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 .
Introduction
The following is a final Office Action in response to Applicant’s communications received on May 18, 2026. No claim has been amended.
Currently claims 1-20 are pending, claims 1, 8 and 15 are independent.
Response to Arguments
Applicant’s arguments filed on May 18, 2026 have been fully considered but are not persuasive.
In the Remarks on page 6, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that the claims are not directed to an abstract idea. Rather, the claims explicitly recite that the schedule comprises “one or more computer tasks to be executed”.
In response to Applicant’s argument, the Examiner respectfully disagrees. Claim 1 recites “optimizing a time off rule…, updating a schedule… one or more computer tasks to be executed, and executing one or more of the tasks”, which are abstract ideas categorized as certain methods of organizing human activity because they are concepts for managing tasks execution and that the claim is directed to managing relationships between the tasks provider and the tasks executor, based on the time off rule. The Specification in paragraph [0032] describes that “scheduling, which may involve creating and managing work schedules and associated tasks to align with various constraints such as for example, demand, resource or employee availability, and regulatory requirements, and, e.g., to maximize productivity while minimizing costs.” The courts have held similar concepts to be abstract. For example, the Federal Circuit held abstract the concepts of “personal management, resource planning, or forecasting” in In re Downing, 754 F. App’x 988, 993 (Fed. Cir. 2018), generating tasks based on rules to be completed upon the occurrence of an event in Accenture Global Services, GmbH v. Guidewire Software, Inc., 728 F.3d 1336, 1344 (Fed. Cir. 2013), and “scheduling business activities using a computer and computer network” in P & RO Solutions Group, Inc. v. CiM Maintenance, Inc., 273 F. Supp. 3d 699, 708 (E.D. Tex. 2017).
In the Remarks on page 7, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that the Examiner has oversimplified the claims by characterizing them at a high level of generality as “methods that allow user to manage task scheduling and follow rules or instructions.”
In response to Applicant’s argument, the Examiner respectfully disagrees. As can be seen on page 3-4 of the previous Office Action, the Examiner identified the specific limitations that recited the abstract idea as required by the Revised Guidance, 84 Fed. Reg. at 54. Besides, there is no requirement to recite limitations verbatim in their entirety. An abstract idea can generally be described at different levels of abstraction.” Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240 (Fed. Cir. 2016).
In the Remarks on page 7, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that even assuming arguendo that the claims recite an abstract idea, the claims integrate any alleged exception into a practical application because they produce a tangible result, i.e., the actual execution of computer tasks based on the updated schedule.
In response to Applicant’s argument, the Examiner respectfully disagrees. In order for a claim to integrate the exception into a practical application, the additional claimed elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), affect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)). See Revised 2019 Guidance. Here, claim 1 recites the additional elements of “one or more computer processors”. The Specification describes that “the computing device 100 may include a controller or computer processor 105 that may be, for example, a CPU, a chip or any suitable computing device, an operation system115, a memory120, a storage 130, input devices 135 and output devices 140 such as a computer display or monitor displaying for example a computer desktop system.”(See ¶ 24). When given the broadest reasonable interpretation and in light of the Specification, these additional elements are no more than generic computer components to perform generic computer functions (i.e., executing, receiving, transmitting), none of them reflect an improvement to the functioning of a computer. Merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014). Therefore, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application.
In the Remarks on page 9, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that the claims recite significantly more. The ordered combination of elements recited in the claims. i.e., calculating a utilization indicator from specific inputs, changing a quota based on that indicator, updating a schedule of computer tasks, and executing those tasks, is not well-understood, routine, or conventional.
In response to Applicant’s argument, the Examiner respectfully disagrees. Step 2B is to determine whether any “inventive concept” which can transform the abstract idea into a patent-eligible invention. The “inventive concept” may arise in one or more of the individual claim limitations or in the ordered combination of the limitations. Alice, 134 S. Ct. at 2355. An “inventive concept” that transforms the abstract idea into a patent-eligible invention must be significantly more than the abstract idea itself, and cannot simply be an instruction to implement or apply the abstract idea on a computer. Id. at 2358.
In the present case, beyond the abstract idea, claim 1 recites the additional elements of “one or more computer processors”. The Specification describes that “the computing device 100 may include a controller or computer processor 105 that may be, for example, a CPU, a chip or any suitable computing device, an operation system115, a memory120, a storage 130, input devices 135 and output devices 140 such as a computer display or monitor displaying for example a computer desktop system.”(See ¶ 24). When given the broadest reasonable interpretation and in light of the Specification, the one or more computer processors are recited at a high level of generality that simply perform generic functions, including access and execute instructions stored in the memory. In this regard, the courts have repeatedly held that such invocations of computers and networks that are not even arguably inventive are “insufficient to pass the test of an inventive concept in the application” of an abstract idea, buySAFE, 765 F.3d at 1353, 1355. A transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering or in a field-of-use limitation) would not provide significantly more than the judicial exception. See MPEP 2106.05(a)-(c), (e-f) & (h).
In the Remarks on page 11, Applicant argues that Sarlay does not disclose calculating any utilization indicator that compares utilized time off against a quota of time off units.
In response to Applicant’s argument, the Examiner respectfully disagrees. Sarlay discloses determining how many agents can take time off on a given day for the time off rules, and the time off rules can be based on certain skills, shifts or sites can manage the availability of time off independently. Agents or supervisors can access display screens on designated workstations to make or modify selections (see ¶ 6, ¶ 26); as each agent receives a holiday schedule, the fairness rotation for the remaining holiday days in the week is recalculated if the sort options for the remaining holiday days include selections that would change by nature of the agent being scheduled for a holiday day (see ¶ 47).
In the Remarks on page 11, Applicant argues that Fama also fails to teach updating a schedule based on an optimized time off rule as recited by claim 1.
In response to Applicant’s argument, the Examiner respectfully disagrees. Fama discloses the schedule engine may prompt agents to provide any time-off requests or schedule change requests that they may have; the schedule engine may further prompt the administrator to update or make any changes to the work rules associated with the contact center. The interfacing and prompts for updates and change may be make immediately prior to the schedule engine generating the schedule (see ¶ 40-41).
In the Remarks on page 12, Applicant argues that Sarlay does not disclose identifying one or more rules matching a time off rule based on activity codes as recited by claim 2.
In response to Applicant’s argument, the Examiner respectfully disagrees. Sarlay discloses the fairness processing allows the contact center manager to set up days to be considered as fairness days and to set up rules to govern which agents will be scheduled for those days. Agents may optionally indicate a preference to work on a fairness day or may indicate a desire to be scheduled off for a fairness day. The rule can be configured such that different processing occurs depending on whether more agents have volunteered to work than are need, or whether fewer agents have volunteered to work than needed. The system will choose which agents should be scheduled in addition to the volunteers. The agents selected can be based on numerous factors, including which agents worked most recently, which agents have worked more fairness days than other agents and various seniority and ranking orders of the agents (see ¶ 30); Sarlay also discloses the type of activities in the screen in Fig. 3A comprises a data entry table having a number of rows and columns. A Holiday Date drop down list box enables the user to identify an earliest data for which to consider holiday history, A ”Holiday Date” column identifies the holidays that are capable of being defined as “Open” or “Closed” for the retrieved management unit shown in the display tab. A “Day” column indicates the day of the week corresponding to the Holiday Date. A “Holiday Description” column provides a description corresponding to the Holiday Date, and the “Holiday Type” column provides an associated type (see ¶ 39).
Sarlay discloses that the functions are performed by one or more processors executing given software (see ¶ 28). Sarlay does not explicitly disclose “by a machine learning model”. However, Fama discloses the IVA systems are more advanced and utilize AI and machine learning technologies to simulate live and unstructured cognitive conversations.
In the Remarks on page 13, Applicant argues that there is no teaching in Sarlay of automatically generating a name for a rule as recited by claim 3.
In response to Applicant’s argument, the Examiner respectfully disagrees. Sarlay discloses a weekend fairness policy represents the time off on a weekend (see Abstract; ¶ 10); and a holiday fairness policy represents the time off on a holiday (see ¶ 11).
Therefore, given the broadest reasonable interpretation to one of ordinary skill in the art, Sarlay and in view of Fama and Viraraghavan teaches the limitation in the form of Applicant claimed.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1-7 and 15-20 are directed to methods for scheduling and time management using one or more computer processors to perform the steps, which falls within the statutory category of a process. However, using one or more computer processor in a method is more like instructing a user to follow instructions using one or more computer processors. Claims 8-14 are directed to a system comprising a memory and one or more processor, which falls within the statutory category or a machine.
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking the method claims as representative, the claims recite the limitations of “optimizing a time off rule by changing the quota of time off units based on calculating a time off utilization indicator based on a utilized time off value and the quota of time off units, updating a schedule based on the optimized time off rule, executing one or more of the tasks based on the updated schedule, identifying one or more rules matching the time of rule based on activity codes for the rule similar to the time off rule, performing one or more of deleting one or more of the identified rule, and merging the time off rule with one or more of the identified rules, generating a rule name for the optimized time off rule, approving or rejecting a time off request based on the optimized time off rule, matching the time off rule comprises clustering one or more of the rules into one or more cluster, updating the schedule are performed using a cloud platform”. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are methods that allow user to manage task scheduling and follow rules or instructions, which falls within the certain methods of organizing human activity grouping. The mere nominal recitation of “using one or more computer processors” and “by a machine learning model” do not take the claim out of the certain methods of organizing human activity grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, the claims recite the additional elements of “one or more computer processors”, “a memory”, “a machine learning model”, “a cloud platform”, “a cloud based database”, and the term “automatically”. The Specification describes that “the computing device 100 may include a controller or computer processor 105 that may be, for example, a CPU, a chip or any suitable computing device, an operation system115, a memory120, a storage 130, input devices 135 and output devices 140 such as a computer display or monitor displaying for example a computer desktop system.”(See ¶ 24). When given the broadest reasonable interpretation and in light of the Specification, these additional elements are no more than generic computer components. The additional elements are recited at a high level of generality and merely involved at tools to perform generic computer functions including receiving, manipulating, and transmitting information over a network. Reciting “a machine learning model”, without training in some specific way with technical implementation details, is merely adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. The Supreme court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). As to learning per se, such an argument overlooks the entire education system. Reciting a machine learning is placing such learning in a computer context, offering no technological implementation details beyond the conceptual idea to use a machine for learning. With respect to the team “Automatically”, the courts have held that “Automating manual and mental processes on generic computers does not make an abstract idea patent eligible.” See Credit Acceptance Corp. v. Westlake Servs., 859 F.3d 1044, 1055 (Fed. Cir. 2017) (“[A]utomation of manual processes using generic computers does not constitute a patentable improvement in computer technology.”). The Federal Circuit has also indicated that mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016). However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and nothing in the claims that reflects an improvement to the functioning of a computer itself or another technology. Therefore, the additional elements do not integrate the judicial exception into a practical application. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, the claims recite the additional elements of “one or more computer processors”, “a memory”, “a machine learning model”, “a cloud platform”, “a cloud based database”, and the term “automatically”. The Specification describes that “the computing device 100 may include a controller or computer processor 105 that may be, for example, a CPU, a chip or any suitable computing device, an operation system115, a memory120, a storage 130, input devices 135 and output devices 140 such as a computer display or monitor displaying for example a computer desktop system.”(See ¶ 24). When given the broadest reasonable interpretation and in light of the Specification, these additional elements are no more than generic computer components. The additional elements are recited at a high level of generality and merely involved at tools to perform generic computer functions including receiving, manipulating, and transmitting information over a network. Taking the claim elements separately and as an ordered combination, the additional elements, at best, may perform the step of fetching staffing data from a cloud based database. However, using generic computer components for fetching (retrieving) staffing data from a cloud based database have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1-7 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other claims 8-14 and 15-20 parallel claims 1-7—similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sarlay, (US 2008/0300953), and in view of Fama et al., (US 2021/0158267, hereinafter: Fama), and further in view of Viraraghavan et al., (US 2021/0157484, hereinafter: Viraraghavan).
Regarding claim 1, Sarlay discloses the a computerized method for computerized task scheduling and execution, comprising, using one or more computer processors (see ¶ 28, ¶ 57):
optimizing (enhancing) a time off rule, the rule comprising a quota of time off units for a time period, wherein the optimizing comprises changing the quota of time off units based on calculating a time off utilization indicator, the calculating of the time off utilization indicator based on a utilized time off value and the quota of time off units (see ¶ 5-6, ¶ 10, ¶ 13, ¶ 26, ¶ 47-48).
Sarlay does not explicitly disclose the following limitations; however, Fama in an analogous for workforce management discloses
updating a schedule based on the optimized time off rule, wherein the schedule comprises one or more computer tasks to be executed (see ¶ 40-41, ¶ 64).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Fama discloses the campaign management may be performed by an application to design, schedule, and execute the outbound campaigns (see ¶ 85).
Sarlay and Fama do not explicitly disclose the following limitations; however, Viraraghavan in an analogous for managing tasks discloses
executing one or more of the tasks based on the updated schedule (see ¶ 80).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay and in view of Fama to include teaching of Viraraghavan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing the schedule implementation, in turn of task execution efficiency. Since the combination of 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.
Regarding claim 2, Sarlay discloses the method of claim 1, wherein optimizing of the time off rule comprises:
identifying, by a machine learning model, one or more rules matching (satisfying, meeting) the time off rule, the identifying based on activity codes for the rules similar to the time off rule (see ¶ 24, ¶ 33, ¶ 36, ¶ 39, ¶ 50); and
performing one or more of: deleting one or more of the identified rules, and merging the time off rule with one or more of the identified rules (see Abstract; ¶ 12).
Sarlay does not explicitly disclose a machine learning model, however, Fama discloses
a machine learning model (see ¶ 83).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 3, Sarlay discloses the method of claim 2, comprising generating, by the machine learning model, a rule name for the optimized time off rule (see Abstract; ¶ 10, ¶ 37).
Sarlay does not explicitly disclose a machine learning model, however, Fama discloses
the machine learning model (see ¶ 83).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 4, Sarlay discloses the method of claim 1, wherein the optimizing of the time off rule and the updating of the schedule are performed based on a staffing requirement, the requirement comprising a number of resources needed for the time period (see ¶ 29-30, ¶ 36, ¶ 45).
Regarding claim 5, Sarlay discloses the module enables a system administrator the ability to streamline vacation and holiday planning and rules-based approvals (see ¶ 26).
Sarlay does not explicitly disclose the following limitations; however, Fama disclose the method of claim 1, comprising automatically approving or automatically rejecting a time off request based on the optimized time off rule, wherein the updating of the schedule is performed based on the automatically approved or the automatically rejected time off request (see ¶ 64, ¶ claim 10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 6, Sarlay discloses the method of claim 2, wherein the identifying of the one or more rules matching the time off rule comprises clustering, by the machine learning model, one or more of the rules into one or more clusters, wherein the one or more rules matching the time off rule are included in one of the clusters including the time off rule (see ¶ 13, ¶ 41, ¶ 48).
Sarlay does not explicitly disclose a machine learning model, however, Fama discloses
the machine learning model (see ¶ 83).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 7, Sarlay does not explicitly the following limitations; however, Fama discloses the method of claim 4, wherein the optimizing of the time off rule and the updating of the schedule are performed using a cloud platform, and wherein the optimizing of the time off rule comprises fetching staffing data from a cloud based database (see ¶ 5, ¶ 15, ¶ 84).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 8, Sarlay discloses a computerized system for intelligent computerized task scheduling and execution, comprising:
a memory (see ¶ 57); and
one or more processors (see ¶ 28, ¶ 57) configured to:
optimize a time off rule, the rule comprising a quota of time off units for a time period, wherein the optimizing comprises changing the quota of time off units based on calculating a time off utilization indicator, the calculating of a utilization indicator based on a utilized time off value and the quota of time off units (see ¶ 5-6, ¶ 10, ¶ 13, ¶ 26, ¶ 47-48).
Sarlay does not explicitly disclose the following limitations; however, Fama in an analogous for workforce management discloses
update a schedule based on the optimized time off rule, wherein the schedule comprises one or more computer tasks to be executed (see ¶ 4041, ¶ 64).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Fama discloses the campaign management may be performed by an application to design, schedule, and execute the outbound campaigns (see ¶ 85).
Sarlay and Fama do not explicitly disclose the following limitations; however, Viraraghavan in an analogous for managing tasks discloses
execute one or more of the tasks based on the updated schedule (see ¶ 80).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay and in view of Fama to include teaching of Viraraghavan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing the schedule implementation, in turn of task execution efficiency. Since the combination of 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.
Regarding claim 9, Sarlay discloses the system of claim 8, wherein the optimizing of the time off rule comprises:
identifying, by a machine learning model, one or more rules matching the time off rule, the identifying based on activity codes for the rules similar to the time off rule (see ¶ 24, ¶ 33, ¶ 36, ¶ 39, ¶ 50); and
performing one or more of: deleting one or more of the identified rules, and merging the time off rule with one or more of the identified rules (see Abstract; ¶ 12).
Sarlay does not explicitly disclose a machine learning model, however, Fama discloses
a machine learning model (see ¶ 83).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 10, Sarlay discloses the system of claim 9, wherein one or more of the processors are to generate, by the machine learning model, a rule name for the optimized time off rule (see Abstract; ¶ 10, ¶ 37).
Sarlay does not explicitly disclose a machine learning model, however, Fama discloses
the machine learning model (see ¶ 83).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 11, Sarlay discloses the system of claim 8, wherein the optimizing of the time off rule and the updating of the schedule are performed based on a staffing requirement, the requirement comprising a number of resources needed for the time period (see ¶ 29-30, ¶ 36, ¶ 45).
Regarding claim 12, Sarlay discloses the module enables a system administrator the ability to streamline vacation and holiday planning and rules-based approvals (see ¶ 26).
Sarlay does not explicitly disclose the following limitations; however, Fama disclose the method of claim 1, comprising automatically approving or automatically rejecting a time off request based on the optimized time off rule, wherein the updating of the schedule is performed based on the automatically approved or the automatically rejected time off request (see ¶ 64, ¶ claim 10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 13, Sarlay discloses the system of claim 9, wherein the identifying of the one or more rules matching the time off rule comprises clustering, by the machine learning model, one or more of the rules into one or more clusters, wherein the one or more rules matching the time off rule are included in one of the clusters including the time off rule (see ¶ 13, ¶ 41, ¶ 48).
Sarlay does not explicitly disclose a machine learning model, however, Fama discloses
the machine learning model (see ¶ 83).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 14, Sarlay does not explicitly the following limitations; however, Fama discloses the system of claim 11, wherein the optimizing of the time off rule and the updating of the schedule are performed using a cloud platform, and wherein the optimizing of the time off rule comprises fetching staffing data from a cloud based database (see ¶ 5, ¶ 15, ¶ 84).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 15, Sarlay discloses a computerized method for intelligent computerized time off management, comprising, using one or more computer processors (see ¶ 28, ¶ 57):
optimizing a downtime rule, the rule comprising an amount of downtime units for a time period, wherein the optimizing comprises changing the amount of downtime units based on computing a downtime utilization score, the computing of a utilization score based on a utilized downtime value and the amount of downtime units (see ¶ 5-6, ¶ 10, ¶ 13, ¶ 26, ¶ 47-48).
Sarlay does not explicitly disclose the following limitations; however, Fama in an analogous for workforce management discloses
updating a schedule based on the optimized downtime rule, wherein the schedule comprises one or more computer operations to be executed (see ¶ 40-41, ¶ 64).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Fama discloses the campaign management may be performed by an application to design, schedule, and execute the outbound campaigns (see ¶ 85).
Sarlay and Fama do not explicitly disclose the following limitations; however, Viraraghavan in an analogous for managing tasks discloses
executing one or more of the computer operations based on the updated schedule (see ¶ 80).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay and in view of Fama to include teaching of Viraraghavan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing the schedule implementation, in turn of task execution efficiency. Since the combination of 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.
Regarding claim 16, Sarlay discloses the method of claim 15, wherein the optimizing of the downtime rule comprises:
identifying, by a large language model (LLM), one or more rules matching the downtime rule, the identifying based on identifiers for the rules similar for the downtime rule (see ¶ 24, ¶ 33, ¶ 36, ¶ 39, ¶ 50); and
performing one or more of: deleting one or more of the identified rules, and merging the downtime rule with one or more of the identified rules (see Abstract; ¶ 12).
Sarlay does not explicitly disclose a machine learning model, however, Fama discloses
a machine learning model (see ¶ 83).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 17, Sarlay discloses the method of claim 16, comprising generating, by the LLM, a rule name for the optimized downtime rule (see Abstract; ¶ 10, ¶ 37).
Sarlay does not explicitly disclose a machine learning model, however, Fama discloses
the machine learning model (see ¶ 83).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 18, Sarlay discloses the method of claim 15, wherein the optimizing of the downtime rule and the updating of the schedule are performed based on a staffing requirement, the requirement comprising a number of resources needed for the time period (see ¶ 29-30, ¶ 36, ¶ 45).
Regarding claim 19, Sarlay discloses the module enables a system administrator the ability to streamline vacation and holiday planning and rules-based approvals (see ¶ 26).
Sarlay does not explicitly disclose the following limitations; however, Fama discloses the method of claim 15, comprising automatically approving or automatically rejecting a downtime request based on the optimized downtime rule, wherein the updating of the schedule is performed based on the automatically approved or the automatically rejected downtime request (see ¶ 64, ¶ claim 10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Regarding claim 20, Sarlay discloses the method of claim 16, wherein the identifying of the one or more rules matching the downtime rule comprises grouping, by the LLM, one or more of the rules into one or more groups, wherein the one or more rules matching the downtime rule are included in one of the groups including the downtime rule (see ¶ 13, ¶ 41, ¶ 48).
Sarlay does not explicitly disclose a machine learning model, however, Fama discloses
the machine learning model (see ¶ 83).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarlay to include teaching of Fama in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal scheduling solution, in turn of operational efficiency. Since the combination of 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vanamala et al., (US 2017/0116577) discloses a system for management human resource scheduling rules and in a graphic user interface.
Aabachrim , (WO 2025206954) discloses a method for analyzing and quantifying the quality of a first user input in relation to a second user input by comparison to a third user input in a computer system.
Pachon et al., (US 2008/0215407) discloses a method for providing functionality that allows multiple different users to accomplish schedule changes in a manner that minimizes conflicts between different schedules.
Yiqiu et al., “Task Scheduling Strategy for Cloud Computing Based on the Improvement of Ant Colony Algorithm”, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC).
Annov et al., “Cloud Based Algorithm for Task Management”, 2019 IEEE Internation Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computer (EUC).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PAN G CHOY/Primary Examiner, Art Unit 3624
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