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 .
This Office Action is responsive to Applicant's amendment filed on 6 April 2026. The Amendment filed 28 July 2025 amended claims 1, and 11. Currently Claims 1-3, 6, 8, 9, 11-13, 16, 18, and 19 are pending and have been examined. Claims 4 and 14 have been canceled and claims 5, 7, 10, 15, 17, 20 and 21 were previously canceled. The Examiner notes that the 101 rejection has been maintained.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6 April 2026 has been entered.
Response to Arguments
Applicant's arguments filed 6 April 2026 have been fully considered but they are not persuasive.
The Applicant argues on pages 8-9 that the precedential Appeals Review Panel decision in Ex Parte Desjardins, Appeal No. 2024-000567 (September 26, 2025), as incorporated into the MPEP via the December 5, 2025 Deputy Commissioner Memorandum, controls the eligibility analysis here and compels a finding that the claims are directed to patentable subject matter, because the claimed features "provide technical improvements over conventional systems by addressing challenges in continual learning and model efficiency."
The Examiner respectfully disagrees.
In response to the argument the Examiner notes that the Examiner has fully considered the Desjardins decision and the December 5, 2025 Deputy Commissioner Memorandum, which are binding guidance, and acknowledges that Examiners must not evaluate claims "at such a high level of generality" that meaningful technical limitations are dismissed without adequate explanation. However, Desjardins is distinguishable from the instant claims in a critical and dispositive respect: in Desjardins, the specification disclosed, and the claims specifically reflected, a concrete technical improvement to the operation of the machine learning model itself namely, training the model to learn new tasks while protecting knowledge about previous tasks, directly addressing the well-recognized technical problem of "catastrophic forgetting" in continual learning systems. The relevant claim limitation in Desjardins specifically recited "adjust the first values of the plurality of parameters 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," which reflected a particular technical solution to a particular technical problem in machine learning architecture.
The December 5, 2025 memo confirms this, adding to MPEP 2106.05(a) examples of eligible improvements including: "An improved way of training a machine learning model that protected the model's knowledge about previous tasks while allowing it to effectively learn new tasks" and "Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams." In the instant application, the amended claims recite "update the ad hoc model of the neural network based on an adequacy of the adjustment to the one or more work schedules as determined from real world operations to improve an accuracy of the work load prediction of the neural network." While this new limitation adds a model-updating step, it does not identify which parameters of the model are adjusted, what specific technical challenge in the machine learning model's operation is being addressed, or how the update mechanism technically functions it merely states at a high level that the model is updated based on real-world outcomes to improve accuracy. Per the December 5, 2025 Memorandum's revision of MPEP 2106.04(d)(1), a claim must "include the components or steps of the invention that provide the improvement described in the specification," and the specification describes the improvement as helping employers "achieve sufficient levels of staffing" and providing more accurate work-level predictions for business management purposes which are improvements to a business outcome, not improvements to how the machine learning model itself technically operates, as was the case in Desjardins. Unlike Desjardins, the specification of the instant application does not identify a specific technical problem inherent in machine learning systems that the claims solve; accordingly, the Desjardins framework, properly applied, does not compel eligibility here, and the rejection is maintained.
The Applicant argues on pages 9-10 that the claims as amended cannot practically be performed in the human mind because the amended independent claims 1 and 11 include features such as specific database inputs to the neural network, detection of a forecasted severe weather event, execution of an ad hoc neural network model, and real-time transmission of electronic messages that preclude mental performance, and therefore the claims are not directed to a mental process under MPEP 2106.04(a)(2).
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that the Examiner acknowledges the correct legal standard: per MPEP 2106.04(a)(2) and the 2024 AI-SME Update, claims do not recite a mental process when they contain limitations that cannot practically be performed in the human mind, and claim limitations that "only encompass AI in a way that cannot practically be performed in the human mind do not fall within" the mental processes grouping. However, the test is not whether a neural network is invoked or whether the process would be inefficient to perform manually; it is whether the underlying concept described by the claim limitations can practically be performed in the human mind using observations, evaluations, judgments, and opinions. See MPEP 2106.04(a)(2). The core of the amended claims, stripped of the generic computer and neural network recitations, describes: (1) correlating historical staffing workload patterns to past trigger conditions (a mental evaluation); (2) detecting that a forecasted weather event has characteristics exceeding predefined thresholds (a mental observation and comparison); (3) predicting a workload in response to that detection (a mental judgment); (4) determining that a predicted workload deviates from a current staffing schedule by a threshold amount and creating a schedule adjustment (a mental decision); and (5) updating a predictive model based on whether the adjustment proved adequate in practice (a mental learning-from-experience process). These are precisely the activities workforce managers and staffing supervisors have performed for decades by reviewing historical weather-related staffing records, observing incoming forecasts, judging likely call or claim volumes, and adjusting staff schedules accordingly.
The August 4, 2025 Deputy Commissioner Memorandum makes clear that the relevant inquiry under mental processes is whether the concept "can practically be performed in the human mind using observations, evaluation, judgement, and opinion," not whether it can be performed optimally or at industrial scale and here, the underpinning concept of workforce scheduling in response to anticipated trigger conditions plainly involves mental observations, evaluations, and judgments. The fact that the claims invoke a neural network and specific databases to perform these mental activities does not remove them from the mental processes grouping when, as the August 4, 2025 memo notes, the computer or ML component is simply being used as a tool to automate an existing process rather than to solve a problem in computer technology itself. The Applicant's reliance on the assertion that the amended claims recite activities that "cannot be mentally performed" misconstrues the analysis the presence of a neural network does not automatically preclude a mental process finding when the claims are described at such a high level of generality that the neural network performs no function beyond automating the described mental evaluations. The rejection is therefore maintained.
The Applicant argues on page 10 that the Applicant argues that the claims as amended are more closely analogized to AI-SME Update Example 47 Claim 3 (eligible, improving the technical field of network intrusion detection) and Example 39 (eligible, involving use and training of a machine learning algorithm), rather than to Example 47 Claim 2 (ineligible), and that per Desjardins the claims integrate the abstract idea into a practical application.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that Under Step 2A Prong Two, the Examiner must determine whether the claim as a whole integrates the recited judicial exception into a practical application, including whether it reflects an improvement to the functioning of a computer or to another technology or technical field. See MPEP 2106.04(d); December 5, 2025 Desjardins Memo. The analogy to Example 47 Claim 3 is not persuasive. In that example, the eligible claims integrated the abstract idea into a practical application by improving the technical field of network intrusion detection a computer and network security field specifically by detecting source addresses of malicious packets in real time, dropping those packets in real time, and blocking future traffic, thereby providing a concrete technical solution to a technical problem in network security that the background section specifically identified. The improvement was to a computer technology field (network security), implemented through specific real-time actions that changed the computer network's state in a concrete, technical way. In the instant application, the technical field of the improvement is workforce management and staffing scheduling a business management field and the real-time actions claimed (transmitting schedule adjustment notifications to employee devices) are standard communication outputs that serve business management purposes rather than improving computer or network functionality. The background section of the instant specification confirms this: the problem identified is that employers struggle to achieve "sufficient levels of staffing" when weather events occur and that "reactionary" schedule changes "delay the timeliness of the response" these are business operations challenges, not technical problems in computer science or ML technology.
Moreover, regarding the analogy to Desjardins at Prong Two: as discussed above with respect to Argument 1, the claims' new "update the ad hoc model" limitation does not specifically reflect an improvement to machine learning technology itself in the way Desjardins required, because the specification does not describe and the claims do not reflect how the ML model's technical operation is improved only that it produces more accurate business predictions over time. The specification's description of the neural network receiving inputs from three databases and generating a prediction as an output, while more specific than before, still does not describe a particular ML architecture, algorithm, or technical mechanism that distinguishes this system from any conventional ML application to workforce scheduling. Under the three-factor test from the August 4, 2025 memo (1) whether the claim recites only the idea of a solution rather than a particular technical solution; (2) whether the computer is invoked merely as a tool; and (3) the particularity of the application the claims still describe the idea of using ML for staffing prediction, invoke the neural network as a tool to perform the existing business process, and apply that idea generally to any ML-based staffing system. The rejection is therefore maintained.
The Applicant argues on page 10 that the Applicant argues that The Applicant argues that the claims as amended recite "additional features" not taught by the prior art of record that, when the claim is interpreted as a whole, constitute significantly more than the alleged abstract idea, and therefore the claims provide an inventive concept under Step 2B.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that Under Step 2B, the question is whether any additional elements individually or in combination amount to significantly more than the judicial exception itself. Per MPEP 2106.05(d) and the AI-SME Update, additional elements are evaluated to determine whether they represent well-understood, routine, conventional activity previously known in the industry, specified at a high level of generality. Applicant's argument that features not taught by the prior art constitute significantly more conflates the 103 analysis with the 101 analysis: the absence of a limitation in the prior art does not establish that the limitation amounts to significantly more than the abstract idea for 101 purposes. See MPEP 2106.05(d). Turning to the specific amended elements: the three-database architecture (weather database, historical database, current database) receiving inputs to the neural network is a generic database configuration described at a high level; using multiple databases to feed a machine learning model is well-understood and routine in any ML-based prediction system. The "detect an occurrence of a weather-related trigger condition... having at least one weather-related characteristic outside of one or more predefined thresholds" limitation describes standard threshold comparison logic comparing sensor or forecast data against a predefined value is conventional programming that has been well-understood in computer systems for decades, as noted in the prior Final Office Action.
The "execute... the ad hoc model using the neural network to make a work load prediction" is generic invocation of ML model execution without any details about model architecture, training parameters, or implementation that would distinguish it from any conventional neural network application. The AI-SME Update Example 47 Claim 2 analysis is directly on point: the training of an ANN "based on the input data and a selected training algorithm" was held to be well-understood, routine, conventional activity at Step 2B, and similarly here, "training an ad hoc model... to correlate one or more workload patterns with trigger conditions" is an equally generic ML training step. The new "update the ad hoc model... based on an adequacy of the adjustment... as determined from real world operations" is likewise stated at a high level of generality it does not specify what aspect of the model is updated, what parameters are modified, or how the technical updating mechanism operates; using real-world feedback to retrain a predictive model is a standard ML practice.
The "automatically transmit in real-time an electronic message to a respective client device" amounts to standard output/communication activity that the AI-SME Update Example 47 Claim 2 analysis identified as "insignificant extra-solution activity" amounting to "receiving or transmitting data over a network, which is well-understood, routine, conventional activity." Even when considered in combination, these additional elements collectively represent mere instructions to apply the abstract idea of workload prediction and schedule adjustment using generic ML technology precisely the scenario the AI-SME Update identifies as failing Step 2B. The rejection is therefore maintained.
The remaining Applicant's arguments filed 6 April 2026 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.— Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 6 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 6 is improperly dependent on claim 4 which has been currently canceled. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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, 6, 8-9, 11-13, 16, 18, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims, as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claims are directed to the abstract idea of automating the business practice of workforce staffing prediction, scheduling, and reallocation in response to anticipated weather-based trigger conditions an abstract idea that falls within the groupings of certain methods of organizing human activity (specifically, managing personal behavior or relationships or interactions between people, and fundamental economic principles or practices) and, in the alternative, mental processes. This judicial exception is not integrated into a practical application, and the claims do not include additional elements sufficient to amount to significantly more than the judicial exception itself. Claim(s) 1-3, 6, 8-9, 11-13, 16, 18, and 19 are directed to an abstract idea without significantly more.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test, claims 1-3, 6, 8, and 9 are directed to a system/machine, and claims 11-13, 16, 18, and 19 are directed to a method. Both statutory categories are satisfied. Therefore, the claims fall within at least one statutory category of invention.
Step 2A, Prong 1
The claimed invention is directed to an abstract idea. The independent claims 1 and 11, as amended, recite limitations that constitute a judicial exception under two independent abstract idea groupings: (A) Certain Methods of Organizing Human Activity, and (B) in the alternative, Mental Processes. Under MPEP 2106.04(a)(2), care should be taken not to parse the claim into multiple exceptions; accordingly, these groupings are treated collectively as a single abstract idea for purposes of the analysis that follows.
Option A Certain Methods of Organizing Human Activity (Primary Grouping) (MPEP 2106.04(a)(2), Subsection II): The claims recite abstract ideas that fall within two sub-groupings of certain methods of organizing human activity: (i) managing personal behavior or relationships or interactions between people, and (ii) fundamental economic principles or practices.
Sub-Grouping 1 Managing Personal Behavior or Relationships or Interactions Between People: The claims are fundamentally directed to managing how workers (people) are scheduled, assigned, and reallocated among work tasks in response to anticipated business conditions. Workforce scheduling and staffing reallocation are established activities involving the management of interactions between employers and employees determining which individuals work which shifts, which employees are reassigned to address increased workloads, and notifying individual workers of changes to their personal work schedules. The specification confirms this characterization: the invention is titled Staffing Forecasting and Reallocation System and paragraph [0001] states the invention "generally relates to staffing reallocation... to accurately forecast and reallocate work staffing in response to a prediction and/or detection of an occurrence of a trigger condition(s)." Paragraph [0003] specifically identifies the problem as determining "which workers, if any, should be reallocated from currently scheduled tasks to other tasks." The overall framework identifying employees to reassign, adjusting their schedules, and notifying them of schedule changes is a method of managing personal behavior and interactions between people that employers have practiced for decades. See MPEP 2106.04(a)(2), Subsection II.B; In re Marco Guldenaar Holding B.V.
Sub-Grouping 2 Fundamental Economic Principles or Practices: The claims are also directed to a fundamental economic practice: optimizing business staffing levels to minimize cost while maximizing customer service quality in response to variable demand conditions. The specification expressly frames the problem in economic terms: paragraph [0002] states that "Employers often seek to achieve sufficient levels of staffing of workers so as to provide customers with a satisfactory level of service in a cost-efficient manner." The goal of the claimed invention predicting when staffing adjustments are economically warranted, determining the minimal adequate staffing response, and avoiding both costly overstaffing and service-damaging understaffing is a fundamental economic management practice. Optimizing resource allocation (labor) in response to predicted demand fluctuations is an economic practice comparable to those the courts and USPTO have recognized as abstract, including risk management, hedging, and price optimization. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l; OIP Techs., Inc. v. Amazon.com, Inc., (a new method of price optimization is a fundamental economic concept); MPEP 2106.04(a)(2), Subsection II.A.
The claims recite the following specific limitations that fall within the CMHA groupings, considered collectively as the single abstract idea of automating workforce staffing prediction, scheduling, and reallocation in response to weather-related trigger conditions:
"train an ad hoc model of the neural network to correlate one or more work load patterns with one or more respective trigger conditions based on the neural network training data, wherein the neural network receives data from each of the weather database, the historical database, and the current database as inputs and generates a work load prediction as an output" This limitation describes the foundational step of the business practice: analyzing historical staffing data correlated with past trigger conditions to establish predictive relationships between weather events and associated workload demands. Identifying historical patterns between business events and staffing needs is a quintessential business management practice performed by workforce managers and operational planners. The use of a neural network to perform this analysis described without any specific architectural, algorithmic, or implementation detail implements the business practice of learning from historical operational data to improve future staffing decisions. This is the type of activity the Federal Circuit has identified as automating an existing abstract idea when ML steps are "incidental" to the business purpose. See Recentive Analytics, Inc. v. Fox Corp., (steps incidental to automating an abstract idea of scheduling were insufficient to confer eligibility); August 4, 2025 Deputy Commissioner Memorandum.
"detect an occurrence of a weather-related trigger condition associated with a forecasted severe weather event having at least one weather-related characteristic outside of one or more predefined thresholds" This limitation describes the triggering mechanism for initiating the business practice of evaluating whether staffing adjustment is warranted. Monitoring external conditions (weather forecasts) and determining whether they exceed threshold values warranting a business response e.g., whether an incoming storm is severe enough to justify pre-emptive staffing changes is a commercial management decision that businesses and insurance companies have made as a standard economic practice for decades. The determination of whether a weather event crosses a severity threshold that warrants operational adjustment is a fundamental business judgment.
"execute, in response to detection of the weather-related trigger condition, the ad hoc model using the neural network to make a work load prediction, the work load prediction based in part on continuous training of the neural network and the at least one weather-related characteristic of the forecasted weather event" This limitation describes using the previously trained model to generate a staffing needs forecast the core economic output of the claimed system. Forecasting labor demand in response to anticipated business events (weather trigger conditions) is a standard business planning activity. The specification describes this as predicting "an anticipated work load, work hours, or staffing" (Spec. [0017]), which is the classic economic activity of demand forecasting for resource allocation. The invocation of a generic neural network to perform this forecasting, described at a high level without specific technical implementation details, automates an existing business forecasting practice.
"create an adjustment to one or more work schedules of the current staffing schedule in response to the work load prediction deviating from a current staffing schedule by at least a threshold amount" This limitation recites the core business management action: modifying employee work schedules when predicted staffing needs exceed current scheduled levels by a defined threshold. Adjusting work schedules to align staffing with anticipated business demand is the fundamental act of workforce management a method of organizing how people work that has been practiced in every industry that manages human labor. This is squarely within "managing personal behavior or relationships or interactions between people" and "fundamental economic principles" as it constitutes the core business decision of labor resource allocation.
"automatically transmit in real-time an electronic message to a respective client device of each individual impacted by the adjustment to the one or more work schedules" This limitation recites notifying affected employees of their schedule changes. Employee notification of work schedule changes is a longstanding management practice a business interaction between employers and employees. The electronic transmission is a generic output step that implements this existing business communication practice.
"update the ad hoc model of the neural network based on an adequacy of the adjustment to the one or more work schedules as determined from real world operations to improve an accuracy of the work load prediction of the neural network" This limitation recites the business practice of evaluating the effectiveness of past staffing decisions and incorporating lessons learned into future planning a standard operational feedback and continuous improvement practice. Using actual outcomes to refine future business predictions is a fundamental economic management activity. The description of this practice in terms of a neural network update, without any specific technical detail about what parameters are modified or how the update is technically implemented, amounts to implementing a standard business feedback loop on a generic ML system.
Collectively, these limitations define the abstract idea of automating the business practice of workforce staffing prediction, scheduling, and reallocation in response to anticipated weather-based trigger conditions. The act of predicting labor demand based on external trigger events and adjusting human work schedules accordingly including notifying workers and incorporating outcome feedback is a method of organizing human activity that employers, particularly in service industries such as insurance call centers (see Spec. par. [0017]-[0021]), have conducted as a standard business practice for decades. See Recentive Analytics.
Option B Mental Processes (Alternative Grouping) (MPEP 2106.04(a)(2), Subsection III)
In the alternative, the identified claim limitations also constitute a mental process. Under MPEP 2106.04(a)(2), Subsection III, claims recite a mental process when they contain limitations that can practically be performed in the human mind, including observations, evaluations, judgments, and opinions. The core steps of the claimed invention correlating historical workload patterns to past trigger conditions (a mental evaluation), observing whether a weather forecast exceeds predefined thresholds (a mental observation), predicting a workload based on trigger characteristics (a mental judgment), comparing the predicted workload to current staffing and deciding whether adjustment is warranted (a mental decision), and evaluating the adequacy of past adjustments to refine future predictions (a mental learning process) are precisely the evaluative activities that workforce managers and business planners have performed mentally for decades. The August 4, 2025 Deputy Commissioner Memorandum confirms that the relevant inquiry is whether the concept can practically be performed in the human mind using observations, evaluations, judgments, and opinions, not whether it can be performed optimally or at industrial scale. Workforce managers have analyzed historical staffing data against trigger events, judged likely workload impacts, compared predictions to available staff, decided on reallocations, and refined their approaches based on outcomes all mentally, with pen and paper long before computers. The recitation of a neural network and generic databases performing these evaluations at a high level of generality, without specific technical implementation details, does not remove these limitations from the mental processes grouping. See MPEP 2106.04(a)(2), Subsection III.B; Electric Power Group v. Alstom, S.A.; AI-SME Update Example 47 Claim 2 analysis (July 2024).
The Examiner notes that whether the primary characterization is CMHA or mental processes, the Step 2A Prong 1 analysis reaches the same conclusion: the claims recite a judicial exception, and the analysis proceeds to Step 2A Prong 2.
Step 2A, Prong 2
The determined judicial exception is not integrated into a practical application. The Examiner identifies the following additional elements beyond the recited abstract idea:
One or more databases: specifically, a weather database, a historical database, and a current database receiving neural network training data
At least one processor
Memory coupled to the at least one processor containing executable instructions
Real-time electronic message transmission to respective client devices
The following considerations establish that these additional elements, evaluated individually and in combination, do not integrate the judicial exception into a practical application:
Improvement to Technology or Technical Field (MPEP 2106.05(a)):
The claims do not reflect an improvement to the functioning of a computer or to any other technology or technical field. The Examiner has fully considered the precedential decision in Ex Parte Desjardins, Appeal, as incorporated into the MPEP by the December 5, 2025 Deputy Commissioner Memorandum. In Desjardins, the claims were found eligible because they specifically reflected a technical improvement to how the machine learning model itself operates protecting performance on prior tasks while learning new tasks, addressing the specific ML-technical problem of "catastrophic forgetting" in continual learning systems. The claimed limitations in Desjardins recited adjustments to specific parameters to achieve a defined technical outcome within the ML model architecture itself.
In the instant application, the specification frames the invention as a solution to a business problem: employers struggle to efficiently manage staffing levels during weather events. Specification paragraph [0002] identifies the goal as achieving "sufficient levels of staffing... in a cost-efficient manner." The specification does not identify a specific technical problem inherent to machine learning systems that the claimed invention solves it does not address catastrophic forgetting, model efficiency, computational limitations, or any other problem in ML technology. The new limitation "update the ad hoc model of the neural network based on an adequacy of the adjustment to the one or more work schedules as determined from real world operations to improve an accuracy of the work load prediction" describes the business practice of using outcome feedback to refine future business predictions, stated at a high level of generality. It does not specify which model parameters are updated, what technical mechanism performs the update, or how the machine learning system's technical operation is improved beyond producing more accurate business outcomes. An improvement to business prediction accuracy is not an improvement to computer technology. See December 5, 2025 Desjardins Memo; Intellectual Ventures I LLC v. Symantec Corp.) (claims must themselves include components or steps that reflect the disclosed technological improvement).
Moreover, per the August 4, 2025 Deputy Commissioner Memorandum, the Examiner must consider whether the claim as a whole provides an improvement to technology or merely uses technology as a tool to improve the recited judicial exception (i.e., automates a business practice). Here, the three-factor test of the Memorandum confirms the claims fall in the latter category: (1) the claims recite only the idea of a solution automated staffing adjustment rather than a particular technical solution; (2) the neural network, databases, and processor are invoked as tools to perform the existing business practice of workforce scheduling; and (3) the application is general any ML-based staffing system satisfies the claim language rather than a particular technical implementation with meaningful limitations. The Federal Circuit's decision in Recentive Analytics, Inc. v. Fox Corp., cited in the August 4, 2025 Memorandum, directly supports this conclusion: steps incidental to automating an abstract idea (scheduling) were insufficient to confer eligibility even where machine learning was involved.
Mere Instructions to Apply the Exception (MPEP 2106.05(f)):
The additional elements amount to no more than mere instructions to implement the abstract idea on a generic computer. The recited processor, memory, databases, and neural network described without specific architectural, algorithmic, or implementation detail amount to generic computing components performing generic computing functions. The claims do not specify particular hardware configurations, specific neural network architectures, particular training algorithms, or any technical mechanism that would distinguish the claimed system from any conventional application of machine learning to the business problem of workforce scheduling. As the AI-SME Update Example 47 Claim 2 analysis confirms, when a neural network is "used to generally apply the abstract idea... without placing any limitation on how [it] operates" and "described at a high level such that it amounts to using a computer with a generic [neural network] to apply the abstract idea," the claims fail to integrate the exception into a practical application. See MPEP 2106.05(f); AI-SME Update Example 47 Claim 2 (July 2024).
Insignificant Extra-Solution Activity (MPEP 2106.05(g)):
The receiving of neural network training data from the weather, historical, and current databases (data gathering inputs) and the automatic real-time transmission of schedule adjustment electronic messages to client devices (data output) constitute insignificant extra-solution activity. Per the AI-SME Update Example 47 Claim 2 analysis: "receiving and outputting were considered insignificant extra solution activity... The limitations are mere data gathering and output recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity." See MPEP 2106.05(g). Even when considered in combination with the other additional elements, these data input and output steps do not impose meaningful limits on the claim.
Field of Use / Particular Technological Environment (MPEP 2106.05(h)):
The recitation that the system operates in the context of call center and queue system staffing (see claims 6 and 16), and that the databases receive data related to weather events and historical staffing in the insurance/call center context, amounts to limiting the abstract idea to a particular field of use or technological environment, which does not integrate the exception into a practical application. See MPEP 2106.05(h). The specification confirms the insurance/call center context is merely a preferred field of application. See Spec. paragraphs [0019]-[0022].
Considering the additional elements the three-database architecture, the generic processor and memory, the generic neural network invocation, the real-time communication output, and the high-level model update step individually and in combination, the claim as a whole does not integrate the judicial exception into a practical application. The additional elements do not reflect a specific technical improvement to the computer or to machine learning technology of the kind required under Ex Parte Desjardins and MPEP 2106.05(a). The core improvement described in the specification is to business outcomes (better staffing efficiency), not to computer functionality. Accordingly, the claims are directed to an abstract idea.
Step 2B
The claims do not include additional elements sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A Prong Two, the additional elements amount to no more than mere instructions to apply the abstract idea using generic computer components. The same analysis applies at Step 2B mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(f). The claims are therefore directed to the abstract idea performed on generic computers. Using a computer to obtain, process, and output data resulting from a method of organizing human activity merely implements the abstract idea in the manner of "apply it" and does not provide significantly more.
Well-Understood, Routine, Conventional Activity Analysis:
The additional elements, individually and in combination, represent well-understood, routine, and conventional activities, as supported by the following evidence:
Generic multi-database architecture (weather database, historical database, current database): The use of multiple databases to store historical event data, historical operational data, and current operational data for use by a predictive ML system is well-understood and routine in ML-based prediction systems. The Examiner notes that the primary reference Datilio teaches using multiple databases, including archived event data and historical staffing data, to train neural networks for staffing prediction in an identical context, establishing that such multi-database architectures are well-known in this art. See Datilio, pg. 2, par. 10-11 (historical database); pg. 5, par. 4 (archived events for model optimization). The specification itself describes these databases in functional, high-level terms without any novel structural details. See Spec. paragraphs [0030]-[0037].
Generic neural network training to correlate patterns with conditions: Training a neural network on historical input data to identify patterns and generate predictions is well-understood, routine, and conventional ML activity. The AI-SME Update Example 47 Claim 2 analysis specifically found that "training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN" was well-understood, routine, conventional activity described at a high level of generality. The same conclusion applies to "train an ad hoc model of the neural network to correlate one or more work load patterns with one or more respective trigger conditions" no specific training algorithm, no specific network architecture, and no specific technical details are recited. See MPEP 2106.05(d); AI-SME Update Example 47 Claim 2.
Threshold-based trigger detection: Detecting whether data values exceed predefined thresholds is standard, conventional threshold comparison logic well-understood in computer systems. The prior art reference Ramanasankaran teaches threshold-based triggering in an analogous AI-based prediction context (see Ramanasankaran, par. [0150]-[0151]), and Johnston teaches threshold-based change detection for call center operations (see Johnston, col. 15, lines 1-21). Threshold comparison operations have been recognized as conventional programming for decades. See MPEP 2106.05(d)(II).
Generic ML model execution to generate a prediction: Executing a trained ML model on new input data to generate an output prediction is a routine, conventional ML operation. AI-SME Update Example 47 Claim 2 found that using "the trained ANN" to detect and analyze data "provides nothing more than mere instructions to implement an abstract idea on a generic computer." The claimed "execute... the ad hoc model using the neural network to make a work load prediction" is materially identical in character a generic invocation of an ML model to produce an output, with no technical specificity about how the execution is accomplished. See MPEP 2106.05(d); AI-SME Update Example 47 Claim 2.
Generic model update based on real-world feedback: Using actual outcome data to update or retrain a predictive model feedback-based model retraining is a standard, well-understood machine learning practice. The claimed "update the ad hoc model of the neural network based on an adequacy of the adjustment to the one or more work schedules as determined from real world operations" does not specify what model parameters are modified, what update algorithm is used, or any technical detail that would distinguish this from any conventional model retraining operation. Retraining ML models based on outcome feedback has been a standard practice since the inception of supervised machine learning. See MPEP 2106.05(d).
Generic electronic notification to client devices: Automatically transmitting electronic messages to communication devices is well-understood, routine, and conventional communication activity. The AI-SME Update Example 47 Claim 2 analysis specifically found that data output steps "amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity." Transmitting schedule notifications to employee mobile devices is directly analogous. See MPEP 2106.05(d)(II); Symantec; TLI Communications LLC v. AV Auto. LLC.
Even when all additional elements are considered in combination, they collectively represent mere instructions to apply the abstract idea of workforce staffing prediction and scheduling management using a generic ML-based computer system. No combination of generic computer components performing generic computer functions databases storing data, processors executing ML models, networks transmitting notifications amounts to significantly more than the underlying method of organizing human activity. See Alice Corp.; AI-SME Update Example 47 Claim 2. The claims are ineligible.
Dependent Claims Analysis
The dependent claims do not add limitations that integrate the judicial exception into a practical application or provide an inventive concept. The dependent claims recite limitations that narrow the metes and bounds of the abstract idea but do not provide 'something more.' The dependent claims do not remedy the deficiencies of the independent claims.
Claims 2 and 12 recite identifying specific individuals, teams, groups, and/or departments to reallocate to assist with a predicted change in workload. This limitation further specifies the business management aspect of the abstract idea namely, which workers should be reassigned as a result of the staffing prediction. Identifying and selecting personnel for reallocation based on predicted workload need is a core element of the abstract idea of workforce management and organizing human activity, not an additional element that integrates the exception into a practical application. This limitation falls squarely within the "managing personal behavior or relationships or interactions between people" sub-grouping.
Claims 3 and 13 recite retrieving the current staffing schedule from a staffing system. This step constitutes insignificant extra-solution/pre-solution data gathering activity obtaining an existing work schedule for use as an input to the abstract business process of comparing predicted need against current staffing. This data retrieval step imposes no meaningful technical limitation on the claims and amounts to routine data access. See MPEP 2106.05(g).
Claim 6 recites one or more queue systems having one or more call centers. This limitation specifies the business field of use (call center/queue system environment) for the abstract idea. Limiting the abstract idea to a particular industry context (insurance call centers) does not integrate the exception into a practical application; it merely applies the business practice within a particular commercial setting. See MPEP 2106.05(h). The Examiner further notes that claim 6 depends from canceled claim 4, and Applicant should correct this dependency.
Claims 8 and 18 recite monitoring a level of service provided to inbound calls and determining whether the level of service satisfies a predetermined threshold. This limitation further defines the business performance monitoring aspect of the abstract idea evaluating whether service quality metrics meet business targets and falls within the abstract idea of managing the commercial operations of a business. Monitoring service metrics and comparing them to threshold KPIs is a standard business management activity and well-understood computer data monitoring operation, neither of which integrates the exception into a practical application or provides significantly more.
Claims 9 and 19 recite recording an actual workload level, evaluating the accuracy of the workload prediction, and adding accuracy results to the neural network training data. These limitations further define the operational feedback and continuous improvement aspect of the business practice (evaluating whether past staffing predictions and adjustments were adequate), which is an inherent component of the abstract idea itself. The recording and accuracy-evaluation steps are analogous to the "update the ad hoc model" limitation of the independent claims and suffer from the same deficiencies they describe standard data recording and model feedback operations at a high level of generality without technical specificity.
Claims 11-13, 16, 18, and 19 recite method claim counterparts of the system claims 1-3, 6, 8, and 9, respectively. These method claims recite the same abstract idea in process form and are rejected for the same reasons set forth above with respect to their system claim counterparts. Independent method claim 11 recites substantially the same limitations as independent system claim 1, and the abstract idea analysis applies with equal force.
Based on the foregoing analysis, claims 1-3, 6, 8-9, 11-13, 16, 18, and 19 are directed to an abstract idea the business practice of automating workforce staffing prediction, scheduling, and reallocation in response to weather-related trigger conditions that falls within the groupings of certain methods of organizing human activity (managing personal behavior or relationships or interactions between people, and fundamental economic principles or practices) and, in the alternative, mental processes. The judicial exception is not integrated into a practical application, and the claims do not provide an inventive concept. Accordingly, claims 1-3, 6, 8-9, 11-13, 16, 18, and 19 are ineligible under 35 U.S.C. 101.
The prima facie case of ineligibility has been properly established in accordance with MPEP 2106.07. The burden now shifts to Applicant to present persuasive arguments or evidence demonstrating that the claims are directed to patent-eligible subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Luijckx et al. (U.S. Patent Publication 2023/0316203 A1) (hereafter Luijckx) in view of Chen et al. (U.S. Patent Publication 2004/0095237 A1) (hereafter Chen).
Referring to Claim 1, Datilio teaches a system for generating predictions by a neural network for staffing scheduling in response to a detection or prediction of a trigger condition, the system comprising:
one or more databases that receive neural network training data corresponding to a plurality of characteristics for each of a plurality of past trigger conditions and a recorded work load associated with each of the plurality of past trigger conditions, wherein the one or more databases include: (see; par. [0131] of Luijckx teaches the use of a neural network par. [0129] incorporating machine learning, par. [0103] and par. [0159] using ad hoc models that par. [0156] incorporate multiple databases including weather database data, par. [0108] which is used to identify one or more weather pattern or events (i.e. triggers)).
a weather database that includes data of characteristics relating to a plurality of actual weather events that previously occurred (see; par. [0083] of Luijckx teaches a weather database used to adjust the labor model for scheduling staffing).
a historical database that includes data relating to respective prior staffing information associated with each of the actual weather events identified in the weather database (see; par. [0086] of Luijckx teaches a historical trends of weather, par. [083] that impacts staffing changes).
a current database that includes data of operations experienced by a queue system and/or a workflow system during a predefined work period immediately preceding a current time (see; par. [0091] of Luijckx teaches forecasting during a projected time period to project staffing based on, par. [0083]-[0086] factors including current and stored data in databases).
at least one processor (see; par. [0065] of Luijckx teaches a processor).
a memory coupled to the at least one processor, the memory including instructions that, when executed by the at least one processor, cause the system to (see; par. [0067] of Luijckx teaches memory coupled to computing devices utilizing processors).
train an ad hoc model of the neural network to correlate one or more work load patterns with one or more respective trigger conditions based on the neural network training data, wherein the neural network receives data from each of the weather database, the historical database, and the current database as inputs and generates a work load prediction as an output (see; par. [0131] of Luijckx teaches the use of a neural network par. [0103] and par. [0159] using ad hoc models that par. [0156] incorporate multiple databases including weather database data, par. [0091] which is further utilized to determine labor needs (i.e. work load patterns and prediction) based on these factors).
execute, in response to detection of the weather-related trigger condition, the ad hoc model using the neural network to make a work load prediction, the work load prediction based in part on continuous training of the neural network and the at least one weather-related characteristic of the forecasted weather event (see; par. [0131] of Luijckx teaches the use of a neural network par. [0129] incorporating machine learning, par. [0103] and par. [0159] using ad hoc models that par. [0156] incorporate multiple databases including weather database data, par. [0108] which is used to identify one or more weather pattern or events (i.e. triggers)).
create an adjustment to one or more work schedules of the current staffing schedule in response to the work load prediction deviating from a current staffing schedule by at least a threshold amount (see; par. [0083] of Luijckx teaches staffing scheduling taking into account factors including weather, par. [0093] where modification to staffing is based on external factors (i.e. triggers)).
automatically transmit in real-time an electronic message to a respective client device of each individual impacted by the adjustment to the one or more work schedules (see; par. [0093]-[0094] of Luijckx teaches a scheduling module that provides an electronic update and schedule modifications that are then transmitted in an automated fashion).
update the ad hoc model of the neural network based on an adequacy of the adjustment to the one or more work schedules as determined from real world operations to improve an accuracy of the work load prediction of the neural network (see; par. [0103] of Luijckx teaches the ad hoc decision making use received information that impact inventory (i.e. inventory) and make adjustments, par. [0108] and [0131] where the machine learning model and neural network is used to improve the accuracy of inputs and outputs (i.e. staffing needs (i.e. workload))).
Luijckx does not explicitly disclose the following limitations, however,
Chen teaches detect an occurrence of a weather-related trigger condition associated with a forecasted severe weather event having at least one weather-related characteristic outside of one or more predefined thresholds (see; par. [0112] of Chen teaches assessing using a weather module that can detect severe weather and trigger a curtailment of current consumption (i.e. trigger conditioned threshold)).
The Examiner notes that Luickx teaches similar to the instant application teaches managing labor to help automate management of inventory. Specifically, Luickx discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Chen teaches electronic message delivery system utilizable in the monitoring and control of remote equipment in order to adjust resources and scheduling and as it is comparable in certain respects to Luickx managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Luickx discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather. However, Luickx fails to disclose detect an occurrence of a weather-related trigger condition associated with a forecasted severe weather event having at least one weather-related characteristic outside of one or more predefined thresholds.
Chen discloses detect an occurrence of a weather-related trigger condition associated with a forecasted severe weather event having at least one weather-related characteristic outside of one or more predefined thresholds.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Luickx detect an occurrence of a weather-related trigger condition associated with a forecasted severe weather event having at least one weather-related characteristic outside of one or more predefined thresholds as taught by Chen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Luickx, and Chen teach the collecting and analysis of data related to a call or contact center in order to optimize scheduling and they do not contradict or diminish the other alone or when combined.
Referring to Claim 11, Luickx in view of Chen teaches a method for generating predictions by a neural network for staff scheduling. Claim 11 recites the same or similar limitations as those addressed above in claim 1, Claim 11 is therefore rejected for the same reasons as set forth above in claim.
Claim(s) 2, 3, 6, 8, 9, 12, 13, 16, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Luijckx et al. (U.S. Patent Publication 2023/0316203 A1) (hereafter Luijckx) in view of Chen et al. (U.S. Patent Publication 2004/0095237 A1) (hereafter Chen).
Referring to Claim 2, see discussion of claim 1 above, while Luickx in view of Chen teaches the system above, Luickx in view of Chen does not explicitly disclose a system having the limitations of, however,
Datilio teaches wherein the memory further includes instructions that, when executed by the at least one processor, cause the system to identify one or more individuals, teams, groups, and/or departments to reallocate to assist with a predicted change in a work load, the predicted change in the work load being at least partially based on the work load prediction from the neural network (see; pg. 11, par. 9 of Datilio teaches an allocation of agents based on need and qualification, pg. 5, par. 7 where the staffing includes adjusting to meet various workloads taking into account the workload prediction model and or employee staffing model, pg. 18, par. 2 which is utilized by the neural network for optimization).
The Examiner notes that Luickx teaches similar to the instant application teaches managing labor to help automate management of inventory. Specifically, Luickx discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Chen teaches electronic message delivery system utilizable in the monitoring and control of remote equipment in order to adjust resources and scheduling and as it is comparable in certain respects to Luickx managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Datilio teaches a contact center scheduling that generates a workload prediction indicating requirements to be introduced in a future planning period based on the workload prediction model and time series data and as it is comparable in certain respects to Luickx and Chen managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Luickx and Chen discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather. However, Luickx and Chen fails to disclose the memory further includes instructions that, when executed by the at least one processor, cause the system to identify one or more individuals, teams, groups, and/or departments to reallocate to assist with a predicted change in a work load, the predicted change in the work load being at least partially based on the work load prediction from the neural network.
Datilio discloses the memory further includes instructions that, when executed by the at least one processor, cause the system to identify one or more individuals, teams, groups, and/or departments to reallocate to assist with a predicted change in a work load, the predicted change in the work load being at least partially based on the work load prediction from the neural network.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Luickx and Chen the memory further includes instructions that, when executed by the at least one processor, cause the system to identify one or more individuals, teams, groups, and/or departments to reallocate to assist with a predicted change in a work load, the predicted change in the work load being at least partially based on the work load prediction from the neural network as taught by Datilio since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Luickx, Chen, and Datilio teach the collecting and analysis of data related to a call or contact center in order to optimize scheduling and they do not contradict or diminish the other alone or when combined.
Referring to Claim 3, see discussion of claim 1 above, while Luickx in view of Chen teaches the system above, Luickx in view of Chen does not explicitly disclose a system having the limitations of, however,
Datilio teaches the memory further includes instructions that, when executed by the at least one processor, cause the system to retrieve the current staffing schedule from a staffing system (see; pg. 5, par. 4 of Datilio teaches pulling staff schedules and using work load predictions to continuously improve the model and subsequent schedules, pg. 5, par. 2 where the schedule is pulled up and used for further optimization).
The Examiner notes that Luickx teaches similar to the instant application teaches managing labor to help automate management of inventory. Specifically, Luickx discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Chen teaches electronic message delivery system utilizable in the monitoring and control of remote equipment in order to adjust resources and scheduling and as it is comparable in certain respects to Luickx managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Datilio teaches a contact center scheduling that generates a workload prediction indicating requirements to be introduced in a future planning period based on the workload prediction model and time series data and as it is comparable in certain respects to Luickx and Chen managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Luickx and Chen discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather. However, Luickx and Chen fails to disclose the memory further includes instructions that, when executed by the at least one processor, cause the system to retrieve the current staffing schedule from a staffing system.
Datilio discloses the memory further includes instructions that, when executed by the at least one processor, cause the system to retrieve the current staffing schedule from a staffing system.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Luickx and Chen the memory further includes instructions that, when executed by the at least one processor, cause the system to retrieve the current staffing schedule from a staffing system as taught by Datilio since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Luickx, Chen, and Datilio teach the collecting and analysis of data related to a call or contact center in order to optimize scheduling and they do not contradict or diminish the other alone or when combined.
Referring to Claim 6, see discussion of claim 4 above, while Luickx in view of Chen teaches the system above, Luickx in view of Chen does not explicitly disclose a system having the limitations of, however,
Datilio teaches comprising one or more queue systems having one or more call centers (see; pg. 5, par. 4 of Datilio teaches a queue system for the contact center (i.e. call center)).
The Examiner notes that Luickx teaches similar to the instant application teaches managing labor to help automate management of inventory. Specifically, Luickx discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Chen teaches electronic message delivery system utilizable in the monitoring and control of remote equipment in order to adjust resources and scheduling and as it is comparable in certain respects to Luickx managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Datilio teaches a contact center scheduling that generates a workload prediction indicating requirements to be introduced in a future planning period based on the workload prediction model and time series data and as it is comparable in certain respects to Luickx and Chen managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Luickx and Chen discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather. However, Luickx and Chen fails to disclose comprising one or more queue systems having one or more call centers.
Datilio discloses comprising one or more queue systems having one or more call centers.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Luickx and Chen comprising one or more queue systems having one or more call centers as taught by Datilio since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Luickx, Chen, and Datilio teach the collecting and analysis of data related to a call or contact center in order to optimize scheduling and they do not contradict or diminish the other alone or when combined.
Referring to Claim 8, see discussion of claim 1 above, while Luickx in view of Chen teaches the system above, Luickx in view of Chen does not explicitly disclose a system having the limitations of, however,
Datilio teaches the memory further includes instructions that, when executed by the at least one processor, further cause the system to monitor a level of service provided to inbound calls received by the one or more call centers, and determine whether the level of service satisfies a predetermined threshold (see; pg. 8, par. 9 of Datilio teaches determining a level of service based on a satisfaction score for inbound calls and staff such that ac certain KPI is reached (i.e. level)).
The Examiner notes that Luickx teaches similar to the instant application teaches managing labor to help automate management of inventory. Specifically, Luickx discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Chen teaches electronic message delivery system utilizable in the monitoring and control of remote equipment in order to adjust resources and scheduling and as it is comparable in certain respects to Luickx managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Datilio teaches a contact center scheduling that generates a workload prediction indicating requirements to be introduced in a future planning period based on the workload prediction model and time series data and as it is comparable in certain respects to Luickx and Chen managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Luickx and Chen discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather. However, Luickx and Chen fails to disclose the memory further includes instructions that, when executed by the at least one processor, further cause the system to monitor a level of service provided to inbound calls received by the one or more call centers, and determine whether the level of service satisfies a predetermined threshold.
Datilio discloses the memory further includes instructions that, when executed by the at least one processor, further cause the system to monitor a level of service provided to inbound calls received by the one or more call centers, and determine whether the level of service satisfies a predetermined threshold.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Luickx and Chen the memory further includes instructions that, when executed by the at least one processor, further cause the system to monitor a level of service provided to inbound calls received by the one or more call centers, and determine whether the level of service satisfies a predetermined threshold as taught by Datilio since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Luickx, Chen, and Datilio teach the collecting and analysis of data related to a call or contact center in order to optimize scheduling and they do not contradict or diminish the other alone or when combined.
Referring to Claim 9, see discussion of claim 1 above, while Luickx in view of Chen teaches the system above, Luickx in view of Chen does not explicitly disclose a system having the limitations of, however,
Datilio teaches the memory further includes instructions that, when executed by the at least one processor, further cause the system to record an actual work load level generated by the occurrence of the trigger condition (see; pg. 16, par. 2 of Datilio teaches recording and aggregating performance and operational aspects in order to make adjustments for average wait time, discard rates, and seat occupancy (i.e. triggers)),
evaluate an accuracy of the work load prediction using the actual work load level (see; pg. 6, par. 8 of Datilio teaches evaluating the accuracy of the predictions of workload prediction models with actual data as a on optimal Key Performance Indicator (KPI) metric), and
wherein a result from the evaluation of the accuracy of the work load prediction is added to the neural network training data (see; pg. 6, par. 8 of Datilio teaches accuracy of predictions in order to adjust models that utilize, pg. 18, par. 2 a neural network to optimize workload predictions).
The Examiner notes that Luickx teaches similar to the instant application teaches managing labor to help automate management of inventory. Specifically, Luickx discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Chen teaches electronic message delivery system utilizable in the monitoring and control of remote equipment in order to adjust resources and scheduling and as it is comparable in certain respects to Luickx managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Datilio teaches a contact center scheduling that generates a workload prediction indicating requirements to be introduced in a future planning period based on the workload prediction model and time series data and as it is comparable in certain respects to Luickx and Chen managing labor to help automate management of inventory as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Luickx and Chen discloses a managing labor and inventory in a workplace to provide projected sales, projected staffing and recommended staff actions including understanding the impact of weather. However, Luickx and Chen fails to disclose the memory further includes instructions that, when executed by the at least one processor, further cause the system to record an actual work load level generated by the occurrence of the trigger condition, evaluate an accuracy of the work load prediction using the actual work load level, and wherein a result from the evaluation of the accuracy of the work load prediction is added to the neural network training data.
Datilio discloses the memory further includes instructions that, when executed by the at least one processor, further cause the system to record an actual work load level generated by the occurrence of the trigger condition, evaluate an accuracy of the work load prediction using the actual work load level, and wherein a result from the evaluation of the accuracy of the work load prediction is added to the neural network training data.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Luickx and Chen the memory further includes instructions that, when executed by the at least one processor, further cause the system to record an actual work load level generated by the occurrence of the trigger condition, evaluate an accuracy of the work load prediction using the actual work load level, and wherein a result from the evaluation of the accuracy of the work load prediction is added to the neural network training data as taught by Datilio since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Luickx, Chen, and Datilio teach the collecting and analysis of data related to a call or contact center in order to optimize scheduling and they do not contradict or diminish the other alone or when combined.
Referring to Claim 12, see discussion of claim 11 above, while Luickx in view of Chen teaches the method above Claim 12 recites the same or similar limitations as those addressed above in claim 2, Claim 12 is therefore rejected for the same or similar limitations as set forth above in claim 2.
Referring to Claim 13, see discussion of claim 11 above, while Luickx in view of Chen teaches the method above Claim 13 recites the same or similar limitations as those addressed above in claim 3, Claim 13 is therefore rejected for the same or similar limitations as set forth above in claim 3.
Referring to Claim 16, see discussion of claim 14 above, while Luickx in view of Chen teaches the method above Claim 16 recites the same or similar limitations as those addressed above in claim 6, Claim 16 is therefore rejected for the same or similar limitations as set forth above in claim 6.
Referring to Claim 18, see discussion of claim 17 above, while Luickx in view of Chen teaches the method above Claim 18 recites the same or similar limitations as those addressed above in claim 8, Claim 18 is therefore rejected for the same or similar limitations as set forth above in claim 8.
Referring to Claim 19, see discussion of claim 11 above, while Luickx in view of Chen teaches the method above Claim 19 recites the same or similar limitations as those addressed above in claim 9, Claim 19 is therefore rejected for the same or similar limitations as set forth above in claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
D’ Attilio et al. (CN 116368505 A) discloses a method and system for extensible contact centre seat arraignment using automatic AI modeling and multi-target optimization.
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/S.S.S/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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