DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office Action for Application Serial Number: 18/357,388, filed on July 24, 2023. In response to Examiner’s Non-Final Rejection dated October 06, 2025, Applicant on December 31, 2025, amended claims 1, 3, 5, 6 and 13-20. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
The 35 U.S.C. § 102 rejections of claims 1, 2, 4, 5, 10, 11, 15 and 18 are hereby withdrawn in light of Applicant’s amendments to claims 1, 15 and 18. New 35 U.S.C. § 103 rejections have been applied to amended claims 1, 2, 4, 5, 10, 11, 15 and 18.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to Applicants amendments to claims 1, 15 and 18.
Response to Arguments
Applicant's Arguments/Remarks filed December 31, 2025 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks regarding the pending rejections will be addressed herein below in the order in which they appear in the response filed December 31, 2025.
Regarding the 35 U.S.C. 101 rejection, Applicant submits that previously-presented independent claims 1, 15 and 18 "when read as a whole" are instead directed to "a specific means or method that improves the relevant technology." See Contour IP Holding LLC v. GoPro, Inc., 2024 U.S. App. LEXIS 22825 (Fed. Cir. 2024), citing McRO, Inc., v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314 (Fed. Cir. 2016).
As shown in the recently-issued 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence ("July 2024 Guidance Update") (see p. 11-12, Applicant Remarks).
In view of the above portions of the July 2024 Updated Guidance, Applicant submits that previously-presented independent claim 1 cannot reasonably be said to be directed to certain methods of organizing human activity. The claims are not directed to "managing personal behavior or relationships or interactions between people." Previously-presented independent claims 1, 15 and 18 include recitations of particular processing operations including establishing data feeds, generating data structures comprising filtered subsets of assets eligible to be selected for inclusion in a given instance of a rotation schedule by parsing information from the data feeds by parsing information from the data feeds, and generating the given instance of the rotation schedule by selecting from among a filtered subset of assets which are eligible to be selected for inclusion. Such recitations of particular processing operations are clearly not simply "methods based on business relations" or "managing personal behavior or relationships or interactions between people" as alleged.
In response, Examiner respectfully disagrees and finds Applicants remarks are directed toward the data analysis regarding the generation of the rotation schedules based on the data analysis of asset eligibility and schedule requirements. Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity processing and assessment of data to generate a schedule without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Examiner finds the aforementioned limitations constitute methods that mimic human thought processes that can be performed mentally by a combination of the human mind and a human using pen and paper. As stated in the 35 U.S.C. 101 rejection, the recitation of the additional elements do not take the claim out of the certain methods of organizing human activity and mental processes groupings. Examiner maintains the claims recite an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states as detailed in the August 4, 2025 memorandum from Charles Kim, Deputy Commissioner for Patents, titled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" (hereinafter "the August Memorandum"), "a claim recites a mental process when it contains limitation(s) that can practically be performed in the human mind, including, for example, observations, evaluations, judgements, and opinions." The August Memorandum further indicates that "a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s)." The August Memorandum further indicates that the "mental process grouping is not without limits" and that "Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind." In the present application, previously-presented independent claims 1, 15 and 18 include recitations of particular processing operations including establishing data feeds, generating data structures comprising filtered subsets of assets eligible to be selected for inclusion in a given instance of a rotation schedule by parsing information from the data feeds by parsing information from the data feeds, and generating the given instance of the rotation schedule by selecting from among a filtered subset of assets which are eligible to be selected for inclusion. Such recitations of particular processing operations are clearly not mental processes as alleged, as the human mind is not equipped to perform these claim limitations.
In response, Examiner respectfully disagrees. Examiner finds even in a computer environment, these limitations are still considered abstract by reciting limitations that mimic human thought processes of observation, evaluations, judgement and opinion, that can feasibly be performed with pen and paper, where the data interpretation is perceptible in the human mind. Claims can recite a mental process even if they are claimed as being performed on a computer; see MPEP 2106.04(a)(2)(III)(C). Examiner finds the pending claims recite similar limitations to claims the courts have indicated may not be sufficient in showing an improvement in computer-functionality, such as accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; see MPEP 2106.05(a)(I) and MPEP 2106.05(a)(II). Examiner maintains the claims are directed to an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states the Examiner further alleges with regard to Step 2B of the above USPTO analysis framework that previously-presented independent claims 1, 15 and 18 do not include additional elements that are sufficient to amount to significantly more than the alleged abstract idea. Applicant traverses, and submits that previously-presented independent claims 1, 15 and 18 clearly recite arrangements providing an improvement in computer technology. For example, illustrative embodiments of the claimed arrangements provide improvements in the functioning of a computer, and more particularly in the functioning of information processing systems as described in the specification at, for example, page 21, line 23 to page 22, line 21 (see p. 13-14, Applicant Remarks).
Illustrative embodiments of the claimed arrangements thus provide significant technical
advantages relative to conventional approaches that rely on manual effort, such as by a user maintaining a spreadsheet and manually selecting users, as such approaches are fraught with issues as described in the specification at, for example, page 14, lines 19-29 (see p. 14, Applicant Remarks).
Accordingly, even if one assumes for purposes of argument only that previously-presented independent claims 1, 15 and 18 could somehow be construed as reciting an abstract idea, these claims are not directed to an abstract idea as previously-presented independent claims 1, 15 and 18 clearly integrate any such abstract idea into practical applications that provide improvements in computer technology.
It is therefore respectfully submitted that the §101 rejection is improper and should be withdrawn.
In response, Examiner respectfully disagrees. Examiner finds all Applicants remarks and cited paragraphs from the specifications are directed towards improving an existing business process (e.g., schedule management) and not improvements to a technology, technological field or computer related technology. Examiner notes the analysis in Step 2B addresses the question on whether an additional element (or combination of additional elements) represents well-understood, routine and/or conventional activities. Examiner finds Applicant is attempting to say the Step 2A-Prong One elements, the abstract idea, is what makes the claim eligible. Applicant has not identified any disclosure in the claimed invention showing and/or submitting that the technology used is being improved, there was a technical problem in the technology that the claimed invention solves, or the ordered combinations of the known elements is significantly more than instructions used to generate rotation schedules based on the data analysis of asset eligibility and schedule requirements. Examiner maintains the additional elements recited in the claims do not perform any unconventional functions that can be considered “significantly more” than the judicial exception. Therefore, Examiner maintains the claims recite additional elements used as tools to perform the instructions of the abstract idea without disclosing limitations that integrate the abstract idea into a practical application, nor do these elements provide meaningful limitations that transforms the judicial exception into significantly more than the abstract idea itself. For at least these reasons, the pending claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Applicant’s arguments, see pg. 15-16, filed December 31, 2025, with respect to the rejection(s) of claims 1-20 under 35 U.S.C. 102/103 have been fully considered. However, upon further consideration, a new ground(s) of rejection is made. Applicant’s arguments are considered moot because they are directed to newly amended subject matter and do not apply to the combination of references being used in the current rejection. Please refer to the 35 U.S.C. 103 rejection for further explanation and rationale.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-7 are directed towards a device, claims 8-14 are directed towards a method and claims 15-20 are directed towards a non-transitory computer-readable medium, which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite generating rotation schedules based on the data analysis of asset eligibility and schedule requirements.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, receive a request to generate a given instance of a rotation schedule for rotation of a plurality of assets of an organization among two or more different groups associated with two or more different functional areas of the organization; establish one or more data feeds, each of the one or more data feeds comprising information affecting eligibility of a plurality of assets for inclusion in the given instance of the rotation schedule; generate one or more data structures by parsing information from the one or more data feeds, the one or more data structures comprising a filtered subset of the plurality of assets which are eligible to be selected for inclusion in the given instance of the rotation schedule wherein at least one of the one or more data feeds comprises information characterizing (i) which ones of the plurality of assets of the organization were selected for inclusion in one or more previous instances of the rotation schedule and (ii) which of the two or more different groups that the ones of the plurality of assets of the organization that were selected for inclusion in the one or more previous instances of the rotation schedule belong to; and generate the given instance of the rotation schedule by selecting, from among the filtered subset of the plurality of assets which are eligible to be selected for inclusion in the given instance of the rotation schedule, one or more of the plurality of assets for the given instance of the rotation schedule, wherein selecting the one or more of the plurality of assets for the given instance of the rotation schedule is based at least in part on information from at least one of the one or more data feeds characterizing requirements related to composition of the given instance of the rotation schedule and requirements related to rotating selection of assets from the two or more different groups in consecutive instances of the rotation schedule; and to control execution of the given instance of the rotation schedule constitutes methods based on business relations and managing personal behavior or relationships or interactions between people, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of an apparatus comprising a processing device comprising a processor coupled to a memory does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea. Claims 15 and 18 recite certain method of organizing human activity and mental processes for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites an apparatus comprising a processing device comprising a processor coupled to a memory at a high-level of generality such that it amounts to no more than generic computer components used as a tool to apply the instructions of the abstract idea; see MPEP 2106.05(f). Thus, the additional element do not integrate the abstract idea into practical application because it does not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs executable by a processing device recited in claim 15 and processing device comprising a processor coupled to a memory in claim 18 also amount to no more than mere instructions to apply the exception using generic computer components; see MPEP 2106.05(f). Thus, the additional elements recited in claims 15 and 18 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including an apparatus comprising a processing device comprising a processor coupled to a memory and computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs executable by a processing device amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claim 4 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claim 12 recites publish the given instance of the rotation schedule which is considered an insignificant extra-solution activities of collecting and delivering data; see MPEP 2106.05(g). Claims 2, 3, 16 and 19 recite additional elements, which merely recites generic computer components used as tools to apply the instructions of the abstract idea; MPEP 2106.05(f). Additionally, claims 5-14, 17 and 20 recite steps that further narrow the abstract idea. Therefore claims 2-14, 16, 17, 19 and 20 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.
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 (i.e., changing from AIA to pre-AIA ) 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, 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, 2, 4, 5, 10, 11, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over D'Attilio et al., U.S. Publication No. 2022/0027837 [hereinafter D’Attilio], and further in view of Garcia et al., U.S. Publication No. 2008/0086500 [hereinafter Garcia].
Referring to Claim 1, D’Attilio teaches:
An apparatus comprising:
at least one processing device comprising a processor coupled to a memory (D’Attilio, [0042]; [0142]); the at least one processing device being configured:
to receive a request to generate a given instance of a rotation schedule (D’Attilio, [0053]), “the system receives an API request is made (e.g., for an optimized contact center agent schedule)”; (D’Attilio, [0072]), “a system may execute a method 700 of processing a staffing requirement forecasting request”; (D’Attilio, [0075]; [0083]; [0077]; [0048]; [0050]);
to establish one or more data feeds, each of the one or more data feeds comprising information affecting eligibility of the plurality of assets of the organization for inclusion in the given instance of the rotation schedule (D’Attilio, [0083]), “After staffing requirements are known from a previous phase, the system may derive shifts that can handle the requirements. Accordingly, the illustrative method 800 begins with block 802 in which the system receives/retrieves a list of agents and staffing request forecasts (e.g., in addition to the number of weeks/days to schedule). It should be appreciated that the list of agents may include, for example, working rules associated with the agents (e.g., work hours, regulatory requirements, etc.), capabilities of the respective agents (e.g., specializations in various areas, etc.), and/or other relevant criteria useful for deriving shifts from the staffing requirements and available agents”; (D’Attilio, [0126]), “the contact center system 1200 may include one or more mass storage devices—represented generally by the storage device 1220—for storing data in one or more databases relevant to the functioning of the contact center”; (D’Attilio, [0114]; [0046]; [0049]);
to generate one or more data structures by parsing information from the one or more data feeds, the one or more data structures comprising a filtered subset of the plurality of assets which are eligible to be selected for inclusion in the given instance of the rotation schedule(D’Attilio, [0089]), “The system may find at least one valid shift schedule for each agent or agent grouping, for example, by solving the initial sub-problem, sunit-SP, for each agent. In some embodiments, to speed up the process, the system may “chunk” agents into groupings of size N and calculate the reduced cost for each agent individually. If a valid shift schedule cannot be found for a particular agent, that agent is excluded”; (D’Attilio, [0085]; [0095]; [0097]);
to generate the given instance of the rotation schedule by selecting, from among the filtered subset of the plurality of assets which are eligible to be selected for inclusion in the given instance of the rotation schedule, one or more of the plurality of assets for the given instance of the rotation schedule, wherein selecting the one or more of the plurality of assets for the given instance of the rotation schedule is based at least in part on information from at least one of the one or more data feeds characterizing requirements related to composition of the given instance of the rotation schedule (D’Attilio, [0085]), “The system performs optimization based on the list of agents, staffing requirement forecasts, and/or other relevant data in order to generate shift schedules with the desired optimal (or approximately optimal output)”; (D’Attilio, [0045]), “performing scheduling in which the headcount requirement is fulfilled through placement of staff throughout the planning horizon according to shift and schedule constraints, such that the final output is a schedule or roster that optimizes (or sub-optimizes) the coverage of workload with staffed agents”; (D’Attilio, [0101]), “…the system may output the optimized shift schedules”; (D’Attilio, [0060]), “workload forecasts may, in turn, be converted into a staffing requirement forecast to be used in scheduling process”; (D’Attilio, [0075]; [0081]; [0107]; [0112]); and
to control execution of the given instance of the rotation schedule (D’Attilio, [0101]), “the system may output the optimized shift schedules. For example, in some embodiments, the system may return the resulting schedules for display. In some embodiments, the optimized shift schedules may be automatically incorporated into one or more aspects of the contact center system 1200”; (D’Attilio, [0055]; [0086]
D’Attilio teaches allowing end users to choose an optimal schedule depending on objectives (see par. 0107), but D’Attilio does not explicitly teach:
for rotation of a plurality of assets of an organization among two or more different groups associated with two or more different functional areas of the organization;
wherein at least one of the one or more data feeds comprises information characterizing (i) which ones of the plurality of assets of the organization were selected for inclusion in one or more previous instances of the rotation schedule and (ii) which of the two or more different groups that the ones of the plurality of assets of the organization that were selected for inclusion in the one or more previous instances of the rotation schedule belong to; and
requirements related to rotating selection of assets from the two or more different groups in consecutive instances of the rotation schedule.
However Garcia teaches:
for rotation of a plurality of assets of an organization among two or more different groups associated with two or more different functional areas of the organization (Garcia, [0031]), “employee schedule record 41 contains a plurality of work assignment sub-records 43 with the number of such fields varying depending upon how many work periods the employee will work during the three week schedule period. Each work assignment sub-record 43 has a first field 44 which stores the date of the assignment and a second field 45 specifying the location where the employee is to work. The scheduling program may be used by a health care system that operates several hospitals and freestanding clinics and a given employee may work at more than one of those facilities and on different days may be assigned to different areas in the same facility… A department may comprise several work units, in which case the work unit to which the employee is assigned is specified in a fifth field 48. A sixth field 49 holds information specifying the job class associated with this assignment. A given nurse may work as a regular registered nurse (RN) on some days, and as head nurse or another capacity on other days”; (Garcia, [0037]);
wherein at least one of the one or more data feeds comprises information characterizing (i) which ones of the plurality of assets of the organization were selected for inclusion in one or more previous instances of the rotation schedule and (ii) which of the two or more different groups that the ones of the plurality of assets of the organization that were selected for inclusion in the one or more previous instances of the rotation schedule belong to (Garcia, [0047]), “the data entry routine displays the information from the completed work assignments within the previous two pay periods, thus displaying the places within this business in which the employee has worked most recently. It is likely that the future work assignment which is being sought will be for one of those places”; (Garcia, [0039]), “computer automatically searches the time and attendance data file 52 (FIG. 4) for work period records 53 for this employee to obtain all instances of the facilities at which this employee has worked”; (Garcia, [0034]), “the time and attendance data file 52 stores a plurality of records 53 each containing information related to a period actually worked by an employee. Specifically each work period record 53 has a first field 54 that contains the identifier for the associated employee and a second field 55 that stores the date on which the work was carried out. Five fields 56, 57, 58, 59, and 60 contain labor distribution information obtained from the corresponding assignment record 43 in the scheduling data file 40, unless manually overwritten by a supervisor. Field 56 identifies the location at which the work was performed, and fields 57-59 specify the facility, department, and unit in which the person worked”; (Garcia, [0045]); and
requirements related to rotating selection of assets from the two or more different groups in consecutive instances of the rotation schedule (Garcia, [0030]), “This software program provides a listing of all the employees available for assignment to a given department and each employee's work preferences, such as a work shift, vacation schedule, and the like. This enables the employees to be assigned to specific work shifts and to an amount of time during each shift, either automatically by the computer program or manually by a supervisor. Some employees may work eight hour shifts, while others have twelve hour shifts. The schedule for each employee is stored in a work schedule data file within a storage device of the employee records computer 12”; (Garcia, [0031]), “A sixth field 49 holds information specifying the job class associated with this assignment. A given nurse may work as a regular registered nurse (RN) on some days, and as head nurse or another capacity on other days. The job class also identifies the wage scale that the employee is to receive for this assignment. The seventh and eighth fields 50 and 51 in the sub-record 43 define the start time of the work assignment and its duration. Preferably the duration is specified by the length of the work period (e.g. a number of hours), but alternatively the duration could be indicated by storing the scheduled end time of the work period”; (Garcia, [0032]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimal schedule in D’Attilio to include the asset limitations as taught by Garcia. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of accessing prior information about each employee being scheduled (see Garcia par. 0063).
Referring to Claim 2, D’Attilio in view of Garcia teaches the apparatus of claim 1. D’Attilio further teaches:
wherein the one or more data feeds comprise one or more representational state transfer (REST) or other application programming interfaces (APIs) (D’Attilio, [0053]), “the system receives an API request is made (e.g., for an optimized contact center agent schedule). In block 204, one or more input data (e.g., ACD data, API request data, and/or other data) may be pre-processed. In block 206, the system retrieves the relevant workload forecast model and, in block 208, the system generates the workload forecasts based on the workload forecast model. For example, in some embodiments, it should be appreciated that the system may execute the method 300 of FIG. 3 described below in order to retrieve the workload forecast model and generate the workload forecasts. However, it should be appreciated that the system may otherwise retrieve the relevant model and/or generate the workload forecasts in other embodiments” (D’Attilio, [0154]; [0046]).
Referring to Claim 4, D’Attilio in view of Garcia teaches the apparatus of claim 1. D’Attilio further teaches:
wherein the plurality of assets comprise information technology assets of an information technology infrastructure (D’Attilio, [0131]), “the processing logic of the chat server 1240 may be rules driven so to leverage an intelligent workload distribution among available chat resources. The chat server 1240 further may implement, manage, and facilitate user interfaces (UIs) associated with the chat feature, including those UIs generated at either the customer device 1205 or the agent device 1230”; (D’Attilio, [0117]), “the contact center system 1200 may be used to engage and manage interactions in which automated processes (or bots) or human agents communicate with customers”; (D’Attilio, [0044]), “create a scalable, multi-objective agent scheduling system able to handle very large cases and a variety of goals. More specifically, in some embodiments, the technologies leverage a state-of-the art solver (e.g., IBM ILOG CPLEX) with a contact-center specific scheduling algorithm that takes workload and staffing requirement forecasts generated by the AI models as inputs, and uses column generation with linear programming (LP) for optimizing a set of specific objectives master problem”; (D’Attilio, [0049]).
Referring to Claim 5, D’Attilio in view of Garcia teaches the apparatus of claim 1. D’Attilio further teaches:
wherein the rotation schedule specifies a given team of the organization that is responsible for management of one or more information technology assets of the organization (D’Attilio, [0045]), “determining the expected number of workload interactions (e.g. calls, emails, chats, back-office work, etc.) as well as the service time associated with those interactions (e.g., average handle time) in the planning horizon, converting the workload predictions from the first phase into a staffing or headcount requirement for the future planning horizon, and performing scheduling in which the headcount requirement is fulfilled through placement of staff throughout the planning horizon according to shift and schedule constraints, such that the final output is a schedule or roster that optimizes (or sub-optimizes) the coverage of workload with staffed agents”; (D’Attilio, [0122]), “the switch 1212 may include an automatic call distributor, a private branch exchange (PBX), an IP-based software switch, and/or any other switch with specialized hardware and software configured to receive Internet-sourced interactions and/or telephone network-sourced interactions from a customer, and route those interactions to, for example, one of the agent devices 1230”; (D’Attilio, [0131]; [0079]; [0115]; [0125]; [0083]-[0084]).
Referring to Claim 10, D’Attilio in view of Garcia teaches the apparatus of claim 1. D’Attilio further teaches:
wherein the requirements related to the composition of the assets in the given instance of the rotation schedule comprise one or more requirements related to at least one of: a number of the plurality of assets to be selected for inclusion in the given instance of the rotation schedule; and geographic locations of ones of the plurality of assets to be selected for inclusion in the given instance of the rotation schedule (D’Attilio, [0081]), “The system may leverage a service performance calculator built using the validated and optimized contact center model that takes in the workload forecast and the number of agents to produce a predicted set of KPIs. In order to get the optimal staffing requirement level, the number of agents may be increased iteratively (e.g., using a bisection algorithm) until the KPIs predicted by the calculator meet the desired, specified KPI goals. In some embodiments, in cases in which there are multiple KPI goals, all goals must be met. After all the optimal staffing requirements for each planning period have been computed, the system may optimize the schedules of shifts to match the available staffing to anticipated needs or requirements”; (D’Attilio, [0077]), “after the workload and agent handle time (AHT), for example, have been forecasted for the planning horizon, the system may determine how many agents are required to handle the forecasted workload, given certain KPI goals (e.g., such as service level, average speed of answer (ASA), abandon rate, customer satisfaction score, etc.)”.
Referring to Claim 11, D’Attilio in view of Garcia teaches the apparatus of claim 1. D’Attilio further teaches:
wherein the requirements related to the composition of the assets in the given instance of the rotation schedule comprise one or more requirements related to functionalities of ones of the plurality of assets to be selected for inclusion in the given instance of the rotation schedule (D’Attilio, [0083]), “After staffing requirements are known from a previous phase, the system may derive shifts that can handle the requirements. Accordingly, the illustrative method 800 begins with block 802 in which the system receives/retrieves a list of agents and staffing request forecasts (e.g., in addition to the number of weeks/days to schedule). It should be appreciated that the list of agents may include, for example, working rules associated with the agents (e.g., work hours, regulatory requirements, etc.), capabilities of the respective agents (e.g., specializations in various areas, etc.), and/or other relevant criteria useful for deriving shifts from the staffing requirements and available agents”; (D’Attilio, [0084]; [0125]-[0126]).
Referring to Claim 15, D’Attilio teaches:
A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs (D’Attilio, [0042]), wherein the program code when executed by at least one processing device causes the at least one processing device:
Claim 15 disclose substantially the same subject matter as claim 1, and is rejected using the same rationale as previously set forth.
Referring to Claim 18, D’Attilio teaches:
A method comprising:
Claim 18 disclose substantially the same subject matter as claim 1, and is rejected using the same rationale as previously set forth.
Claims 3, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over D'Attilio et al., U.S. Publication No. 2022/0027837 [hereinafter D’Attilio], in view of Garcia et al., U.S. Publication No. 2008/0086500 [hereinafter Garcia], and further in view of Manickam et al., U.S. Publication No. 2022/0103580 [hereinafter Manickam].
Referring to Claim 3, D’Attilio in view of Garcia teaches the apparatus of claim 2. D’Attilio teaches an API (see par. 0053; 0154), but D’Attilio does not specifically teach:
wherein at least one of the one or more REST APIs is established with a scheduling database maintained by an organization.
However Manickam teaches:
wherein at least one of the one or more REST APIs is established with a scheduling database maintained by the organization (Manickam, [0029]), “The resource orchestrator 114 may manage the deployment of the resources 106 on the respective host nodes 102 and monitor the resources 106 deployed in the cluster 104 or across various clusters that are managed by the resource management system 112. The resource orchestrator 114 may communicate with other components (e.g., the scheduler 116 and the taint monitoring system 118) of the resource management system 100 using an API (Application Programming Interface) interface such as REST (representational state transfer) API, SOAP (simple object access protocol) and the like”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the API in D’Attilio to include the REST API limitation as taught by Manickam. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of improving overall performance of the application(s) or service(s) (see Manickam par. 0016).
Referring to Claim 16, D’Attilio in view of Garcia teaches the computer program product of claim 15. D’Attilio further teaches:
wherein the one or more data feeds comprise one or more representational state transfer (REST) application programming interfaces (APIs) (D’Attilio, [0053]), “the system receives an API request is made (e.g., for an optimized contact center agent schedule). In block 204, one or more input data (e.g., ACD data, API request data, and/or other data) may be pre-processed. In block 206, the system retrieves the relevant workload forecast model and, in block 208, the system generates the workload forecasts based on the workload forecast model. For example, in some embodiments, it should be appreciated that the system may execute the method 300 of FIG. 3 described below in order to retrieve the workload forecast model and generate the workload forecasts. However, it should be appreciated that the system may otherwise retrieve the relevant model and/or generate the workload forecasts in other embodiments” (D’Attilio, [0154]; [0046]).
D’Attilio teaches an API (see par. 0053; 0154), but D’Attilio does not specifically teach:
wherein at least one of the one or more REST APIs is established with a scheduling database maintained by the organization.
However Manickam teaches:
wherein at least one of the one or more REST APIs is established with a scheduling database maintained by the organization (Manickam, [0029]), “The resource orchestrator 114 may manage the deployment of the resources 106 on the respective host nodes 102 and monitor the resources 106 deployed in the cluster 104 or across various clusters that are managed by the resource management system 112. The resource orchestrator 114 may communicate with other components (e.g., the scheduler 116 and the taint monitoring system 118) of the resource management system 100 using an API (Application Programming Interface) interface such as REST (representational state transfer) API, SOAP (simple object access protocol) and the like”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the API in D’Attilio to include the REST API limitation as taught by Manickam. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of improving overall performance of the application(s) or service(s) (see Manickam par. 0016).
Claim 19 disclose substantially the same subject matter as claim 16, and is rejected using the same rationale as previously set forth.
Claims 6-9, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over D'Attilio et al., U.S. Publication No. 2022/0027837 [hereinafter D’Attilio], in view of Garcia et al., U.S. Publication No. 2008/0086500 [hereinafter Garcia], and further in view of Bowles (check et. al), U.S. Publication No. 2024/0193509 [hereinafter Bowles]
Referring to Claim 6, D’Attilio in view of Garcia teaches the apparatus of claim 1. D’Attilio further teaches:
wherein generating the one or more data structures comprises: generating a first data structure by parsing first information from a first one of the one or more data feeds, the first information characterizing ones of the plurality of assets which may be selected for inclusion in the given instance of the rotation schedule (D’Attilio, [0087]), “a column is defined as the set of shifts assigned to an agent (i.e., a “shift schedule”). The master problem ensures that the shift schedules selected meet the requirements for each planning group, and the sub-problem finds shift schedules that meet the scheduling constraints”; (D’Attilio, [0044]), “a contact-center specific scheduling algorithm that takes workload and staffing requirement forecasts generated by the AI models as inputs, and uses column generation with linear programming (LP) for optimizing a set of specific objectives master problem (e.g., service performance, agent preference, paid cost, etc.) and constraint programming (CP) for solving sub-problems that find potential candidates of best agent shifts”; (D’Attilio, [0085]), “an optimization problem can be represented as a matrix where each row corresponds to a constraint and each column corresponds to a decision variable. Standard optimization approaches such as linear programming and discrete optimization typically try to solve for all decision variables at once, which creates a scalability issue”; (D’Attilio, [0099]), “the integer master problem is only solved once, and the pool of shift schedule candidates to choose from contains all valid shift schedules found during column generation”; (D’Attilio, [0105]);
generating a second data structure by filtering the first data structure using second information parsed from a second one of the one or more data feeds, the second information characterizing scheduling conflicts for the ones of the plurality of assets which may be selected for inclusion in the given instance of the rotation schedule (D’Attilio, [0126]), “the storage device 1220 may store agent data in an agent database. Agent data maintained by the contact center system 1200 may include, for example, agent availability and agent profiles, schedules, skills, handle time, and/or other relevant data”; (D’Attilio, [0089]), “If a valid shift schedule cannot be found for a particular agent, that agent is excluded”; (D’Attilio, [0087]), “a column is defined as the set of shifts assigned to an agent (i.e., a “shift schedule”). The master problem ensures that the shift schedules selected meet the requirements for each planning group, and the sub-problem finds shift schedules that meet the scheduling constraints”; (D’Attilio, [0044]), “a contact-center specific scheduling algorithm that takes workload and staffing requirement forecasts generated by the AI models as inputs, and uses column generation with linear programming (LP) for optimizing a set of specific objectives master problem (e.g., service performance, agent preference, paid cost, etc.) and constraint programming (CP) for solving sub-problems that find potential candidates of best agent shifts”; (D’Attilio, [0085]), “an optimization problem can be represented as a matrix where each row corresponds to a constraint and each column corresponds to a decision variable. Standard optimization approaches such as linear programming and discrete optimization typically try to solve for all decision variables at once, which creates a scalability issue”; (D’Attilio, [0099]), “the integer master problem is only solved once, and the pool of shift schedule candidates to choose from contains all valid shift schedules found during column generation”; and
generating a fourth data structure by selecting, using fourth information parsed from a fourth one of the one or more data feeds, the filtered subset of the plurality of assets which are eligible for inclusion in the given instance of the rotation schedule, the fourth information comprising one or more organization requirements of the organization related to the composition of the given instance of the rotation schedule (D’Attilio, [0083]), “the list of agents may include, for example, working rules associated with the agents (e.g., work hours, regulatory requirements, etc.), capabilities of the respective agents (e.g., specializations in various areas, etc.), and/or other relevant criteria useful for deriving shifts from the staffing requirements and available agents. In other words, shift scheduling balances the problem of selecting what shifts are to be worked by each employee to meet workload requirements and hit certain KPI goals with adhering to various work plan constraints and state/national labor regulations (e.g., such as maximum shift duration, earliest shift starting time, latest finishing time, etc.). Depending on the constraints, agent preferences, and availability, the pool of feasible shifts can range from just a few possible combinations and permutations to billions of possible combinations and permutations”; (D’Attilio, [0087]), “a column is defined as the set of shifts assigned to an agent (i.e., a “shift schedule”). The master problem ensures that the shift schedules selected meet the requirements for each planning group, and the sub-problem finds shift schedules that meet the scheduling constraints”; (D’Attilio, [0044]), “a contact-center specific scheduling algorithm that takes workload and staffing requirement forecasts generated by the AI models as inputs, and uses column generation with linear programming (LP) for optimizing a set of specific objectives master problem (e.g., service performance, agent preference, paid cost, etc.) and constraint programming (CP) for solving sub-problems that find potential candidates of best agent shifts”; (D’Attilio, [0085]), “an optimization problem can be represented as a matrix where each row corresponds to a constraint and each column corresponds to a decision variable. Standard optimization approaches such as linear programming and discrete optimization typically try to solve for all decision variables at once, which creates a scalability issue”; (D’Attilio, [0099]), “the integer master problem is only solved once, and the pool of shift schedule candidates to choose from contains all valid shift schedules found during column generation”; (D’Attilio, [0077]; [0081]).
D’Attilio teaches decision variable columns (see par. 0085) and an agent databased (see par. 0126), but D’Attilio does not explicitly teach:
generating a third data structure by filtering the second data structure using third information parsed from a third one of the one or more data feeds, the third information characterizing ones of the plurality of assets selected for inclusion in the one or more previous instances of the rotation schedule.
However Bowles teaches:
generating a third data structure by filtering the second data structure using third information parsed from a third one of the one or more data feeds, the third information characterizing ones of the plurality of assets selected for inclusion in the one or more previous instances of the rotation schedule (Bowles, [0057]), “the list of eligible shift workers may be modified based on previous shift selections 204 for the work shift schedule 208. For example, shift workers that have already selected a designated number of shifts may be excluded from the notifications for the remaining unassigned work shifts 210”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the decision variables in D’Attilio to include the information limitation as taught by Bowles. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of ensuring compliance with employment regulations and internal business policies (see Bowles par. 0005).
Referring to Claim 7, D’Attilio in view of Garcia in view of Bowles teaches the apparatus of claim 6. D’Attilio further teaches:
wherein generating the first data structure comprises creating a list of the plurality of assets meeting one or more eligibility criteria (D’Attilio, [0083]), “After staffing requirements are known from a previous phase, the system may derive shifts that can handle the requirements. Accordingly, the illustrative method 800 begins with block 802 in which the system receives/retrieves a list of agents and staffing request forecasts (e.g., in addition to the number of weeks/days to schedule). It should be appreciated that the list of agents may include, for example, working rules associated with the agents (e.g., work hours, regulatory requirements, etc.), capabilities of the respective agents (e.g., specializations in various areas, etc.), and/or other relevant criteria useful for deriving shifts from the staffing requirements and available agents”; (D’Attilio, [0085]; [0089]).
Referring to Claim 8, D’Attilio in view of Garcia in view of Bowles teaches the apparatus of claim 6. D’Attilio further teaches:
wherein generating the second data structure comprises utilizing the second information to at least one of: remove ones of the plurality of assets located in geographic regions having geographic holidays observed during a time frame of the given instance of the rotation schedule; and remove ones of the plurality of assets based at least in part on deadlines for one or more tasks that the plurality of assets are responsible for (D’Attilio, [0066]-[0067]), “the system performs outlier detection to identify data points that are significantly different from other observations, for example, as outliers can cause significant problems in model building by resulting in highly skewed models. Thus, detecting and normalizing outliers is an important step towards obtaining a proper and accurate forecast. Outliers that occur on specific calendar days (e.g. holiday and trading day effects)… after the data is cleaned (e.g., including removal of outlier data)”; (D’Attilio, [0045]), “determining the expected number of workload interactions… the final output is a schedule or roster that optimizes (or sub-optimizes) the coverage of workload with staffed agents”; (D’Attilio, [0062]-[0065]; [0053]; [0077]; [0081]).
Referring to Claim 9, D’Attilio in view of Garcia in view of Bowles teaches the apparatus of claim 6. D’Attilio teaches decision variable columns (see par. 0085) and an agent databased (see par. 0126), but D’Attilio does not explicitly teach:
wherein generating the third data structure comprises utilizing the third information to remove ones of the plurality of assets which have been selected for inclusion in the one or more previous instances of the rotation schedule.
However Bowles teaches:
wherein generating the third data structure comprises utilizing the third information to remove ones of the plurality of assets which have been selected for inclusion in the one or more previous instances of the rotation schedule (Bowles, [0057]), “the list of eligible shift workers may be modified based on previous shift selections 204 for the work shift schedule 208. For example, shift workers that have already selected a designated number of shifts may be excluded from the notifications for the remaining unassigned work shifts 210”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the decision variables in D’Attilio to include the information limitation as taught by Bowles. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of ensuring compliance with employment regulations and internal business policies (see Bowles par. 0005).
Claim 17 disclose substantially the same subject matter as claim 6, and is rejected using the same rationale as previously set forth.
Claim 20 disclose substantially the same subject matter as claim 6, and is rejected using the same rationale as previously set forth.
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over D'Attilio et al., U.S. Publication No. 2022/0027837 [hereinafter D’Attilio], in view of Garcia et al., U.S. Publication No. 2008/0086500 [hereinafter Garcia], and further in view of Meharwade et al., U.S. Publication No. 2021/0125124 [hereinafter Meharwade].
Referring to Claim 12, D’Attilio in view of Garcia teaches the apparatus of claim 1. D’Attilio further teaches:
wherein the at least one processing device is further configured: to publish the given instance of the rotation schedule (D’Attilio, [0101]), “the system may output the optimized shift schedules. For example, in some embodiments, the system may return the resulting schedules for display”.
D’Attilio teaches allowing end users to choose an optimal schedule depending on objectives (see par. 0107), but D’Attilio does not explicitly teach:
to determine whether the given instance of the rotation schedule is accepted by one or more authorized users.
However Meharwade teaches:
to determine whether the given instance of the rotation schedule is accepted by one or more authorized users (Meharwade, [0094]), “providing the release schedule to a user device with a request for approval to allocate resources according to the release schedule, and generating, based on receiving approval from the user device, a project release plan for the project, the project release plan identifies the release schedule and allocates resources for the new project according to the release schedule”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the user selection in D’Attilio to include the acceptance limitation as taught by Meharwade. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of addressing issues associated with the incorrect and/or suboptimal release plans (see Meharwade par. 0063).
Referring to Claim 13, D’Attilio in view of Garcia in view of Meharwade teaches the apparatus of claim 12. D’Attilio teaches allowing end users to choose an optimal schedule depending on objectives (see par. 0107), but D’Attilio does not explicitly teach:
wherein the at least one processing device is further configured responsive to the one or more authorized users accepting the given instance of the rotation schedule, to update information in at least one of the one or more data feeds characterizing ones of the plurality of assets which have been selected for inclusion in one or more previous instances of the rotation schedule.
However Meharwade teaches:
wherein the at least one processing device is further configured responsive to the one or more authorized users accepting the given instance of the rotation schedule, to update information in at least one of the one or more data feeds characterizing ones of the plurality of assets which have been selected for inclusion in one or more previous instances of the rotation schedule (Meharwade, [0095]), “providing, to a user device associated with a user, information suggesting a determined allocation of resources for the new project; receiving, from the user device, a response indicating an approval of a first portion of the determined allocation of resources and a disapproval of a second portion of the determined allocation of resources; causing, based on the response, resources associated with the first portion of the determined allocation to be allocated for the new project, and retraining the machine learning model based on the response.”; (Meharwade, [0049]), “the project management platform may retrain the machine learning model based on an approval of the project release plan by a user (e.g., similar to a supervised training technique). In this way, the project management platform may be configured to continue to train the machine learning model according to newly received project data and generated project release plans for the newly receive project data, thus permitting the machine learning model to evolve and adjust overtime (e.g., due to changes in resource usage, availability, capability, type, and/or the like)”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the user selection in D’Attilio to include the updating limitations as taught by Meharwade. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of detecting patterns and/or trends (see Meharwade par. 0026).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over D'Attilio et al., U.S. Publication No. 2022/0027837 [hereinafter D’Attilio], in view of Garcia et al., U.S. Publication No. 2008/0086500 [hereinafter Garcia], in view of Meharwade et al., U.S. Publication No. 2021/0125124 [hereinafter Meharwade], and further in view of Yang, U.S. Publication No. 2023/0229993 [hereinafter Yang].
Referring to Claim 14, D’Attilio in view of Garcia in view of Meharwade teaches the apparatus of claim 12. D’Attilio teaches allowing end users to choose an optimal schedule depending on objectives (see par. 0107), but D’Attilio does not explicitly teach:
wherein the at least one processing device is further, responsive to the one or more authorized users submitting one or more change requests for the given instance of the rotation schedule:
to modify information in at least one of the one or more data feeds characterizing at least one of ones of the plurality of assets which may be selected for inclusion in the given instance of the rotation schedule and scheduling conflicts for the ones of the plurality of assets which may be selected for inclusion in the given instance of the rotation schedule; and
to re-generate the given instance of the rotation schedule based at least in part on the modified information in said at least one of the one or more data feeds.
However Yang teaches:
wherein the at least one processing device is further, responsive to the one or more authorized users submitting one or more change requests for the given instance of the rotation schedule:
to modify information in at least one of the one or more data feeds characterizing at least one of ones of the plurality of assets which may be selected for inclusion in the given instance of the rotation schedule and scheduling conflicts for the ones of the plurality of assets which may be selected for inclusion in the given instance of the rotation schedule; and to re-generate the given instance of the rotation schedule based at least in part on the modified information in said at least one of the one or more data feeds (Yang, [0043]), “. Administrator system 104 is a system for use by an administrator or a manager. An administrator or a manager utilizes administrator system 104 to administrate and use a shift design and shift assignment system—for example, installing the applications, configuring the applications, managing the scheduling configuration data, to request schedule creation, to refine the schedule, to receive schedule data, etc. Depending on the nature of the interaction, administrator system 104 may communicate with (via the network 100) scheduling engine 106 or transaction engine 108, or both. Scheduling engine 106 manages the scheduling input data using the transition engine 108 to persist data, generates a set of shift candidates based at least in part on the labor demand, creates an optimal schedule by invoking a MIP solver, produces an incremental solution if a portion of the scheduling input data is changed”; (Yang, [0044]), “In response to a solution being provided, the user is able to use the provided shift design and shift assignment (e.g., to provide to a worker that will work according to the schedule provided as a solution) and/or modify the shift design and shift assignment by adjusting the input data and resubmitting the input data for a new solution.”; (Yang, [0077]-[0078]; [0093]-[0095]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the user selection in D’Attilio to include the modifying and re-regenerating limitations as taught by Yang. The motivation for doing this would have been to improve the method of performing contact center agent scheduling in D’Attilio (see par. 0005) to efficiently include the results of determining an appropriate optimization problem making a set of related decisions in such a way that a set of given constraints are satisfied and a utility function is optimized (see Yang par. 0028).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Horne et al. (US 20100070294 A1) – The central server staff assignment module may identify the staff members by accessing the database, which, as noted above, may store a mapping of one or more staff members to each of the different areas within the healthcare facility in which the staff members are capable of working and/or assigned to work. According to one embodiment, each staff member may be capable of facilitating treatment of a patient in one or more different areas and multiple staff members may be capable of facilitating treatment of a patient in the same area.
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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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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