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 Final Office Action is responsive to Applicant's amendment filed on 28 July 2025. The Amendment filed 28 July 2025 amended claims 1, 8, 11, and 18. Currently Claims 1-4, 6, 8, 9, 11-14, 16, 18, and 19 are pending and have been examined. Claims 5, 7, 15, 17, 20 and 21 have been canceled and claim 10 was previously canceled. The Examiner notes that based on continuing court cases and the guidance provided by the United States Patent Office in July of 2024 and therefore a 101 rejection has been newly presented.
Response to Arguments
Applicant's arguments filed 28 July 2025 have been fully considered but they are not persuasive.
The Applicant argues on pages 10-11 that “independent claims 1 and 11 have been amended to delete the allegedly problematic language (“identify a number of insurance policies in force anticipated to be impacted by the trigger condition” and “the one or more characteristics of the trigger condition comprising the identified number of insurance policies in force anticipated to be impacted by the trigger condition”)… A person of ordinary skill in the art would readily appreciate that an NP-Hard problem is a problem that has non-deterministic polynomial-type harness – i.e. a problem that cannot be practically solved by a human mentally with the aid of pen and paper. The primary reference further describes that “scheduling optimization models may become too large to be resolved in a reasonable amount of time using conventional techniques… such scheduling problems certainly cannot be practically performed mentally”.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that the Applicant’s arguments that the claims cannot recite mental processes because they involve “NP-Hard problem” fundamentally misunderstands the USPTO’s mental process analysis. The 2025 memorandum makes clear that the relevant inquiry is not whether human can solve problems as efficiently or at the same scale as computers, but whether the claim limitations describe activities that can practically be performed in the human mind including “observations, evaluations, judgements, and opinions.” The fact that a problem is computationally complex or time-consuming for humans does not transform mental activities into non-abstract subject matter.
The claims recite: analyzing training data to identify workload patters, detecting an occurrence of a weather-related trigger, executing a workload, comparing the prediction with a current staffing schedule, and adjusting work schedules based on the comparison outcome. Each of these limitations describes mental evaluations and judgements that humans routinely perform when managing staffing. Workforce managers have analyzed historical weather patterns and staffing needs, predicted workload changes based on forecasted events, compared predicted needs against current schedules, and adjusted staffing assignments for decades before computer automation. The AI-SME Update Example 47 Claim 2 directly addresses this issue, finding claims ineligible despite involving artificial neural networks processing data that would be impractical for humans to analyze manually at the same scale and speed.
The Applicant’s NP-Hard complexity argument confuses computational difficulty with the nature of the operations being performed. Under MPEP 2106.04(a)(2) mental processes include “concepts performed in the human mind (including an observation, evaluation, judgement, opinion).” Scheduling decisions – determining which employees should work which shifts based on predicted workload – are quintessential mental process activities involving observation (reviewing current schedules and predicted needs), evaluation (assessing whether current staffing is sufficient), and judgement (deciding which workers to reassign). The fact the optimal solutions to complex scheduling problems may require substantial computation does not mean the activity of scheduling itself cannot be practically performed mentally. Humans make suboptimal but workable scheduling decisions every day through mental evaluation without achieving mathematically perfect solutions.
Moreover, the 2025 memorandum emphasizes that claims recite mental processes when limitations “can practically be performed in the human mind using observations, evaluation, judgement, and opinion” – not whether they can be performed optimally or at industrial scale. The claims here describe analyzing patterns in historical data (mental evaluation), predicting workload based on trigger characteristics (judgement based on experience), comparing predictions to schedules (observation and evaluation), and adjusting schedules (decision-making) – all activities within human mental capability. The complexity or volume of data involved does not change this fundamental analysis. The AI-SME Update confirms that invoking neural networks to perform these mental activities “without limiting how the trained (neural network) functions” and describing the system “at a high level” results in claims that recite abstract ideas of mental processes automated through generic ML technology, which is precisely what these claims do. The rejection is therefore maintained.
The Applicant argues on pages 11-12 that “the Applicant respectfully submits that each of the pending claims, when considered as a whole, has clearly been integrated into a practical application of an abstract idea regardless of the characterization of the abstract idea, and therefore each of the pending claims, as amended, is directed to statutory subject matter”.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that the Applicant’s conclusory assertion that the claims “have clearly been integrated into a practical application” fails to identify any specific claim limitations that accomplish this integration. The 2025 memorandum requires that “the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement” and emphasizes evaluating “whether the claim invokes computers or other machinery merely as a tool to perform an existing process, or whether the claim purports to improve computer capabilities or to improve an existing technology.” The claims here fall squarely into the former category – they automate the existing business process of workforce scheduling and reallocation using generic computer technology.
The addition elements beyond the recited abstracts ideas consist of: generic database detecting a weather related trigger, a generic neural network performing unspecified machine learning, generic processor and memory executing instructions, and generic communication to client devices. None of these elements, individually or in combination, demonstrate improvements to computer functionality or technology. The AI-SME Update Example 47 Claim 2 found nearly identical elements insufficient, explaining that when a neural network is “used to generally apply the abstract idea without limiting how the trained (neural network) functions” and is “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 practical application under MPEP 2016.05(f).
The core problem being solved is a business workforce management problem, not a computer technology problem. The specification describes helping employers “achieve sufficient levels of staffing” and “better plan workloads” – these are business optimization goals, not technological improvements. The claims provide no specific neural network architecture, no particular training algorithms with technical details, no novel database structures, no improvements to computational efficiency, and no technical constraints on how the system operates. Under the 2025 memorandum’s three-factor test: the claims recite only the idea of a solution (automated staffing adjustment) rather than a particular technical solution, computers and neural networks are invoked as tools to perform the existing workforce management process rather than improving computer capabilities and the application is general (any ML based staffing system) rather than particular with meaningful limitations.
The amended limitations regarding “detecting” weather trigger conditions and “executing” an ad hoc model do not change this analysis. “Detecting” the weather characteristics exceed thresholds is routine data comparison and threshold evaluation – conventional computer operations described generically. “Executing and ad hoc model using the neural network” amounts to “mere instructions to implement an abstract idea on a computer” under MPEP 2106.05(f) because it provides no technical details about how this execution improves computer functionality or differs from conventional ML models application. The AI-SME Update makes clear that simply changing “detecting” and “executing” does not transform generic computer implementation into practical application integration when the underlying operations remain described at high levels of generality without specific technical improvements. The rejection is therefore maintained.
The Applicant argues on pages 12-13 that “the claims adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present” or “simply appends well-understood, routine, conventional activities previously known to the industry”… the amended features of independent claims 1 and 11 as referenced below, and further submits that at least those amended “additional features” constitutes significantly more than the alleged abstract idea. Each of the pending claims, as amended, when interpreting the respective claim as a whole, recites “additional features” that constitutes a practical integration of the alleged abstract idea and/or significantly more than alleged abstract idea.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that the Applicant’s assertion that the amended features constitute “specific limitations that are not well-understood, routine, conventional activity” is contradicted by the claim language itself, which recites only generic, high-level function descriptions of conventional operations. Under MPEP 2016.05(d), Step 2B evaluates whether additional elements represent “well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality.” The claims here fail this analysis because every recited element represents standard computer operations described without technical specificity. The amended limitations – “detect an occurrence of a weather related trigger condition” and “execute, in response to detection… an ad hoc model using the neural network” – describe routine data monitoring an conventional ML model execution. “Detecting” that weather characteristics exceed predefined thresholds is standard threshold comparison logic that has been well-understood in computer systems for decades. “Executing” a model using a neural network is generic invocation of ML technology without any details about model architecture, training methodology, algorithmic implementation, or technical constraints that would distinguish this from any conventional neural network application. The AI-SME Update Example 47 Claim 2 directly addresses this type of limitation, finding ineligible at Step 2B claims that recite “training, by the computer, the ANN based on the input data and a selected training algorithm” because these were “well-understood, routine, conventional activity” described at a high level of generality.”
The Applicant has not identified what makes these limitations specific rather than generic. Claims that recite “using a neural network” or “detecting trigger conditions” without technical implementation details encompass any possible approach to neural network based workload prediction and weather monitoring – this is the definition of high-level generality. The 2025 memorandum emphasizes that eligible claims must include “a specific limitation other than what is well-understood, routine, conventional activity in the field.” Here, the claims provide: generic database operations – detecting, executing and storing data is routine, generic neural network training- analyzing training data to identify patterns is standard ML, generic threshold detection – comparing values to thresholds is conventional programming, generic model execution – applying trained models to new data is routine ML operation, generic schedule comparison and adjustment – basic business logic and generic communication to client devices – standard output operations.
The AI-SME Updated Example 47 Claims 2’s conclusion applies directly: “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.” The claims here similarly recite data gathering (receiving training data, detecting weather conditions), and output (communicating schedule adjustments) – all without technical specifics. Under Step 2B, “even when considered in combination, the additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept.” The fact that the applicant deleted certain limitations does not transform the remaining generic computer implementation into non-conventional subject matter – it simply removes potentially problematic language while leaving the fundamental deficiency of high-level generality unchanged. The rejection is therefore maintained.
The remaining Applicant's arguments filed 28 July 2025 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
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 1-4, 6, 8, 9, 11-14, 16, 18, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) 1-4, 6, 8, 9, 11-14, 16, 18, and 19 as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) is/are directed to the abstract idea of generating staff scheduling based on work patterns and adjusting based on compared predictions. The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself. Claim(s) (1-4, 6, 8, 9, 11-14, 16, 18, and 19) is/are directed to an abstract idea without significantly more.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes (from the January 2019 §101 Examination Guidelines), claim(s) (1-4, 6, 8, and 9) is/are directed to a system, claim(s) (11-14, 16, 18, and 19) is/ are directed to a method and therefore the claims recites a series of steps and, therefore the claims are viewed as falling in statutory categories.
Step 2A Prong 1
The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental process. Specifically, the independent claims 1, and 11 recite a mental process as drafted, the claim recites the limitation of generating staff scheduling based on work patterns and adjusting based on compared predictions which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a processor, nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for a processor language, the claim encompasses the user manually analyzing staff scheduling data and predict based on trigger conditions a number of policies impacted. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. This limitation is a mental process. While the Guidance provides that claims do not recite a mental process when they contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations (GPS position calculation, network monitoring, data encryption for communication, rendering images. However with regard to the instant application the Examiner has reviewed the disclosure and determined that the underlying claimed invention is described as a concept that is performed in the human mind and/or with the aid of a pen and paper, and thus it is viewed that the applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept, and therefore is considered to recite a mental process.
Note to the Applicant per the 2019 October Guidance: The 2019 PEG sets forth a test that distills the relevant case law to aid in examination, and does not attempt to articulate each and every decision. As further explained in the 2019 PEG, the Office has shifted its approach from the case-comparison approach in determining whether a claim recites an abstract idea and instead uses enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent. By grouping the abstract ideas, the 2019 PEG shifts examiners’ focus from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. In sum, the 2019 PEG synthesizes the holdings of various court decisions to facilitate examination.
Step 2A Prong 2
Specifically, the determined judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer additionally the data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity).
The claim recites the additional element(s): that a processor is used to perform the analyzing, identifying, and comparing steps. The processor in the steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (generating staff scheduling based on work patterns and adjusting based on compared predictions). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim recites the additional element(s): receiving training data, detecting an occurrence, execute a work load prediction, and transmitting a signal performs the analyzing, identifying, and comparing steps. The receiving, detecting, executing and transmitting steps are recited at a high level of generality (i.e., as a general means of transmitting data for use in the analyzing, identifying, and comparing steps), and amounts to mere data transmission, which is a form of insignificant extra-solution activity. The processor that performs the analyzing, identifying, and comparing steps is also recited at a high level of generality, and merely automates the analyzing, identifying, and comparing steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the processor).
The Examiner has further determined that the claims as a whole does not integrate a judicial exception into a practical application in order to provide an improvement in the functioning of a computer or an improvement to other technology or technical field. It has been determined that based on the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. It has not been provided clearly in the disclosure that the alleged improvement would be apparent to one of ordinary skill in the art, but is instead in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, and therefore does not improve the technology. Second, in the instance, which in this case it is not clear that the specification sets forth an improvement in technology, the claim must not reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification).
Note to the Applicant from the October 2019 Guidance: Generally, examiners are not expected to make a qualitative judgment on the merits of the asserted improvement. If the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 C.F.R. § 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification. For example, in response to a rejection under 35 U.S.C. § 101, an applicant could submit a declaration under § 1.132 providing testimony on how one of ordinary skill in the art would interpret the disclosed invention as improving technology and the underlying factual basis for that conclusion.
For further clarification the Examiner points out that the claim(s) 1-9 and 11-21 recite(s) receiving training data, analyzing training data, detecting an occurrence trigger, executing a work load prediction, comparing the work load prediction and transmitting a signal which are viewed as an abstract idea in the form of a mental process. This judicial exception is not integrated into a practical application because the use of a computer for receiving, analyzing, detecting, executing, comparing and transmitting which is the abstract idea steps of valuing an idea (using analyzed staffing information to create a work load prediction) in the manner of “apply it”.
Thus, the claims recite an abstract idea directed to a mental process (i.e. to generating staff scheduling based on work patterns and adjusting based on compared predictions). Using a computer to obtaining, classifying, quantifying, generation, and determining the data resulting from this kind of mental process merely implements the abstract idea in the manner of “apply it” and does not provide 'something more' to make the claimed invention patent eligible. The claimed limitations of a computing device is not constraining the abstract idea to a particular technological environment and do not provide significantly more.
The generating staff scheduling based on work patterns and adjusting based on compared predictions would clearly be to a mental activity that a company would go through in order to decide how to manage staffing. The specification makes it clear that the claimed invention is directed to the mental activity data gathering and data analysis to determine how to manage the scheduling of staffing schedules based on workload:
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’.
The dependent claims do not remedy these deficiencies.
Claims 2, 9, 12, and 19 recite limitations which further limit the claimed analysis of data.
Claims 3, 8, 13, and 18 recites limitations directed to claim language viewed insignificantly extra solution activity.
Using a computer to perform the data processing as claimed is merely implementing the abstract idea in the manner of “apply it” and does not provide significantly more. Additionally with respect to the Berkheimer the Examiner points out that the steps of the claim are viewed to be to nothing more than spell out what it means to apply it on a computer and cannot confer patent-eligibility as there are no additional limitations beyond applying an abstract idea, restricted to a computer. As the claims are merely implementing the abstract idea in the manner of “Apply It” the need for a Berkheimer analysis does not apply and is not required. With respect to the currently filed claims the implementing steps can be found in Datilio which discloses how the claims alone and in combination are viewed to be well understood, routine and conventional based on point 3 of the Berkheimer memo and subsequent evidence, complying with and providing evidence.
Claims 4, 6, and 14 recites limitations directed to claim language viewed non-functional data labels.
Thus, the problem the claimed invention is directed to answering the question based on gathered and analyzed information about the consumer what type of marketing is to be provided to the consumer. This is not a technical or technological problem but is rather in the realm of business or marketing management and therefore an abstract idea.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. This is the case because in order for the claims to be viewed as significantly more the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more in order for a machine to add significantly more, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more. Additionally, another consideration when determining whether a claim recites significantly more is whether the claim effects a transformation or reduction of a particular article to a different state or thing. "[T]ransformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines. All together the above analysis shows there is not improvement in computer functionality, or improvement to any other technology or technical field. The claim is ineligible.
With respect to the Berkheimer as noted above the same analysis applies to the 2B where the claims are viewed as applying it and as such no further analysis is required. However, with respect to the claims that are viewed as extra solution or post solution activity the Examiner notes that the claims are viewed as well-understood, routine, and conventional because, a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). An appropriate publication could include a book, manual, review article, or other source that describes the state of the art and discusses what is well-known and in common use in the relevant industry.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, the dependent claims do not remedy these deficiencies of the independent claims.
With respect to the legal concept of prima facie case being a procedural tool of patent examination, which allocates the burdens going forward between the examiner and the applicant. MPEP § 2106.07 discusses the requirements of a prima facie case of ineligibility. In particular, the initial burden was on the Examiner and believed to be properly provided as to explain why the claim(s) are ineligible for patenting because of the above provided rejection which clearly and specifically points out in accordance with properly providing the requirement satisfying the initial burden of proof based on the Guidance from the United States Patent and Trademark Office and the burden now shifts to the applicant.
Therefore, based on the above analysis as conducted based on the Guidance from the United States Patent and Trademark Office the claims are viewed as a court recognized abstract idea, are viewed as a judicial exception, does not integrate the claims into a practical application, and does not provide an inventive concept, therefore the claims are ineligible.
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) 1-3, 6-9, 11-13, and 16-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Datilio et al. (CN 116368505 A) (hereafter Datilio) in view of Ramanasankaran et al. (U.S. Patent Publication 2023/016220 A1) (hereafter Ramanasankaran).
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 (see; pg. 18, par. 2 Datilio teaches training a neural network to manage staffing, pg. 20, line 5 and based on activity triggers related to events, including pg. 5, par. 4 utilizing archived events to optimize the models used by the neural network).
at least one processor (see; pg. 3, par. 4 of Datilio teaches the use of 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; pg. 3, par. 4 of Datilio teaches memory that is utilized by a processor).
analyze, for continuous training of the neural network based on machine learning, the neural network training data to identify one or more work load patterns corresponding to one or more of the plurality of past trigger conditions (see; pg. 5, par. 4 of Datilio teaches continuous model improvement of employee staffing, pg. 5, par. 4, utilizing archived workload events in order to optimize, pg. 18, par. 2 as part of training of the neural network to manage staffing, pg. 5, par. 2 in order to optimize the scheduling model, pg. 20, par. 5 that is used by particular activity/trigger events (i.e. pattens)).
compare the work load prediction with a current staffing schedule and, based at least in part on an outcome of the comparison, adjust one or more work schedules of the current staffing schedule (see; pg. 5, par. 7 of Datilio teaches adjusting to meet workloads based on a workload prediction model and employee staffing model, pg. 6, par. 10 – pg. 7 that uses the data to fulfill staff demand (i.e. current schedule)).
automatically communicate the adjustment to the one or more work schedules to a client device (see; pg. 20, par. 5 of Datilio teaches based on activity/trigger events sending scheduling data to an API (i.e. client device)).
Datilio does not explicitly disclose the following limitation, however,
Ramanasankaran teaches detect an occurrence of a weather-related trigger condition associated with a forecasted weather event having at least one weather-related characteristic outside of one or more predefined thresholds (see; par. [0150]-[0151] of Ramanasankaran teaches an AI model used to utilize weather forecast models and rule based triggers to determine if a rule based trigger is met and then actions are initialized), and
execute, in response to detection of the weather-related trigger condition, an ad hoc model using the neural network to make a work load prediction, the work load prediction based in part on the continuous training of the neural network and the at least one weather-related characteristic of the forecasted weather event (see; par. [0150]-[0151] of Ramanasankaran teaches a neural networks models for making energy load predictions using deep learning and machine learning based on collected data to determine a forecast and making a prediction).
The Examiner notes that Datilio teaches similar to the instant application teaches scalable contact center seating arrangement with automatic AI modeling and multi-objective optimization. Specifically, Datilio discloses 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 it is therefore viewed as analogous art in the same field of endeavor. Additionally, Ramanasankaran teaches building data platform with digital twin based inferences and predictions for a graphical building model that provides the detecting of weather and as it is comparable in certain respects to Datilio which scalable contact center seating arrangement with automatic AI modeling and multi-objective optimization 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.
Datilio discloses 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. However, Datilio fails to disclose identifying a number of insurance policies in force anticipated to be impacted by the trigger condition, and the one or more characteristics of the trigger condition comprising the identified number of insurance policies in force anticipated to be impacted by the trigger condition.
Ramanasankaran discloses identifying a number of insurance policies in force anticipated to be impacted by the trigger condition, and the one or more characteristics of the trigger condition comprising the identified number of insurance policies in force anticipated to be impacted by the trigger condition.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Datilio identifying a number of insurance policies in force anticipated to be impacted by the trigger condition, and the one or more characteristics of the trigger condition comprising the identified number of insurance policies in force anticipated to be impacted by the trigger condition as taught by Ramanasankaran 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, Datilio, and Ramanasankaran 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 2, see discussion of claim 1 above, while Datilio in view of Ramanasankaran teaches the system above, Datilio further disclose a system having the limitations of:
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).
Referring to Claim 3, see discussion of claim 1 above, while Datilio in view of Ramanasankaran teaches the system above, Datilio further disclose a system having the limitations of:
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).
Referring to Claim 6, see discussion of claim 4 above, while Datilio in view of Ramanasankaran teaches the system above, Datilio further disclose a system having the limitations of:
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)).
Referring to Claim 8, see discussion of claim 7 above, while Datilio in view of Ramanasankaran teaches the system above, Datilio further disclose a system having the limitations of
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)).
Referring to Claim 9, see discussion of claim 1 above, while Datilio in view of Ramanasankaran teaches the system above, Datilio further disclose a system having the limitations of
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).
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).
Referring to Claim 11, Datilio in view of Ramanasankaran 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.
Referring to Claim 12, see discussion of claim 11 above, while Datilio in view of Ramanasankaran 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 Datilio in view of Ramanasankaran 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 Datilio in view of Ramanasankaran 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 Datilio in view of Ramanasankaran 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 Datilio in view of Ramanasankaran 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.
Claims 4, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Datilio et al. (CN 116368505 A) (hereafter Datilio) in view of Ramanasankaran et al. (U.S. Patent Publication 2023/0169220 A1) in further view of Johnston et al. (U.S. Patent 11,368,588 B1) (hereafter Johnston).
Referring to Claim 4 see discussion of claim 1 above, while Datilio in view of Ramanasankaran teaches the system above, Datilio further disclose a system having the limitations of:
the one or more databases comprise a historical database, and a current database (see; pg. 2, par. 10 – 11 of Datilio teaches using stored historical data (i.e. stored historical database) and current data (i.e. stored current database)).
Datilio does not explicitly disclose the following limitation, however,
Johnston teaches weather database (see; col. 15, lines (1-21) of Johnston teaches the handling of unanticipated changes that trigger different requirements for the call center including data that pertains to weather data (i.e. weather database)).
The Examiner notes that Datilio teaches similar to the instant application teaches scalable contact center seating arrangement with automatic AI modeling and multi-objective optimization. Specifically, Ramanasankaran teaches building data platform with digital twin based inferences and predictions for a graphical building model that provides the detecting of weather and as it is comparable in certain respects to Datilio which scalable contact center seating arrangement with automatic AI modeling and multi-objective optimization 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, Johnston teaches dynamic communication routing at contact centers including scheduling based on unanticipated changes and as it is comparable in certain respects to Datilio which scalable contact center seating arrangement with automatic AI modeling and multi-objective optimization 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.
Datilio and Ramanasankaran discloses 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. However, Datilio fails to disclose weather database.
Johnston discloses weather database.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Datilio and Ramanasankaran the weather database as taught by Johnston 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, Datilio, Ramanasankaran, and Johnston 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 14, see discussion of claim 11 above, while Datilio in view of Ramanasankaran teaches the method above Claim 14 recites the same or similar limitations as those addressed above in claim 4, Claim 14 is therefore rejected for the same or similar limitations as set forth above in claim 4.
Conclusion
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.
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/SSS/
Patent Examiner, Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623