [0026], For example, the chief information security officer may present the risk severity associated with the scenario to executives of the entity so that the executives may be convinced to allocate resources for the scenario associated with the risk severity).
Regarding to claim 8, Mohanty discloses the system of claim 1, wherein the structure of entities comprises entities related to one or more of: time entry, payroll, human resources, compensation, benefits, real estate, or finance (para [0011], The set of integrated applications enables an entity (e.g., a business, an organization, and/or the like) to collect, store, manage, and interpret data from many entity activities).
Regarding to claim 9, Mohanty discloses the system of claim 1, comprising the one or more processors to select the action from a list of predetermined actions associated with the one or more impacts (para [0071], process 500 may include performing one or more actions based on the risk severity associated with the scenario (block 580). For example, the device may perform one or more actions based on the risk severity associated with the scenario, as described above. In some implementations, performing the one or more actions based on the risk severity associated with the scenario, includes one or more of providing the risk severity associated with the scenario for display, causing a new scenario to be selected based on the risk severity associated with the scenario, or causing the scenario to be modified based on the risk severity associated with the scenario).
Regarding to claim 10, Mohanty discloses the system of claim 1, comprising the criteria to include a first change to the one or more parameters (para [0042], the machine learning system may apply the trained machine learning model 225 to a new observation, such as by receiving a new observation and inputting the new observation to the trained machine learning model 225. As shown, the new observation may include a first feature of configurations X, a second feature of test scripts Y, a third feature of master data Z, and so on, as an example).
Regarding to claim 11, Mohanty discloses the system of claim 1, wherein the first node includes the first entity (para [0026], For example, the process composer system may provide the risk severity associated with the scenario for display to an operator of the process composer system, to a chief information officer of the entity, to information operators of the entity, and/or the like).
Regarding to claim 12, Mohanty discloses the system of claim 1, comprising the one or more processors to:
determine a suggestion of the criteria using historical data associated with the first entity as input to a second machine learning model using natural language learning (para [0035], a machine learning model may be trained using a set of observations. The set of observations may be obtained from historical data, such as data gathered during one or more processes described herein. In some implementations, the machine learning system may receive the set of observations (e.g., as input) from the process composer system; para [0036], the machine learning system may identify a feature set (e.g., one or more features and/or feature values) by extracting the feature set from structured data, by performing natural language processing to extract the feature set from unstructured data, by receiving input from an operator, and/or the like);
present, via the client device associated with the first entity, the suggestion of the criteria (para [0015], the process composer system may provide option data identifying processes and base solutions across different industries to the user device, and the user device may provide the option data for display to the user); and
receive a selection of the criteria from the suggestion of the criteria (para [0015], the user may select one or more processes and/or base solutions from the option data, and the user device may provide data identifying the selected one or more processes and/or base solutions to the process composer system).
Claims 13-18 are written in method and contain the same limitations as described in claims 1-5 and 7 above, therefore are rejected by the same rationale.
Claims 19-20 are written in non-transitory computer-readable medium and contain the same limitations as described in claims 1-2 above, therefore are rejected by the same rationale.
Response to Arguments/Amendment
7. Applicant's arguments with respect to claims 1-20 have been fully considered but are not persuasive.
I. Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
In response to the Applicant’s argument that the pending claims are not directed towards a method of organizing activity such as fundamental economic principle or practice, the Examiner respectfully disagrees and submits that the claims recite a system and method for predicting scenarios for a structure of entities. The Specification described in para [0003] that the system facilitates predicting one or more scenarios for a structure of entities using a collaborative process among client devices associated with the structure of entities. The predicted scenarios identify one or more nodes within a structure of entities that are impacted by a change in resource utilization. The system facilitates determining one or more scenarios that indicate the amount of impact a change in resource utilization can have on a structure of entities or nodes therein, determining the impact scenarios using a data set and criteria received from a client device, and analyzing the impact scenarios to determine what aspects of the structure of entities might be affected. The claim recites the steps: receive a data set indicative of resource utilization of the structure of entities, receive criteria indicating a change in the data set, generate a scenario comprising impacts to a performance of the structure of entities, determine that the impacts are above a threshold impact for a first node of the nodes, and execute, responsive to the impacts being above the threshold impact, an action associated with the scenario for the structure of entities, the action comprising an update to the resource utilization to control an impact on the first node, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of fundamental economic principles or practices including hedging, insurance, mitigating risk (determining the impact scenarios and analyzing the impact scenarios to determine what aspects of the structure of entities might be affected).
See MPEP 2106.04(a)(2), subsection III.
In response to the Applicant’s argument that the pending claims are not directed to a “mental process”, as the claims include subject matter that “cannot practically be performed in the human mind, the Examiner respectfully disagrees and submits that the claims recite the steps: receive a data set indicative of resource utilization of the structure of entities, receive criteria indicating a change in the data set, generate a scenario comprising impacts to a performance of the structure of entities, determine that the impacts are above a threshold impact for a first node of the nodes, and execute, responsive to the impacts being above the threshold impact, an action associated with the scenario for the structure of entities, the action comprising an automatic update to the resource utilization to control an impact on the first node, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III. Accordingly, the claims recite an abstract idea.
The step of “using the criteria and the data set as inputs to a machine learning model” is additional element and is analyzing in Step 2A-Prong 2. This additional element is used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, this limitation only recites the outcome of “generate a scenario comprising impacts to a performance of the structure of entities” and does not include any details about how the solution is accomplished. See MPEP 2106.05(f). The additional element “using the criteria and the data set as inputs to a machine learning model” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element ““using the criteria and the data set as inputs to a machine learning model” limits the identified judicial exceptions “generate a scenario comprising impacts to a performance of the structure of entities”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
In response to the Applicant’s argument that the claimed technology is directed the practical application of providing an improved computing architecture for heterogenous data modeling and automatic action execution, the Examiner respectfully disagrees and submits that the claimed features and the additional elements do not effect an improvement in the functioning of the processor, memory, machine learning model, or other technology, do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing. Accordingly, the claims are not integrated into a practical application.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claims are not patent eligible.
Accordingly, the 101 rejection is maintained.
II. Claim Rejections - 35 USC § 102
In response to the Applicant’s argument that Mohanty does not disclose “wherein a first node of the nodes defines a relationship between a first entity of the structure of entities and related entities of the structure of entities, the relationship indicating impacts to a performance of the structure of entities based on changes in the resource utilization using one or more parameters correlated with the data set”, the Examiner respectfully disagrees and submits that Mohanty discloses in para [0017], “The process composer system may generate the heatmap user interface based on the identified scenario, base solution, processes, other scenarios, and/or the like. The heatmap user interface may display scenarios associated with the base solution (e.g., a base industry) first, followed by scenarios of other industries. For example, the heatmap user interface may display the scenario first since the scenario is associated with the base solution. The process composer system may provide the heatmap user interface for display to the user device, and the user device may display the heatmap user interface to the user. In this way, the user may easily identify which scenarios are associated with which industries”, thus Mohanty’s system generating a heatmap user interface (displaying the relationship) that includes data identifying the scenario associated with the base industry (the structure) and the scenario associated with other industries (the nodes). Therefore, Mohanty does disclose “wherein a first node of the nodes defines a relationship between a first entity of the structure of entities and related entities of the structure of entities, the relationship indicating impacts to a performance of the structure of entities based on changes in the resource utilization using one or more parameters correlated with the data set, as claimed.
Accordingly, the 102 rejection is maintained.
Conclusion
8. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 date of this final action.
9. Claims 1-20 are rejected.
10. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Zhang et al. (US 2025/0217442) disclose the method includes generating an entity data object for each of the plurality of entities and applying a machine-learning model to the entity data objects generated for the plurality of entities. The method further includes determining a prediction indicator for each entity of the plurality of entities, generating a re-utilization offset data object for each of the plurality of entities, and causing the re-utilization offset data object for each entity to be displayed on a Graphical User Interface (GUI).
Goel et al. (US 2022/031) disclose provide methods and systems for determining prospective acquisitions among business entities using machine learning techniques.
Patil et al. (US 11,321,366) disclose systems and methods for machine learning models for entity resolution.
Chang et al. (US 11,030,561) disclose techniques for scenario planning.
Lehr (US 2021/0065049) discloses methods and systems for providing data that can be used for prediction and automation of process execution based on machine learning.
Gribelyuk et al. (US 10,754,946) disclose systems and methods are provided for implementing a machine learning approach to modeling entity behavior.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 March 4, 2026