DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office Action for Application Serial Number: 18/051,731, filed on November 01, 2022. In response to Examiner’s Non-Final Rejection dated December 02, 2025, Applicant on March 02, 2026, amended claims 1, 8 and 15. Claims 1-17 and 21-23 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
Response to Arguments
Applicant's Arguments/Remarks filed March 02, 2026 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks will be addressed herein below in the order in which they appear in the response filed March 02, 2026.
Regarding the 35 U.S.C. 101 rejection, Applicant states the Office Action does not contain any prior art rejections, indicating that the claims are allowable over the prior art. In Ex parte Desjardins, the Appeals Review Panel stated that the "the traditional and appropriate tools to limit patent protection to its proper scope" are 35 U.S.C. §§ 102, 103, & 112, and that these statutory provisions should be the focus of examination. Ex parte Desjardins, p. 10. For at least the reasons that the claims are allowable over the prior art and in view of Ex parte Desjardins, Applicant respectfully request reconsideration of the Office Action's rejection under 35 U.S.C. § 101.
In response, Examiner respectfully disagrees. Applicant’s citation from the Analysis Section of the abovementioned proceedings is directly related to the claims at issue in that respective case. Although the New Ground of Rejection under 35 U.S.C. § 101 was overturned, the Board did not disturb the standing rejections under 35 U.S.C. § 103. Applicant is respectfully reminded novelty and non-obviousness over the prior art, have no bearing on whether a claim recites or is directed to an abstract idea. The Federal Circuit has made this clear - rejecting an argument substantially similar to Applicants’ in Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) ("We do not agree . . . that the addition of merely novel or non-routine components to the claimed idea necessarily turns an abstraction into something concrete.").
Regarding the 35 U.S.C. 101 rejection, Applicant argues Ex Parte Desjardins (see p. 10-11, Applicant Remarks) and submits that the Office Action's approach here mirrors the approach criticized in Ex Parte Desjardins. The Office Action has characterized the trained machine-learning model as "solely used as a tool to perform the instructions of the abstract idea" and dismissed the continuous improvement operations and other additional elements as generic computer components. See Office Action, pages 7-11.
Applicant further submits that the claims recite an improvement to how the machine- learning model itself operates, not merely applying the machine learning model as a tool. In Ex Parte Desjardins, the Appeal Review Panel found that claims reciting parameter adjustment to "optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task" constituted "an improvement to how the machine learning model itself operates." Ex Parte Desjardins, p. 9. Similarly, Claim 1 recites continuous improvement operations that record assessments and effectiveness of recommendations and identify key contributors to both positive and negative outcomes, where the continuous improvement operations are configured to adjust significance level of a parameter for the trained machine-learning model or identify one or more additional parameters for the trained machine-learning model.
Applicant submits that the claims do not merely use machine learning as a tool. Unlike Example 47, Claim 2, which merely used machine learning to detect and analyze anomalies without any remedial action, the present claims use the machine learning output to automatically initiate specific corrective actions and continuously improve the machine learning model by recording assessments and effectiveness of recommendations, and adjusting significance level of parameters for the machine learning model or identify additional parameters for the machine learning model. The machine learning model is not merely applied to perform an abstract idea; rather, the machine learning model output drives automated corrective actions and the machine learning model itself is continuously improved through operations that identify key contributors to positive and negative outcomes-an improvement to how the machine-learning model itself operates, as recognized in Ex Parte Desjardins.
In response, Examiner respectfully disagrees. Examiner finds Applicants arguments in relation to this matter are not persuasive. Specifically, in Ex Parte Desjardins, the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”, and the claims reflect the improvement identified in the specification. The improvements identified in the Ex Parte Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. Examiner finds no similar improvements to take into consideration here. Examiner maintains the claims are directed to an abstract idea of using a machine learning model to assess worker performance and implementing corrective actions. Examiner finds the continuous improvement operations that record assessments and effectiveness of recommendations and identify key contributors to both positive and negative outcomes, where the continuous improvement operations are configured to adjust significance level of a parameter or identify additional parameters for the trained machine-learning model is an improvement to an existing business process (e.g., performance assessments) and not an improvement to the functioning of a machine learning technology, a computer-related technology or any technological field.
7. Regarding the 35 U.S.C. 101 rejection, Applicant argues Example 47 (see p. 11, Applicant Remarks) and submits that the amended claims are analogous to eligible Claim 3 of the USPTO Subject Matter Eligibility Example 47, not ineligible Claim 2. In Example 47, Claim 2 was found ineligible because it merely used a trained ANN to detect and analyze anomalies and output anomaly data, without any remedial action based on the ML output. In contrast, Claim 3 was found eligible because it included specific automated remedial actions that used the machine learning (ANN) output to provide specific computer solutions.
Similarly, Claim 1 as amended recites a closed-loop feedback system that: (1) detects performance changes based on sensing information received from sensor devices; (2) automatically initiates corrective actions by transmitting a task reassignment instruction to the worker computing device or reallocating the worker to a different task; (3) continuously improves the trained machine-learning model through operations that record assessments and effectiveness of recommendations and identify key contributors to positive and negative outcomes; and (4) dynamically updates the real-time dashboard and triggers alerts to the worker computing device. At least the "automatically initiating... the at least one corrective action ... by transmitting a task reassignment instruction to the worker computing device or reallocating the worker to a different task" parallels the automated remedial actions in Example 47, Claim 3 that were found to integrate the abstract idea into a practical application..
In response Examiner respectfully disagrees. Examiner maintains the machine learning model merely confines the use of the abstract idea to a particular technological environment (i.e., machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Examiner finds automatically initiated corrective action of transmitting a task reassignment or reallocation to a worker is merely instructions to implementing the abstract idea using generic computer components. The corrective actions are directed to the management of tasks to workers and provides no improvement to the functioning of a computer, computer technology or technological field, does not apply the abstract idea with or by use of a particular machine or apply or use the abstract idea in a meaningful way beyond generally linking the use of the abstract idea to a particular environment. The general use of a machine learning technique does not provide meaningful limitations to transform the abstract idea into a practical application. Examiner maintains using machine learning models to determine worker and target scores and instructions to reassign or reallocate tasks relative to workers, is considered a method of organizing human activity involving managing personal behavior, an improvement to an existing business process and not an improvement to the functioning of a machine learning technology, computer-related technology or any technological field. Applicant has not identified any limitations in the claimed invention that show or submit that the technology used is being improved or there was a problem in or with the technology that the claimed invention solves. Examiner maintains the claims are directed to an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states under MPEP 2106.04(d), "[a] claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." The claims as amended impose meaningful limits by requiring specific sub-score calculations (time on task, time between tasks); real-time sensor integration; automatic initiation of corrective actions (task reassignment or worker reallocation); continuous improvement operations that record assessments and effectiveness of recommendations and identify key contributors to positive and negative outcomes; and dynamic dashboard updates with triggered alerts. This is not a generic sequence of data processing but a specific technical architecture for real-time worker performance monitoring with automated remediation.
In response, Examiner respectfully disagrees. Examiner notes Diamond v. Diehr is an example that recites meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Specifically, the claim is directed to the use of the Arrhenius equation in an automated process for operating a rubber‐molding press. The court found the claim recites meaningful limitations along with the judicial exception including installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, constantly recalculating the cure time and opening the press at the proper time. These limitations sufficiently limit the claim to the practical application of molding rubber products and are clearly not an attempt to patent the mathematical equation and thus recite improvements to the technology. Examiner finds there are no similar technology, technological problem or solution here. Examiner finds sub-score calculations (time on task, time between tasks); real-time sensor integration; automatic initiation of corrective actions (task reassignment or worker reallocation); continuous improvement operations that record assessments and effectiveness of recommendations and identify key contributors to positive and negative outcomes; and dynamic dashboard updates with triggered alerts to perform real-time worker performance monitoring with automated remediation improves the business process rather than the technology being used. Examiner maintains the claims recite addition elements used as tools to perform the instructions of the abstract idea without disclosing limitations that integrates the abstract idea into a practical application, nor do these elements provide meaningful limitations that transforms the judicial exception into significantly more than the abstract idea itself. Examiner maintains the claims are directed to an abstract idea.
Additionally, Examiner respectfully reminds Applicant, although preemption is considered, the two-part analysis is used to determine patent eligibility. Preemption concerns are, thus fully addressed and rendered moot where a claim is determined to disclose patent ineligible subject matter under the two-part framework. While preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016). It is necessary to evaluate eligibility using the Alice/Mayo test, because while a preemptive claim may be ineligible, the absence of complete preemption does not demonstrate that a claim is eligible. Diamond v. Diehr, 450 U.S. 175, 191-92 n.14, 209 USPQ 1, 10-11 n.14 (1981) ("We rejected in Flook the argument that because all possible uses of the mathematical formula were not pyre-emptied, the claim should be eligible for patent protection"). For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-7 and 21-23 are directed towards a method, claims 8-14 are directed towards a system and claims 15-17 are directed towards a non-transitory computer-readable medium, which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-17 and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite using a machine learning model to assess worker performance and implementing corrective actions.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity. Specifically, determining a worker score for the worker based on the value of the at least one worker performance metric and the value of the at least one worker performance parameter, wherein determining the worker score for the worker comprises: determining a first sub-score indicative of a time associated with performance of a task in a plurality of tasks by the worker; and determining a second sub-score indicative of a time between performance of a second task by the worker and a third task by the worker; and determining the worker score based on a combination of the first sub-score and the second sub-score; determining a target score for the worker based on the value of the at least one worker performance metric and the value of the at least one worker performance parameter; comparing whether the worker score determined for the worker is less than, equal to, or greater than the target score determined for the worker; upon comparison that the worker score is less than the target score, predicting at least one potential schedule delay based on the worker score; automatically initiating at least one corrective action based on the potential schedule delay by reallocating the worker to a different task; detecting based on sensing information, a performance change for the worker; upon determining that the worker score is equal to or greater than the target score subsequent to the detected performance change, determining an instruction to perform a second action relative to the worker, and wherein the one or more alerts comprise one or more recommendations to improve productivity of the worker constitutes methods based on managing personal behavior. The recitation of a processor, sensor devices, worker computing device, trained machine learning model, and dashboard does not take the claim out of the certain methods of organizing human activity grouping. Thus, the claim recites an abstract idea. Claims 8 and 15 also recite certain method of organizing human activity for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites receiving, using at least one processor, a value of at least one worker performance metric for a worker, wherein the at least one worker performance metric is received from one or more sensor devices connected over a network and a plurality of worker computing devices, and wherein each worker is associated with each worker computing device of the plurality of worker computing devices; receiving, using the at least one processor, a value of at least one worker performance parameter for the worker from the worker computing device; transmitting a task reassignment instruction to the worker computer device; and outputting to a real-time dashboard of a display the instruction to perform the second action, wherein the real-time dashboard is dynamically updated based on the performance change for the worker, wherein the real-time dashboard is configured to trigger one or more alerts to the worker computing device associated with the worker based on the performance change, and wherein the one or more alerts comprise one or more recommendations to improve productivity of the worker, which are limitations considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 1 recites a processor, sensor device and dashboard at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claim 1 recites the use of a machine learning model to determine worker and target scores and determining an instruction to perform a second action relative to the worker based on the trained machine-learning model, wherein one or more parameters of the trained machine-learning model is updated based at least in part on the worker score and through one or more continuous improvement operations configured to adjust significance level of a parameter for the trained machine-learning model or identify one or more additional parameters for the trained machine-learning model, wherein the one or more continuous improvement operations comprises at least recording assessments and effectiveness of recommendations, and identifying key contributors to both positive and negative outcomes. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning model disclosed in the claim is solely used as a tool to perform the instructions of the abstract idea. Thus, the additional element do not integrate the abstract idea into practical application because it does not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The system comprising processors configured toto access memory and execute processor-readable instructions recited in claim 8 and non-transitory computer-readable medium containing executable instructions in claim 15 also amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Thus, the additional elements recited in claims 8 and 15 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including the dashboard, processor(s), memory, machine-learning model, sensor device and non-transitory computer-readable medium amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, 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); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). (see at least Specification [54]; [101]-[102]. The machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [53]-[54]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 3, 10, 17 and 22 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 6 and 13 recite sensor devices connected over a network, and worker computing devices, claims 7 and 14 recite connected warehouse service systems, performance management systems, labor management system and gateway device and claim 21 recites an application programming interface (API) and an external system at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claims 6 and 13 recites the use of a trained machine learning model to aggregate and analyze data and claims 7 and 14 recite the use of the trained machine-learning model to determine the plurality of worker performance metrics and claim 23 recites the trained machine-learning model being configured to perform a convolution operation. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning model disclosed in the claim is solely used as a tool to perform the instructions of the abstract idea. Additionally, claims 2, 4, 5, 9, 11, 12, 16 and 21 recite steps that further narrow the abstract idea. Therefore claims 2-7, 9-14, 16, 17 and 21-23 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xu et al. (US 20200401970 A1) – Based at least in part on current demand and current roles of workers associated with an organization, the electronic device computes a need for a first set of workers capable of performing a type of task. Then, based at least in part on locations of a second set of workers, current roles of the second set of workers, and additional qualifications of the second set of workers, the electronic device determines dynamic reassignments to the type of task for the second set of workers. Note that the type of task is different from sets of tasks that define the current roles. Next, the electronic device provides assignment information that proposes the dynamic reassignment to the type of task for the second set of workers, and receives opt-in messages from at least a subset of the second set of workers confirming acceptance of the dynamic reassignment.
Wineberg et al. (US 6310951 B1) – A process for reassignment of agents based on their performance in the last performance interval; if the performance of an agent is superior, then he/she is moved to a higher campaign. "Higher" refers to a campaign for which the call center receives a greater remuneration whether or not the call center in turn increases the rate of pay of the agent. Similarly, if the performance is inferior, then the agent is moved to a lower campaign, i.e., one for which the call center receives less remuneration. If the performance just meets the objective, then the agent's assignment stays the same. There can be many levels of superior or inferior performance but the basic principle remains the same.
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 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Crystol Stewart whose telephone number is (571)272-1691. The examiner can normally be reached 9:00am-5:00pm.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624