DETAILED ACTION
This communication is a Final Office Action rejection on the merits. Claims 1-3 and 5-8 are currently pending and have been addressed below.
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 .
Response to Arguments
Applicant's arguments filed on 04/28/2026 (related to the 101 Rejection) have been fully considered but they are not persuasive.
Applicant states, on pages 1-5, that the amended claims are not directed merely to calculating values or to an optimization result in the abstract. Rather, the amended claims recite a specific computer-implemented scheduling workflow for a cloud-based contact center. In particular, amended independent claims 1 and 8 now recite how lower-bound and upper-bound session values are calculated using open-slot-specific net staffing data, skill-type-specific buffer levels, and maximum-agents-per-session constraints. The claims then use those calculated bounds to determine a number of sessions, allocate agents to the determined sessions, evaluate whether the resulting allocation leaves the contact center overstaffed or understaffed for the selected skill-types, and iteratively update the bounds until an optimal number of sessions is determined.
Also, the amended claims do not merely state that an optimal number of sessions is calculated. Rather, they recite the particular mechanism by which the claimed system computes the session bounds used in determining the number of sessions. That mechanism is tied to open slots in scheduled work-shifts, skill-type-specific net staffing data, and buffer level is selected for the relevant skill -types.
Lastly, the Office Action evaluates the computer, cloud, user interface, schedule manager Microservice, database, and binary search algorithm largely in isolation. Applicant respectfully submits that the claims must be considered as an ordered combination. The ordered combination recited in amended claims 1 and 8 is not merely a generic instruction to apply an abstract idea on a computer.
Examiner respectfully disagrees with Applicant. These claim elements are considered to be abstract ideas because they are directed to “mathematical concepts” which include “mathematical calculations.” In this case, using an algorithm for optimizing a number of sessions of a multi-session meeting is considered a mathematical calculation (see MPEP 2106.04(a)(2), using an algorithm for determining an optimal number of visits). Also, allocating agents across sessions subject to skill type and staffing constraints is directed to managing personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers mathematical calculations or managing personal behavior, then it falls within the “mathematical concepts” or “certain methods or organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The mere nominal recitation of generic computer components does not take the claim out of the mathematical concepts grouping. The computer is merely used to execute instructions (Paragraph 0005). The cloud is merely used to receive data (Paragraph 0112). The user interface of a webapp is merely used to receive a time-range of scheduled work-shifts, a maximum number of agents in each session of the multi-session meeting, one or more skill-types and a buffer-level for each skill-type of the one or more skill-types (Paragraph 0006). The schedule manager Microservice is merely used to provide: a. scheduled work-shifts of agents in the time-range of scheduled work-shifts that include open slots from a database of a plurality of agents with respective plurality of schedules of work-shifts thereon; and b. net staffing data net staffing data of each skill-type of the received one or more skill-types in each open slot in the scheduled work- shifts in the time-range of scheduled work-shifts (Paragraph 0006). The database is merely used to store open slots of a plurality of agents with respective plurality of schedules of work-shifts thereon (Paragraph 0006). The binary search algorithm is merely used to: calculate lower-bound and upper-bound of sessions; and determine a number of sessions of the multi-session meeting based on agents’ skill requirement by calculating an average of the calculated lower-bound of sessions and the calculated upper-bound of sessions (Paragraphs 0007-0009). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “computer,” “cloud,” “user interface,” “schedule manager Microservice,” “database,” and “binary search algorithm” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Also, the user interface is considered “field of use” (MPEP 2106.05h) since it’s just used to receive parameters and constraints for an optimization analysis, but the user interface is not improved. Further, the step of “allocating the total number of agents in the time-range of scheduled work-shifts that include open slots to the determined number of sessions of the multi-session meeting based on agents’ skill requirement during the received time-range of scheduled work- shifts” is merely part of the optimization analysis and lacked details as to how the computer/scheduler performed the allocations (see MPEP 2106.05f). Lastly, an iterative adjustment (e.g., re-runs the computation of average session count and agent allocation) is considered a conventional computer function of “receiving and transmitting over a network” and “performing repetitive calculations” (see MPEP 2106.05d).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or 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 claim amounts to significantly more than the abstract idea itself. Therefore, the claim is not patent eligible.
Independent claim 8 recites similar features and therefore is rejected for the same reasons as independent claim 1. Claims 2-3 and 5-7 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claims 1 and 8.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3 and 5-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - Claim 1 recites: A implemented method for optimizing a number of sessions of a multi-session meeting based on agents skill requirement during a time-range of scheduled work-shifts, in a contact center, said implemented method comprising: (i) receiving a time-range of scheduled work-shifts, a maximum number of agents in each session of the multi-session meeting, one or more skill-types and a buffer-level for each skill-type of the one or more skill-types; (ii) operating to provide a. scheduled work-shifts of agents in the time-range of scheduled work-shifts that include open slots of a plurality of agents with a respective plurality of schedules of work-shifts thereon; and b. net staffing data of each skill-type of the received one or more skill- types in each open slot, in the scheduled work-shifts in the time-range of scheduled work-shifts; (iii) determining a total number of agents in the time-range of scheduled work-shifts that include open slots; (iv) calculating a lower-bound of sessions and an upper-bound of sessions, wherein the calculating of the lower-bound of sessions is operated by allocating for each open slot in the scheduled work-shifts in the time-range of scheduled work-shifts the received maximum number of agents in each session of the multi-session meeting and meeting the received buffer-level for each skill-type of the one or more skill-types based on net staffing data of each skill-type for the open slot, and wherein the calculating of the upper-bound of sessions is operated by allocating for each open slot in the scheduled work-shifts in the time-range of scheduled work-shifts a preconfigured percentage of the received maximum number of agents in each session of the multi-session meeting of skill-based agents and meeting the received buffer-level for each skill-type of the one or more skill-types based on net staffing data for the open slot; (v) determining a number of sessions of the multi-session meeting based on agents skill requirement by calculating an average of the calculated lower-bound of sessions and the calculated upper-bound of sessions, and (vi) allocating the total number of agents in the time-range of scheduled work-shifts that include open slots to the determined number of sessions of the multi-session meeting based on agents skill requirement during the received time-range of scheduled work-shifts, and (vii) evaluating the allocating of the total number of agents to the determined number of sessions to determine whether the contact center is overstaffed or understaffed for the one or more skill-types, and wherein when the determined number of sessions of the multi-session meeting based on agents skill requirement is above a preconfigured percentage of the calculated upper-bound of sessions, decreasing the calculated upper-bound by an algorithm and performing operations (v)-(vi), and wherein when the determined number of sessions of the multi-session meeting based on agents skill requirement is below or equal to the preconfigured percentage of the calculated upper-bound of sessions increasing the calculated lower-bound by the binary search algorithm and performing operations (v)-(vi) until an optimal number of sessions of the multi-session meeting is determined. These claim elements are considered to be abstract ideas because they are directed to “mathematical concepts” which include “mathematical calculations.” In this case, using an algorithm for optimizing a number of sessions of a multi-session meeting is considered a mathematical calculation. If a claim limitation, under its broadest reasonable interpretation, covers mathematical calculations, then it falls within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: a computer; a cloud; a user interface of a webapp; a schedule manager Microservice; a database; and a binary search algorithm.
The computer is merely used to execute instructions (Paragraph 0005). The cloud is merely used to receive data (Paragraph 0112). The user interface of a webapp is merely used to receive a time-range of scheduled work-shifts, a maximum number of agents in each session of the multi-session meeting, one or more skill-types and a buffer-level for each skill-type of the one or more skill-types (Paragraph 0006). The schedule manager Microservice is merely used to provide: a. scheduled work-shifts of agents in the time-range of scheduled work-shifts that include open slots from a database of a plurality of agents with respective plurality of schedules of work-shifts thereon; and b. net staffing data net staffing data of each skill-type of the received one or more skill-types in each open slot in the scheduled work- shifts in the time-range of scheduled work-shifts (Paragraph 0006). The database is merely used to store open slots of a plurality of agents with respective plurality of schedules of work-shifts thereon (Paragraph 0006). The binary search algorithm is merely used to: calculate lower-bound and upper-bound of sessions; and determine a number of sessions of the multi-session meeting based on agents skill requirement by calculating an average of the calculated lower-bound of sessions and the calculated upper-bound of sessions (Paragraphs 0007-0009). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “computer,” “cloud,” “user interface,” “schedule manager Microservice,” “database,” and “binary search algorithm” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Also, the user interface is considered “field of use” (MPEP 2106.05h) since it’s just used to receive parameters and constraints, but the user interface is not improved. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of using an algorithm for optimizing a number of sessions of a multi-session meeting. The specification shows that the computer is merely used to execute instructions (Paragraph 0005). The cloud is merely used to receive data (Paragraph 0112). The user interface of a webapp is merely used to receive a time-range of scheduled work-shifts, a maximum number of agents in each session of the multi-session meeting, one or more skill-types and a buffer-level for each skill-type of the one or more skill-types (Paragraph 0006). The schedule manager Microservice is merely used to provide: a. scheduled work-shifts of agents in the time-range of scheduled work-shifts that include open slots from a database of a plurality of agents with respective plurality of schedules of work-shifts thereon; and b. net staffing data net staffing data of each skill-type of the received one or more skill-types in each open slot in the scheduled work- shifts in the time-range of scheduled work-shifts (Paragraph 0006). The database is merely used to store open slots of a plurality of agents with respective plurality of schedules of work-shifts thereon (Paragraph 0006). The binary search algorithm is merely used to: calculate lower-bound and upper-bound of sessions; and determine a number of sessions of the multi-session meeting based on agents skill requirement by calculating an average of the calculated lower-bound of sessions and the calculated upper-bound of sessions (Paragraphs 0007-0009). Also, the step of “decreasing the calculated upper-bound or increasing the calculated lower-bound by a binary search algorithm” is considered a well-understood, routing, and conventional function since it’s just “performing repetitive calculations” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 8 is directed to a system at step 1, which is a statutory category. Claim 8 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 8 further recites: a memory and a processing unit – which are treated as just an explicit “processor/computer” for executing the operations and is treated under MPEP 2106.05f in the same manner as claim 1. Accordingly, these additional elements of “memory” and “processor” are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, claim is not patent eligible.
Dependent claims 2-3 are not directed to any additional claim elements. Rather, these claims offer further functions of elements found in the independent claims and addressed above - such as wherein the computer is used to: send a notification to each agent in the total number of agents with session date and time and storing each updated schedule in the database of a plurality of agents with a respective plurality of schedules of work-shifts thereon; and update each schedule of each agent of the determined total number of agents with session date and time. Those additional functions are considered “field of use” (MPEP 2106.05h) at step 2A, Prong 2; as they are just used to receive and transmit information, but the technology is not improved. At step 2B, this is still a conventional computer function of “receiving and transmitting over a network” and “storing information in a memory” (see MPEP 2106.05d), Thus, nothing in the claim adds significantly more to the abstract idea. The claim is not patent eligible.
Dependent claim 5 is not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of the abstract idea mentioned above - such as: wherein the determining of the total number of agents in the time-range of scheduled work-shifts that include open slots is based on preferences thereof. These processes are similar to the abstract idea noted in the independent claim because they further the limitations of the independent claim which are directed to “mathematical concepts” which include “mathematical calculations.” In addition, there are no additional elements to consider at Step 2A Prong 2 and Step 2B. Therefore, the claims still recite an abstract idea that can be grouped into mathematical concepts.
Dependent claims 6-7 are directed to additional elements such as: a greedy algorithm and round-robin method; and a random method. The combination of the greedy algorithm with the round-robin or random method is merely used to allocate the total number of agents to the determined number of sessions (Paragraphs 0014-0015). Merely stating that the step is performed by a computer component (e.g., greedy algorithm being applied on a computer) results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is not patent eligible.
Potential Allowable Subject Matter
The closest prior art is Velednitsky (US 12,154,046 B1). Velednitsky discloses a computer-implemented method for optimizing a number of sessions of a multi-session [non-productive activities such as worker breaks/lunches] based on agents skill requirement during a time-range of scheduled work-shifts, in a cloud-based contact center, said computer-implemented method comprising (Column 2, lines 12-45, Systems and methods are described herein for generating shifts and assigning resources to those shifts to generate schedules for resources, such as human or computing. One specific example use case for these techniques include contact center agents scheduling. In one aspect, a scheduling problem may be decomposed into stages solved sequentially, where the first stage allocates the overall headcount or minimum resource requirement to groups. This first stage includes breaking the total workload, e.g., a contact volume to respond to, into different scheduling groups prior to generated the unrostered shifts. The shifts may be grouped based on common characteristics, such that shifts which are grouped together may generally be viewed as interchangeable (similar tasks or type, similar start and end times, etc.); Column 8, lines 24-26, It should be appreciated that the described scheduling service 116 may diverge from prior scheduling solutions in that it may be cloud based and not on-premise based; Column 16, lines 57-67, Looking at the scheduling problem naively, one might assume that break placement is relatively unimportant. As long as the placement of breaks adheres to labor laws, the breaks may be placed with a simple heuristic. In other cases, optimal break placement can be quite important. This is exemplified by the “lunch problem”: when all the agents have to take 30-minute lunches in a relatively short window of time, a contact center that is normally well-staffed may end up dramatically under-staffed during lunch and create a call backlog that lasts into the afternoon. Mathematically, the MIP formulation used in this step utilizes and generalizes upon an idea called implicit break scheduling. Rather than placing every break, it can be determined how many agents will take breaks in each interval. The actual assignments of breaks to shifts is done in a post-processing step. In some cases, a greedy algorithm may be applied for this post-processing; Examiner interprets “determining how many agents will take breaks in each interval using a greedy optimization algorithm” as “optimizing a number of sessions of a multi-session meeting based on agents skill requirement during a time-range of scheduled work-shifts.” In this case, each break is interpreted as a session):
(i) receiving via a User Interface (UT) of a webapp (Column 4, lines 24-28, In some cases, agent 104 may send a request 114 to the computing resource service provider 102 to generate or modify an agent or resource schedule, as will be described in greater detail below; Column 4, lines 46-51, In at least one embodiment, client 104 interacts with a GUI to interact with various data provided by or through the computing resource service provider 102, and client-side software translates the GUI setup to a web service API request which is transmitted from the client computer system 104 to front end 108 via a network 106) a time-range of scheduled work-shifts (Column 11, lines 23-47, Process 600 may begin at operation 602, in which a request to generate a schedule for resource allocation over a time period may be received. The resources maybe human or agent resources, such as may include a pool of agents ready to be assigned shifts (hours of work) to process a workload (e.g., respond to contact center communications), …, one or more skill-types … (Column 8, lines 60-67, As illustrated, an agent profile 202 may include various attributes, such as a type 240, an assigned staffing group 206, and a shift profile 208. Various rules may apply to the agent, such as agent level rules 210 (e.g., hours minimum and/or maximum limits, type of work the agent can performed, etc.), staffing group rules 214, which may apply to a group of agents having similar characteristics; Column 21, lines 32-42, At operation 906, agents may be assigned or grouped into scheduling groups based on one or more same or similar shift characteristics. The shift characteristics may include at least one of hours of operation, a duration, a type, or an activity profile, or various other attributes (e.g., as described above in reference to FIG. 3) of a shift that would make shifts within a scheduling group interchangeable for scheduling purposes. Operation 906 may include grouping a first set of agents that have similar shift characteristic(s) into a first group and a second set of agents with different similar shift characteristics(s) into a second group, and so on);
(ii) operating a schedule manager Microservice (MS) to provide a scheduled work-shifts of agents in the time-range of scheduled work-shifts that include open slots from a database of a plurality of agents with a respective plurality of schedules of work-shifts thereon (Column 9, lines 39-57, FIG. 3 illustrates an example data structure that represents a rostered shift 300, which may be used by the scheduling service 116 and/or contact service 110 to generate schedules, as described above in reference to FIG. 1. As used herein, an empty shift has an assigned start and end time 306 and shift profile 308, but no activity placement 320, and no attached agent. An unrostered shift has an assigned start and end time 306, as well as activity placement 320 and shift profile 308, but no attached agent. Finally, a rostered shift has everything: start time and end time 306, shift profile 308, activity placement 320, and an agent 304. A set of shifts which are linked together, usually because they are rostered to the same agent (or intended to be rostered to the same agent), is referred to as a tour. An activity placement 320 may define at what times during a shift, certain activities 312, 314, 316, 318 have been assigned. As illustrated, the boxes 312-318 may represent certain activities or productive periods and their duration, whereas the spaces in between may represent breaks or non-productive activities or periods); and b. net staffing data of each skill-type of the received one or more skill- types in each open slot, in the scheduled work-shifts in the time-range of scheduled work-shifts (Column 8, lines 4-11, The scheduling service 116 may have as inputs: the time period for the schedule, including start and end times, dates, etc. (e.g., October 1 to December 31); the minimum resource requirement or headcount per time interval (e.g., 15 minutes), as may be determined by a queuing model; and the legal and business rules affecting shifts and tours, including one or more of staffing group rules, shift profile rules, and agent rules; Column 8, lines 60-67, As illustrated, an agent profile 202 may include various attributes, such as a type 240, an assigned staffing group 206, and a shift profile 208. Various rules may apply to the agent, such as agent level rules 210 (e.g., hours minimum and/or maximum limits, type of work the agent can performed, etc.), staffing group rules 214, which may apply to a group of agents having similar characteristics; Column 21, lines 32-42, At operation 906, agents may be assigned or grouped into scheduling groups based on one or more same or similar shift characteristics. The shift characteristics may include at least one of hours of operation, a duration, a type, or an activity profile, or various other attributes (e.g., as described above in reference to FIG. 3) of a shift that would make shifts within a scheduling group interchangeable for scheduling purposes; Column 12, lines 57-62, In some cases, operations 614 and 616 may be performed to reduce understaffing or under-allocating resources, or overstaffing or over-allocating resources based on the minimum headcount or minimum resources for the workload, as may be defined by one or more constraints and/or the workload itself; Column 15, lines 1-5, In Step 1, empty shifts are created. This operation allows the total headcount requirement to be divided into a headcount by scheduling group requirement, thereby separating the overall scheduling problem into separate sub-problems by scheduling group);
(iii) determining a total number of agents in the time-range of scheduled work-shifts that include open slots (Column 8, lines 4-11, The scheduling service 116 may have as inputs: the time period for the schedule, including start and end times, dates, etc. (e.g., October 1 to December 31); the minimum resource requirement or headcount per time interval (e.g., 15 minutes), as may be determined by a queuing model; and the legal and business rules affecting shifts and tours, including one or more of staffing group rules, shift profile rules, and agent rules;
…;
(vi) allocating the total number of agents in the time-range of scheduled work-shifts that include open slots to the … number of sessions of the multi-session [non-productive activities such as worker breaks/lunches] based on agents skill requirement during the received time-range of scheduled work-shifts (Column 16, lines 57-67, Looking at the scheduling problem naively, one might assume that break placement is relatively unimportant. As long as the placement of breaks adheres to labor laws, the breaks may be placed with a simple heuristic. In other cases, optimal break placement can be quite important. This is exemplified by the “lunch problem”: when all the agents have to take 30-minute lunches in a relatively short window of time, a contact center that is normally well-staffed may end up dramatically under-staffed during lunch and create a call backlog that lasts into the afternoon. Mathematically, the MIP formulation used in this step utilizes and generalizes upon an idea called implicit break scheduling. Rather than placing every break, it can be determined how many agents will take breaks in each interval. The actual assignments of breaks to shifts is done in a post-processing step. In some cases, a greedy algorithm may be applied for this post-processing), ....
Although Valednitsky discloses optimizing a number of sessions of a multi-session [non-productive activities such as worker breaks/lunches] based on agents skill requirement during a time-range of scheduled work-shifts (e.g., determining, using a greedy optimization algorithm, how many agents will take breaks in each interval based on resource requirements for each time interval), Valednitsky does not specifically disclose wherein optimizing the number of sessions further includes the steps of: calculating a lower-bound of sessions and an upper-bound of sessions; and dynamically increasing or decreasing the lower-bound or upper-bound of number of sessions by a binary search algorithm based on agents skill requirements until an optimal number of sessions of the multi-session meeting is determined (e.g., overstaffing or understaffing).
Matias (US 2021/0218838 A1). Matias discloses (iv) calculating a lower-bound of [staffing requirement] and an upper-bound of [staffing requirement], …; (v) determining a number of [staff requirement] based on agents skill requirement by calculating an average of the calculated lower-bound of sessions and the calculated upper-bound of sessions, and; (vi) allocating the total number of agents in the time-range of scheduled work-shifts that include open slots to the determined [optimal staff requirement] based on agents skill requirement during the received time-range of scheduled work-shifts, … wherein when the determined [staffing requirement] based on agents skill requirement is above a preconfigured … of the calculated upper-bound of [staff], decreasing the calculated upper-bound by a binary search algorithm and performing operations (v)-(vi), and wherein when the determined [staffing requirement] based on agents skill requirement is below or equal to the preconfigured … of the calculated upper-bound of sessions increasing the calculated lower-bound by the binary search algorithm and performing operations (v)-(vi) until an optimal number of sessions of the multi-session meeting is determined (Paragraph 0046, he goal for the deferred communication engine 210 is to distribute the staffing requirement 213 of the current interval a attributable to the workload 214 among the intervals within the region [a, b], so that the sum of the staffing requirement 213 for the deferred queue 123 and the staffing requirement 213 of the immediate queue 125 for each interval is as even as possible. Depending on the embodiment, the deferred communication engine 210 may use binary search and multiple iterations to calculate a satisfactory distribution of the staffing requirements 213; Paragraph 0048, For each interval a, as above, the deferred communication engine 210 may consider some tentative levels from which it may pick an optimal level that is used as a reference to distribute the staffing requirement 213 from the current interval a attributable to the workload 214 among the intervals within the region [a, b]. Each optimal level approximates the lowest level at which the staffing requirement 213 from the current interval attributable to the workload 214 can be distributed. The deferred communication engine 210 at each iteration of the algorithm may determine an upper bound 217 and a lower bound 218 for the optimal level. The upper bound 217 and lower bound 218 may be updated by the deferred communication engine 210 during each iteration of the algorithm, getting progressively closer to the optimal level. To do so, the deferred communication engine 210 may pick intermediate values and may analyze whether they are above or below the optimal level; Paragraph 0051, In each iteration, the deferred communication engine 210 may determine if the value is above or below the optimal level for the level and then the deferred communication engine 210 may update the bounds accordingly).
Although the combination of Valednitsky and Matias discloses allocating agents to a number of sessions based on staff requirements (e.g., allocating agents to take breaks when the contact center is overstaffed), the combination of Valednitsky and Matias does not specifically disclose wherein the number of sessions include other non-productive activities such as team meetings or coaching sessions or compliance trainings (see Applicant’s specification, Paragraph 0032).
Pahud (US 2024/0031217 A1). Padud discloses (vi) allocating the total number of agents in the time-range of scheduled work-shifts that include open slots to the determined number of sessions of the multi-session meeting based on agents skill requirement during the received time-range of scheduled work-shifts, … by a [AI] algorithm … (Paragraph 0283, In a second example, relating to agent training, assume that the end-user network 320 has existing rules running, via the management network 300, to monitor the real-time status of all incoming service queues and identify unexpected periods of downtime that are automatically leveraged for off-phone working, such as training, coaching, reviewing communications, etc. If the enterprise associated with the end-user network 320 has a new training program that needs to be deployed that requires a prescribed number of hours of each agent's time over the next 30 days, the recommendation engine 314 can apply AI algorithms to data from incoming data streams cached and stored in the database devices 306 to recommend to the customer what each queue threshold would have to be set at in order to a) maintain service levels, while b) finding the prescribed number of hours per agent of training time needed over the next 30 days. The recommendation engine 314 would recommend a rules modification to set the specific thresholds by queue that would ensure that (a) training could be delivered (b) without causing overall service level commitments to be missed. This second example also could be appropriately modified to apply to rules recommendations for training or updating of agent instance devices).
However, the cited art, alone or in any combination, fails to teach or suggest at least: a computer-implemented method for optimizing a number of sessions of a multi-session meeting based on agents skill requirement during a time-range of scheduled work-shifts, in a cloud-based contact center, said computer-implemented method comprising: (i) receiving via a User Interface (UI) of a webapp a time-range of scheduled work-shifts, a maximum number of agents in each session of the multi-session meeting, one or more skill-types and a buffer-level for each skill-type of the one or more skill-types; (ii) operating a schedule manager Microservice (MS) to provide a. scheduled work-shifts of agents in the time-range of scheduled work-shifts that include open slots from a database of a plurality of agents with a respective plurality of schedules of work-shifts thereon; and b. net staffing data of each skill-type of the received one or more skill-types in each open slot, in the scheduled work-shifts in the time-range of scheduled work-shifts; (iii) determining a total number of agents in the time-range of scheduled work-shifts that include open slots; (iv) calculating a lower-bound of sessions and an upper-bound of sessions, wherein the calculating of the lower-bound of sessions is operated by allocating for each open slot in the scheduled work-shifts in the time-range of scheduled work-shifts the received maximum number of agents in each session of the multi-session meeting and meeting the received buffer-level for each skill-type of the one or more skill-types based on net staffing data of each skill-type for the open slot, and wherein the calculating of the upper-bound of sessions is operated by allocating for each open slot in the scheduled work-shifts in the time-range of scheduled work-shifts a preconfigured percentage of the received maximum number of agents in each session of the multi-session meeting of skill-based agents and meeting the received buffer-level for each skill-type of the one or more skill-types based on net staffing data for the open slot; (v) determining a number of sessions of the multi-session meeting based on agents skill requirement by calculating an average of the calculated lower-bound of sessions and the calculated upper-bound of sessions, (vi) allocating the total number of agents in the time-range of scheduled work-shifts that include open slots to the determined number of sessions of the multi-session meeting based on agents skill requirement during the received time-range of scheduled work-shifts, and (vii) evaluating the allocating of the total number of agents to the determined number of sessions to determine whether the contact center is overstaffed or understaffed for the one or more skill-types, wherein when the determined number of sessions of the multi-session meeting based on agents skill requirement is above a preconfigured percentage of the calculated upper-bound of sessions, decreasing the calculated upper-bound by a binary search algorithm and performing operations (v)-(vi), and wherein when the determined number of sessions of the multi-session meeting based on agents skill requirement is below or equal to the preconfigured percentage of the calculated upper-bound of sessions increasing the calculated lower-bound by the binary search algorithm and performing operations (v)-(vi) until an optimal number of sessions of the multi-session meeting is determined.
Nor does the remaining prior art of record remedy the deficiencies found in the cited prior art. Furthermore, neither the prior art, the nature of the problem, nor knowledge of a person having ordinary skill in the art provides for any predictable or reasonable rationale to combine prior art teachings.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Mendes (Mendes, F., Lucet, C. and Moukrim, A., 2006, October. Tabu search to plan schedules in a multiskill customer contact center. In 2006 International Conference on Service Systems and Service Management (Vol. 2, pp. 1126-1131). IEEE) – discloses a vacation represents one day of work, for one agent. The solution of the scheduling problem is a schedule of the vacations of each agent on a given planning horizon: weekly or monthly. The schedule indicates also the different skills that will be used by the agent during his working days. After scheduling, for each skill and each time period, we obtain the number of agents ideally necessary and the number of agents effectively planned. A covering curve can be designed, showing excess and shortage intervals. Our main objective is to obtain at least minimal service levels for minimal costs, by using flexibility in task assignments. Multiskill agents are scheduled for one or several skills during their day of work. Moreover, equity between agents has to be maintained, notably in assignment of the off days and in the average length of meal periods. These parameters are evaluated by measuring standard deviation with regard to average values over all agents (see at least Page 3, 2.4. Feasible scheduling solution).
Gartner (Gärtner, J., Musliu, N. and Slany, W., 2004, December. A heuristic based system for generation of shifts with breaks. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 95-106). London: Springer London) – discloses considering generation of shifts together with breaks for each employee. Generation of breaks makes the problem of generation of shifts much more complex, correspondingly the automatic generation of shifts with breaks is a very important issue for schedulers as good solutions can reduce significantly the costs of organizations. In Section 4 we apply the system to a real problem of a large airport in Europe. Note that experienced professional planners can construct solutions for practical problems by hand. However, the time they need is sometimes very long (one hour to several days for very large instances), and, because of the large number of possible solutions, the human planners can never be sure how strong their solution differs from the best one. Therefore, the aim of automating the generation of shifts with breaks is to make possible the generation of good solutions in a short time, thereby reducing costs and finding better solutions for problems that appear in practice (see at least Page 2, Introduction).
D'ATTILIO (WO 2022/020796 A1) – discloses as indicated above, modern contact center agent scheduling solutions also consider the fact that not all interactions can be handled by a single agent, and that different agents will have different skillsets and qualifications. Additionally, shift start times, shift lengths, breaks, and meals typically should vary in order to obtain “good” shifts that cover the workforce requirements adequately. Often, the shifts over-staff (i.e., exceed the requirements) in certain time intervals and under-staff in other time intervals based on the number of available agents and various, oftentimes conflicting, constraints (see at least Paragraph 0084).
D'ATTILIO (US 2022/0027837 A1) – discloses to generate a work load forecast indicative of a demand that will be introduced into the contact center in a future planning period based on a workload forecast model and time series data, generate a staffing requirement forecast indicative of a number of agents required to handle the workload forecast based on the workload forecast, one or more service goals, and a staffing requirement model, and perform schedule optimization using column generation to generate an optimized contact center agent shift schedule for a plurality of agents based on the staffing requirement forecast and one or more constraints (see at least abstract).
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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/M.P./Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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