DETAILED ACTION
Acknowledgement
This final office action is in response to the claims filed on 10/18/2025.
Status of Claims
No claims have been amended.
Claims 1-4 and 6-12 are pending.
Response to Arguments
Applicant's arguments filed on 10/18/2025 regarding the 35 U.S.C. 101 and 103 rejections of claims 1-4 and 6-12 have been fully considered. The Applicant argues the following.
1) As per the 101 rejection, the Applicant repeats previous arguments that the claim elements provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. The Applicant asserts that the result of the process to find an optimum interval is applied in real time to result in the change in the system to be patentable, which is automatically adjusting the agent- schedule accordingly by updating the agent-schedule in the real time agents-schedule cache with the next-interval-scheduled activity in the optimum interval with no man in the loop. The claim elements link the use of the alleged judicial exception to the particular technological environment. The additional elements recite a specific manner of agents' schedule adjustment to better balance the workforce during the day, which provides a specific improvement over prior systems, resulting in an improved Employee Engagement Manager (EEM) application and as a result an improved workforce Management (WFM) system. Thus, the claim is eligible because it is not directed to the recited judicial exception.
The Examiner respectfully disagrees and maintains previous arguments. The Examiner maintains the position that the additional elements recited in the claims and highlighted in Steps 2A(2) and 2B do not integrate the abstract idea into a practical application nor provide significantly more because the additional elements do not improve upon the functioning of a computer or improve another technology. The additional elements are viewed as mere instructions to implement an abstract idea on a computer and merely indicates a field of use or technological environment in which to apply the abstract idea. Per MPEP 2106.05 (f) and (h), applying an abstract idea on a computer and/or generally linking the use of the abstract idea to a particular technological environment does not integrate a judicial exception into a practical application or provide an inventive concept. Per MPEP 2106.04(a)(I), mere automation of manual processes (e.g. adjusting schedules) is not sufficient to show an improvement in computer-functionality. Improvements in adjusting agents schedules to better balance the workforce is considered a workforce management improvement, which is considered abstract and not an improvement in technology. Per MPEP 2106.05(a), an improvement in the abstract idea (e.g. schedule adjustments) itself is not an improvement in technology. Therefore, the 35 U.S.C. 101 rejection is maintained.
2) As per the 103 rejection, the Applicant argues that (i) Marr doesn't continuously collect data because Marr doesn't perform operations (e) and (f); (ii) Marr doesn't require to find the optimum interval in real time, as Marr doesn't postpone the activity of the next interval, i.e. unavailability-interval type, during the agent-schedule in real time, in which the agent may not be available to that optimum interval when the agent may not be needed because it is overstaffed; (iii) Krucek doesn't cure the deficiencies of Marr. Krucek merely detects overstaffed slots in a schedule "slack periods"; and (iv) Marr nor Krucek, alone or combined, teaches or suggests an automated prevention of out of adherence state. Moreover, none of the prior art is acting before the agent becomes in non-adherence state, thus allowing the agent to avoid a situation of out of adherence when the agent is under legitimate work rules.
The Examiner respectfully disagrees. The Examiner submits that based on the broadest reasonable interpretation of the claims, Marr in view of Krucek teach all of the limitations recited in independent claims 1 and 11. Marr teaches real-time monitoring of schedule adherence among a plurality of agents which involves continuous collection of data and continuous monitoring as recited in “Real-time monitoring system 102 collects updates from multiple sources in real time (i.e. preconfigured interval) and routes new normalized data to Web application server 122. In turn, Web application server 122 generates new user interface with the new normalized data and present the user interface to the applications (col. 4 lines 43-48). Because the displays are continuously refreshed by real time monitoring system as new events and data are processed, a user may keep monitoring the status of the agent by selecting the agent (col. 7 lines 56-59)”. Although Marr’s invention does not explicitly teach steps (e) and (f) nor requires finding the optimum interval that is an interval that is overstaffed…and postponing the agent’s activity, Krucek teaches this concept with finding or predicting slack periods. A slack period is one or more intervals of time where the number of agents 120 assigned to work for the contact center 150, or on a particular queue, exceeds the number of agents 120 that are needed to meet one or more service level goals for the contact center 150 or queue 120 (i.e. overstaffed) [0018]. Krucek addresses the slack periods by automatically scheduling the agents to perform other tasks (i.e. offline) [0003]. The Examiner has previously stated that argument (iv) is considered moot because ““an automated prevention of out of adherence state” and “acting before the agent becomes in non-adherence” is not recited in the Applicant’s claims. This argument is in contradiction to the Applicant’s claims to a computerized-method for monitoring unavailability-interval type to adjust agent-schedule to be in adherence and to balance workforce. This suggests that the agent is out of adherence, therefore, there is no prevention nor preemption.
Therefore, the 35 U.S.C. 103 rejection is maintained for the reasons presented above.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4 and 6-12 are rejected under 35 U.S.C. 101 because the claimed invention, “System and Method for Monitoring an Unavailability-Interval Type to Adjust Agent-Schedule to be in Adherence and to Balance Workforce, in a Contact-Center”, is directed to an abstract idea, specifically Certain Methods of Organizing Human Activity and Mental Processes, without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1-4 and 6-12 are directed to a statutory category, namely a process (claims 1-4, 6-10, and 12) and a machine (claim 11).
Step 2A (1): Independent claims 1 and 11 are directed to an abstract idea of Certain Methods of Organizing Human Activity and Mental Processes, based on the following claim limitations: “method for monitoring unavailability-interval type to adjust agent-schedule to be in adherence and to balance workforce; a) monitoring…an unavailability-interval type and a time-threshold based on parameters of a rule …wherein the parameters of the rule are the unavailability-interval type for monitoring and the time-threshold; for each agent in…agents-schedule…: b) checking if the agent is in adherence by retrieving agent-state … and comparing said agent-state with agent currently scheduled-activity in the… agents-schedule …, wherein when the agent-state is compatible with the agent currently scheduled-activity in the…agents-schedule… the agent is determined as in adherence, c) when the agent is in adherence, retrieving a scheduled-activity after the time-threshold from current-time, from the…agents-schedule…, wherein the retrieved scheduled-activity is determined as next-interval-scheduled activity when the retrieved-scheduled activity is different than the currently scheduled-activity; d) when the next-interval-scheduled activity is same as the unavailability-interval type for monitoring, calculating an interval-range; e) finding an optimum interval, during the calculated interval-range, wherein the optimum interval is an interval that is overstaffed and having a highest staffing gap between scheduled staff and required staff than other overstaffed intervals in the calculated interval-range; and f) when an optimum interval has been found, automatically adjusting the agent- schedule accordingly by updating the agent-schedule…with the unavailability-interval type in the optimum interval”. These claims describe a process of monitoring and managing workforce schedules to meet certain conditions/rules and adjusting schedules to address changes. Dependent claims 2-4, 6-10, and 12 further describe the monitoring and management of workforce schedules. Monitoring and managing workforce schedules are considered certain method of organizing human activity as the work schedules directs and manages the workforce time and activities. Monitoring and managing workforce schedules via operating a rules, checking for adherence, comparing agent state to agent schedules, calculating an interval-range, finding an optimal interval, and adjusting the agent-schedule can be practically performed in the human mind with pen and paper via observation, evaluation, and judgements. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of “Certain Methods of Organizing Human Activity” which encompasses managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions and Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping). Therefore, claims 1-4 and 6-12 are directed to an abstract idea and are not patent eligible.
Step 2A (2): This judicial exception is not integrated into a practical application. In particular, claims 1-2 and 6-12 recite additional elements of “a computerized-method, a contact center, a computerized system comprising: one or more processors, Employee Engagement Manager (EEM) application; a real-time agents-schedule cache; Automatic Call Distribution (ACD) system; wherein the real time agents-schedule cache is synced with a Workforce Management (WFM) system via the EEM; sending a notification to a computerized-device, a computerized system…comprising one or more processors; an ACD system; a real time agents-schedule cache; and EEM application, the one or more processors are configured to: ”. These additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing and display devices that are used to perform the abstract process of monitoring and managing workforce schedules to meet certain conditions/rules, adjusting schedules, and notifying the workforce of changes. Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Also, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception (e.g. call center) do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)). Therefore, claims 1-4 and 6-12 do not include individual or a combination of additional elements integrate the judicial exception into a practical application and thus are not patent eligible.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-2 and 6-12 recite additional elements of “a computerized-method, a contact center, a computerized system comprising: one or more processors, Employee Engagement Manager (EEM) application; a real-time agents-schedule cache; Automatic Call Distribution (ACD) system; wherein the real time agents-schedule cache is synced with a Workforce Management (WFM) system via the EEM; sending a notification to a computerized-device, a computerized system…comprising one or more processors; an ACD system; a real time agents-schedule cache; and EEM application, the one or more processors are configured to: ”. These additional elements are viewed as mere instructions to implement an abstract idea on a computer and merely indicates a field of use or technological environment in which to apply the abstract idea. Applying an abstract idea on a computer and/or generally linking the use of the abstract idea to a particular technological environment does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05 (f) and (h)). Therefore, claims 1-4 and 6-12 do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the judicial exception and thus are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4 and 6-12 are rejected under 35 U.S.C. 103 as being unpatentable over Marr et al. (US 8,670,553 B1) in view of Krucek et al. (US 2021/0201226 A1).
As per claims 1 and 11 (Currently Amended), Marr teaches a computerized-method for monitoring unavailability-interval type to adjust agent-schedule to be in adherence and to balance workforce, in a contact-center, the computerized-method comprising (Marr e.g. The present disclosure provides a method, a system, and a computer readable medium comprising instructions for real-time monitoring of schedule adherence among a plurality of agents (col. 1 lines 17-20). FIG. 2, is a flowchart of a process for real-time monitoring of agent adherence (col. 4 lines 14-16).) one or more processors, an Automatic Call Distribution (ACD) system, an agents-schedule cache, and Employee Engagement Manager (EEM) application, the one or more processors configured to (Marr e.g. System 100 comprises a real-time monitoring system 102 which collects data and events from multiple sources... including multiple agent phone routers 104 (i.e. ACD system) and the web application server 118 (i.e. EEM) which collects data from the agent scheduling system 114 (i.e. agents-schedule cache), and timekeeping system 130 (Fig. 1 and col. 2 lines 57-63). Process 200 may be implemented in a computer readable-medium comprising instructions executing within real-time monitoring system 102 and web application server 122 (col. 4 lines 17-19).)
a) Marr teaches monitoring by one or more processors an unavailability-interval type and a time- threshold based on parameters of a rule that is carried out by an Employee Engagement Manager (EEM) application, wherein the parameters of the rule are the unavailability-interval type for monitoring and the time-threshold (Marr e.g. System 100 comprises a real-time monitoring system 102 which collects data and events from multiple sources... including multiple agent phone routers 104 and the web application server 118 which collects data from the agent scheduling system 114, and timekeeping system 130 (Fig. 1 and col. 2 lines 57-63). Agent phone routers 104 provide phone states and skills of the agents to real-time monitoring system 102. An example of phone state may be whether the agent is active or inactive on the switch (col. 2 lines 64-67). Based on the phone states and skills, content management server 112 generates reports that include a list of current states and skills of all agents who are currently on the switch. Content management server 112 also updates the list of current states every ten seconds (col. 3 lines 1-6). System 100 further comprises a message broker 122 that provides a view of real-time agent status to management. In one embodiment, message broker 122 provides Web user interfaces that are updated in real time with events from real-time monitoring system 102. Examples of Web user interfaces include desktop application user interface 124 and staff application user interface 126...Desktop application may comprise a set of rules for determining adherence of agents (Fig. 1 and col. 3 lines 32-44). Systems 102, 106, 114, 118, 122, and 130 may be a data processing system, such as a desktop computer, a laptop computer, a server or any other type of device capable of sending and receive information via a wired or wireless protocol (col. 3 lines 45-49). Real-time monitoring system 102 also collects events from time keeping system 130 through web application server 118. Time keeping information includes clocked-in data of agents as well as lunch and break information (i.e. unavailability-interval-type). The time keeping data 10 may be refreshed up to 12 times an hour (col. 3 lines 7-11). In one embodiment, the clocked-in data indicates whether an agent is clocked in or out. If the agent is clocked out, the time keeping data also indicates whether the agent is out for break, for lunch, or sick (col. 3 lines 11-14). Real-time monitoring system 102 collects updates from multiple sources in real time and routes new normalized data to Web application server 122. In turn, Web application server 122 generates new user interface with the new normalized data and present the user interface to the applications (col. 4 lines 43-48).)
Marr teaches for each agent in a real time agents-schedule cache (Marr e.g. In one embodiment the schedule data indicates when the agent is scheduled to arrive and begin work and when they are scheduled to end work and leave (col. 3 lines 18-20). Agent scheduling system 114 generates agent schedules based on previously created schedules, which are fetched from the web application server 118 by real-time monitoring system 102 (col. 3 lines 15-18). Real-time monitoring system 102 collects updates from multiple sources in real time and routes new normalized data to Web application server 122. In turn, Web application server 122 generates new user interface with the new normalized data and present the user interface to the applications (col. 4 lines 43-48).):
b) Marr teaches checking if the agent is in adherence by retrieving agent-state from an Automated Call Distribution (ACD) system and comparing said agent-state with agent currently scheduled-activity in the real-time agents-schedule cache, wherein when the agent-state is compatible with the agent currently scheduled-activity in the real-time agents-schedule cache the agent is determined as in adherence, (Marr e.g. Agent phone routers 104 (i.e. ACD system) provide phone states and skills of the agents to real-time monitoring system 102. An example of phone state may be whether the agent is active or inactive on the switch (col. 2 lines 64-67).) Server side process 106 performs computations of events and data collected from the multiple sources. Real-time monitoring system 102 then supplies these events to message broker 122 (col. 3 lines 28-31). In one embodiment, a view of real-time agent status may include headcounts, scheduled agents, clocked-in agents, and adherence state of the agents (col. 3 lines 59-61). The adherence state may be in adherence or out of adherence. In adherence means that the agent's working skills and/or clocked-in state match up to the schedule. Out of adherence means that the agent's working skills and/or clocked-in state does not match up to the schedule (col. 3 lines 61-66). To determine whether an agent is in or out of adherence, real-time monitoring system 102 examines the clocked-in data from time keeping system 130 and agent schedules from the agent scheduling system 114 and determines whether the agent is clocked in to work as scheduled or clocked in to work but not scheduled. Alternatively, based on the clocked-in data and agent schedules, real-time monitoring system 102 also determines agents who are scheduled and clocked in and scheduled but not clocked in (col. 4 lines 5-13). In summary, the present disclosure provides real-time monitoring of agent adherence to a work schedule. Data and events, including events from call router, time clock information and schedule events, are collected from multiple sources and normalized using a server side process. A view of details for sites, skills, and agents are provided to management via a Web user interface and refreshed at real time to provide updated schedule adherence information (col. 8 lines 57-64).)
c) Marr teaches when the agent is in adherence, retrieving a scheduled-activity after the time-threshold from current-time, from the real time agents-schedule cache, wherein the retrieved scheduled-activity is determined as next-interval-scheduled activity when the retrieved-scheduled activity is different than the currently scheduled- activity; and (Marr e.g. Real-time monitoring system 102 collects updates from multiple sources in real time and routes new normalized data to Web application server 122. In turn, Web application server 122 generates new user interface with the new normalized data and present the user interface to the applications (col. 4 lines 43-48). Because the displays are continuously refreshed by real time monitoring system as new events and data are processed, a user may keep monitoring the status of the agent by selecting the agent (col. 7 lines 56-59). FIG. 7 is a diagram illustrating a list of details for a particular agent (col. 6 lines 22-23) and shows Current Adherence as “In Adherence”. Display 700 comprises details for a particular agent. In this example, the details include switch skills 702, scheduled skills 704, time clock state 706, and adherence history 708 (col. 6 lines 26-29). Skill state 712 is the status of the agent on the switch, which may be logged out, on call, busy other, wrap, waiting, and not ready (col. 6 lines 33-35). State Duration 714 is the amount of time that the agent has spent in that state (col. 6 lines 35-36).)
Marr teaches wherein the real time agents-schedule cache is synced with a Workforce Management (WFM) system via the EEM, (Marr e.g. System 100 comprises a real-time monitoring system 102 which collects data and events from multiple sources... including multiple agent phone routers 104 and the web application server 118 (i.e. EEM) which collects data from the agent scheduling system 114 (i.e. WFM), and timekeeping system 130 (col. 2 lines 57-63). Agent scheduling system 114 (i.e. WFM) generates agent schedules 15 based on previously created schedules, which are fetched from the web application server 118 (i.e. EEM) by real-time monitoring system 102 (col. 3 lines 15-18). Real-time monitoring system 102 collects updates from multiple sources in real time and routes new normalized data to Web application server 122. In turn, Web application server 122 generates new user interface with the new normalized data and present the user interface to the applications (col. 4 lines 43-48). Because the displays are continuously refreshed by real time monitoring system as new events and data are processed, a user may keep monitoring the status of the agent by selecting the agent (col. 7 lines 56-59).)
d) Marr teaches when the next-interval-scheduled activity is same as the unavailability-interval type for monitoring, calculating an interval-range; (Marr e.g. The scheduled state 722 may either be scheduled or not scheduled. State duration 724 is the amount of time the agent has spent in that schedule duration (col. 6 lines 44-46). Adherence history 708 include the agent's tracking since 728, time tracked 730, in adherence 732, and out of adherence 734 (col. 6 lines 55-57).)
f) Marr teaches….updating the agent-schedule in the real time agents-schedule cache (Marr e.g. Agent scheduling system 114 generates agent schedules 15 based on previously created schedules, which are fetched from the web application server 118 by real-time monitoring system 102 (col. 3 lines 15-18). Real-time monitoring system 102 collects updates from multiple sources in real time and routes new normalized data to Web application server 122. In turn, Web application server 122 generates new user interface with the new normalized data and present the user interface to the applications (col. 4 lines 43-48). A view of real-time agent status may include headcounts, scheduled agents, clocked-in agents, and adherence state of the agents (col. 3 lines 59-61). The present disclosure provides real-time monitoring of agent adherence to a work schedule. Data and events, including events from call router, time clock information and schedule events, are collected from multiple sources and normalized using a server side process. A view of details for sites, skills, and agents are provided to management via a Web user interface and refreshed at real time to provide updated schedule adherence information (col. 8 lines 57-64).)
Marr does not explicitly teach, however, Krucek teaches the following:
e) Krucek teaches finding an optimum interval, during the calculated interval-range, wherein the optimum interval is an interval that is overstaffed and having a highest staffing gap between scheduled staff and required staff than other overstaffed intervals in the calculated interval-range; and (Krucek e.g. A system for predicting slack periods is provided. The system predicts and detects time intervals for an entity (e.g., a contact center) where the amount of available work (e.g., call volume) is less than what can be handled by the number of employees (e.g., agents) that are scheduled to work during the time intervals. These detected intervals are referred to herein as "slack periods". (Abstract and [0003]). The contact center 150 may include one or both of a workforce management ("WFM") application 154 and a quality management ("QM") application 155. The WFM application 154 may generate forecasts and schedules for the contact center 150 and the agents 120 and may ensure that the contact center 150 complies with all laws and regulations regarding agent 120 work hours (Fig. 1 and [0017]). The slack engine 210 is configured to predict or detect what are referred to herein as slack periods for the contact center 150. A slack period is one or more intervals of time where the number of agents 120 assigned to work for the contact center 150, or on a particular queue, exceeds the number of agents 120 that are needed to meet one or more service level goals for the contact center 150 or queue 120 [0018]. The slack engine 210 may predict a slack period for one or more intervals of a plurality of intervals. The intervals of the plurality of intervals may each have the same time duration and may correspond to the smallest amount of time that can be scheduled in the contact center 150. For example, depending on the needs of the contact center 150, each interval may have a duration of fifteen minutes, thirty minutes, or one hour. Other time intervals may be used [0022]. A slack period may be predicted for an entire contact center 150, may be predicted on a queue by queue basis, or may predicted with respect to a particular agent group or set of agent skills [0023].The slack predictor 220 may further predict slack periods using one or more slack rules. The slack rules may be based on criterial such as how many excess agents 120 are required for an interval to be considered part of a slack period or what percentage of the interval is required to be overstaffed for the interval to be considered part of a slack period [0027]. For example, a slack rule that matches short slack periods may be: staffing is >=X staffing (default X=5) for a 30 minute period. As another example, a slack rule that matches long slack periods may be: staffing is >=X staffing (default X=5) for X % (default X=75%) of an interval for >than a 30 minute period [0028].)
f) Krucek teaches when an optimum interval has been found, automatically, adjusting the agent-schedule accordingly by updating the agent-schedule in the real time agents-schedule cache with the unavailability-interval type in the optimum interval. (Krucek e.g. When slack periods are predicted or detected, the system encourages the employees to perform QM tasks during the slack periods and can even automatically schedule the QM tasks for the employees (Abstract and [0003]). Moreover, the slack engine 210 is not limited to the performance of QM tasks but can be used to perform any type of task that can be associated with an entity or employee [0020]. The slack engine 210 may predict two types of slack periods. The first type of slack period is referred to herein as a forecast slack period and the second type of slack period is referred to herein as an intraday slack period [0023]. Intraday slack periods may be slack periods that are predicted for a current day on a real-time or near real-time basis [0029]. After detecting or predicting a slack period, the slack predictor 220 may send a request 246 to the administrator 290 asking the administrator 290 to either confirm or reject the slack period [0031]. The notification engine 230 may send notifications 245 to one or more agents 120 in response to a predicted (and confirmed) slack period. The notification 245 may be an electronic message (e.g., email or SMS) and may be sent to each agent 120 associated with the slack period [0035]. Each notification 245 sent to an agent 120 may include identifiers of one or more tasks 257 that the agent 120 could complete during the slack period. The tasks 257 may be QM tasks such as performing a self-evaluation, meeting with a supervisor, receiving coaching, or taking one or more classes or courses [0036]. In some embodiments, the notification engine 230 may display slack periods where agents 120 are scheduled to work as events on one or more calendars associated with each agent 120. The event for an agent 120 may include identifiers of any tasks 257 that may be completed by the agent 120 during the slack period [0038]. The notification engine 230 may automatically schedule agents 120 to QM tasks 257 during a slack period [0046].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Marr’s real time monitoring system of agent adherence process to include finding an optimal interval…wherein the optimum interval that is overstaffed and having a highest staffing gap and automatically adjusting the agent’s schedule with QM tasks as taught by Krucek in order to avoid agents being idle or underutilized during slack periods (Krucek e.g. [0019]).
As per claim 2 (Previously Presented), Marr in view of Krucek teach he computerized-method of claim 1, Marr teaches wherein schedules of each agent in the real time agents-schedule cache are synchronized every preconfigured interval with a Workforce Management (WFM) system (Marr e.g. System 100 comprises a real-time monitoring system 102 which collects data and events from multiple sources... including multiple agent phone routers 104 and the web application server 118 which collects data from the agent scheduling system 114 (i.e. WFM), and timekeeping system 130 (col. 2 lines 57-63). Agent scheduling system 114 (i.e. WFM) generates agent schedules 15 based on previously created schedules, which are fetched from the web application server 118 (i.e. EEM) by real-time monitoring system 102 (col. 3 lines 15-18). Real-time monitoring system 102 collects updates from multiple sources in real time (i.e. preconfigured interval) and routes new normalized data to Web application server 122. In turn, Web application server 122 generates new user interface with the new normalized data and present the user interface to the applications (col. 4 lines 43-48). Because the displays are continuously refreshed by real time monitoring system as new events and data are processed, a user may keep monitoring the status of the agent by selecting the agent (col. 7 lines 56-59).)
As per claim 3 (Original), Marr in view of Krucek teach the computerized-method of claim 1, Marr teaches wherein the unavailability-interval is selected from at least one of: break, lunch, meeting, coaching, back office or any other activity. (Marr e.g. Real-time monitoring system 102 also collects events from time keeping system 130 through web application server 118. Time keeping information includes clocked-in data of agents as well as lunch and break information. The time keeping data 10 may be refreshed up to 12 times an hour (col. 3 lines 7-11). In one embodiment, the clocked-in data indicates whether an agent is clocked in or out. If the agent is clocked out, the time keeping data also indicates whether the agent is out for break, for lunch, or sick (col. 3 lines 11-14).)
As per claim 4 (Original), Marr in view of Krucek teach the computerized-method of claim 1, Marr teaches wherein the calculating of the interval range is based on work-rules related to the unavailability-interval type. (Marr e.g. Fig. 7, Time clock state 706 includes time clock status 726, and state duration 728. The timeclock state 726 indicates the agent's current status on the timekeeping system. State Duration 728 is the amount of time that the agent has spent in that timeclock state. The time clock may be obtained from the agent's system and the time clock information may be updated twelve times an hour. The time clock state 728 may be clocked in, clocked out, and lunch and break (col. 6 lines 47-54).)
As per claim 6 (Previously Presented), Marr in view of Krucek teach the computerized-method of claim 1, Marr in view of Krucek teach when no optimum interval has been found then sending a notification to a computerized-device of the agent with a suggestion to conclude current activity within a predefined threshold and to precede the next-interval-scheduled activity to current-time and upon receiving an approval from the agent, adjusting the agent-schedule in the real time agents-schedule cache based on the time that the agent moved to the next-interval-scheduled activity.
Marr teaches….updating the agent-schedule in the real time agents-schedule cache (Marr e.g. Agent scheduling system 114 generates agent schedules 15 based on previously created schedules, which are fetched from the web application server 118 by real-time monitoring system 102 (col. 3 lines 15-18). Real-time monitoring system 102 collects updates from multiple sources in real time and routes new normalized data to Web application server 122. In turn, Web application server 122 generates new user interface with the new normalized data and present the user interface to the applications (col. 4 lines 43-48). A view of real-time agent status may include headcounts, scheduled agents, clocked-in agents, and adherence state of the agents (col. 3 lines 59-61). The present disclosure provides real-time monitoring of agent adherence to a work schedule. Data and events, including events from call router, time clock information and schedule events, are collected from multiple sources and normalized using a server side process. A view of details for sites, skills, and agents are provided to management via a Web user interface and refreshed at real time to provide updated schedule adherence information (col. 8 lines 57-64).)
Marr does not explicitly teach, however, Krucek teaches when no optimum interval has been found then sending a notification to a computerized-device of the agent with a suggestion to conclude current activity within a predefined threshold and to precede the next-interval-scheduled activity to current-time and upon receiving an approval from the agent, adjusting the agent-schedule … based on the time that the agent moved to the next-interval-scheduled activity (Krucek e.g. FIG. 3 is an illustration of an example method 300 for predicting one or more forecast slack periods. The method 300 may be implemented by the slack engine 210 of the contact center 150 [0048]. At 320, a slack period is detected based on the schedule and the forecast. The slack period may be a forecast slack period and may be detected by the slack predictor 220 using one or more slack rules. The slack period may be associated with a confidence value [0051]. At 325, whether the confidence is greater than a threshold is determined. The determination may be made by the slack predictor 220. If the confidence is greater than the threshold then the method 300 may continue at 345. Else, the method 300 may continue at 330 [0052]. At 330, confirmation is requested. The confirmation may be requested by the slack predictor 220 sending a request 246 to the administrator 290 [0053]. At 335, whether confirmation was received is determined. The determination may be made by the slack predictor 220. If the confirmation 255 was received, then the method 300 may continue at 345. Else, a rejection 256 was received, and the method 300 may continue at 340 [0054]. At 345, a notification is sent. The notification 245 may be sent by the notification engine 230 to one or more agents 120 scheduled to work during the slack period. Each notification 245 may identify one or more QM tasks 257 that the associated agent 120 could complete during the slack period. Alternatively, or additionally, the notification engine 230 may automatically add one or more of the identified tasks 257 to the schedule 215 for the agent 120 to complete during the slack period [0056].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Marr’s real time monitoring system of agent adherence process to include sending a notification to agents suggesting schedule adjustments and then adjusting the agent’s schedule as taught by Krucek in order to avoid agents being idle or underutilized during slack periods (Krucek e.g. [0019]).
As per claim 7 (Previously Presented), Marr in view of Krucek teach the computerized-method of claim 6, Marr in view of Krucek teach wherein the adjusting of the agent-schedule is operated by updating the agent-schedule in the real time agents-schedule cache with the next-interval-scheduled activity starting in the current-time.
Marr teaches….updating the agent-schedule in the real time agents-schedule cache (Marr e.g. Agent scheduling system 114 generates agent schedules 15 based on previously created schedules, which are fetched from the web application server 118 by real-time monitoring system 102 (col. 3 lines 15-18). Real-time monitoring system 102 collects updates from multiple sources in real time and routes new normalized data to Web application server 122. In turn, Web application server 122 generates new user interface with the new normalized data and present the user interface to the applications (col. 4 lines 43-48). A view of real-time agent status may include headcounts, scheduled agents, clocked-in agents, and adherence state of the agents (col. 3 lines 59-61). The present disclosure provides real-time monitoring of agent adherence to a work schedule. Data and events, including events from call router, time clock information and schedule events, are collected from multiple sources and normalized using a server side process. A view of details for sites, skills, and agents are provided to management via a Web user interface and refreshed at real time to provide updated schedule adherence information (col. 8 lines 57-64).)
Marr does not explicitly teach, however, Krucek teaches wherein the adjusting of the agent-schedule is operated by updating the agent-schedule … with the next-interval-scheduled activity starting in the current-time (Krucek e.g. In some embodiments, the notification engine 230 may display slack periods where agents 120 are scheduled to work as events on one or more calendars associated with each agent 120. The event for an agent 120 may include identifiers of any tasks 257 that may be completed by the agent 120 during the slack period [0038]. The notification engine 230 may automatically schedule agents 120 to QM tasks 257 during a slack period [0046]. FIG. 5 is an illustration of an example method 500 for sending a notification in response to a slack period. The method 500 may be implemented by the slack engine 210 of the contact center 150 [0066]. At 530, notification is sent to the at least one agent. The notification 245 may be sent by the notification engine 230 to the at least one agent 120. The notification 245 may indicate the slack period to the at least one agent 120 along with the determined task 257. The agent 120 may schedule the task 257 through the notification 245 [0071].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Marr’s real time monitoring system of agent adherence process to include adjusting the agent’s schedule with the next-interval-scheduled activity as taught by Krucek in order to avoid agents being idle or underutilized during slack periods (Krucek e.g. [0019]).
As per claim 8 (Previously Presented), Marr in view of Krucek teach the computerized-method of claim 1, Marr teaches when the agent is not in adherence-state and the agent- state is on-call and the agent currently scheduled-activity in the real time agents-schedule cache is an unavailability-interval type. (Marr e.g. System 100 comprises a real-time monitoring system 102 which collects data and events from multiple sources... including multiple agent phone routers 104 and the web application server 118 which collects data from the agent scheduling system 114, and timekeeping system 130 (col. 2 lines 57-63). Agent scheduling system 114 generates agent schedules 15 based on previously created schedules, which are fetched from the web application server 118 by real-time monitoring system 102 (col. 3 lines 15-18). A view of real-time agent status may include headcounts, scheduled agents, clocked-in agents, and adherence state of the agents (col. 3 lines 59-61). FIG. 3 is a diagram illustrating a display of details (e.g. adherence) for all sites (col. 4 lines 57-58). These details include the adherence state, which is whether the agent is in adherence or out of adherence. The details also include reasons for being out of adherence, including the number of agents inactive on the switch, working but not scheduled, working and schedule mismatch, working the wrong skill, clocked in but not working and not scheduled, on break but working, at lunch but working and clocked out but scheduled and working (Fig. 6 and col. 6 lines 3-11). Marr does not explicitly teach, however, Krucek teaches automatically adjusting the agent schedule (Krucek e.g. In some embodiments, the notification engine 230 may display slack periods where agents 120 are scheduled to work as events on one or more calendars associated with each agent 120. The event for an agent 120 may include identifiers of any tasks 257 that may be completed by the agent 120 during the slack period [0038]. The notification engine 230 may automatically schedule agents 120 to QM tasks 257 during a slack period [0046].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Marr’s real time monitoring system of agent adherence process to include adjusting the agent’s schedule as taught by Krucek in order to avoid agents being idle or underutilized during slack periods (Krucek e.g. [0019]).
As per claim 9 (Previously Presented), Marr in view of Krucek teach the computerized-method of claim 8, Marr in view of Krucek teaches wherein the adjusting of the agent-schedule is operated by updating the agent-schedule in the real time agents-schedule cache when agent-state from the ACD system changes accordingly.
Marr teaches monitoring real time updates in the agent-schedule and ACD system changes (Marr e.g. System 100 comprises a real-time monitoring system 102 which collects data and events from multiple sources... including multiple agent phone routers 104 (i.e. ACD) and the web application server 118 which collects data from the agent scheduling system 114, and timekeeping system 130 (col. 2 lines 57-63). Agent scheduling system 114 generates agent schedules 15 based on previously created schedules, which are fetched from the web application server 118 by real-time monitoring system 102 (col. 3 lines 15-18). Agent phone routers 104 (i.e. ACD system) provide phone states and skills of the agents to real-time monitoring system 102. An example of phone state may be whether the agent is active or inactive on the switch (col. 2 lines 64-67). Real-time monitoring system 102 collects updates from multiple sources in real time and routes new normalized data to Web application server 122. In turn, Web application server 122 generates new user interface with the new normalized data and present the user interface to the applications (col. 4 lines 43-48). A view of real-time agent status may include headcounts, scheduled agents, clocked-in agents, and adherence state of the agents (col. 3 lines 59-61).)
Marr does not explicitly teach, however, Krucek teaches wherein the adjusting of the agent-schedule is operated by updating the agent-schedule…when agent-state from the ACD system changes accordingly (Krucek e.g. A system for predicting slack periods is provided. The system predicts and detects time intervals for an entity (e.g., a contact center) where the amount of available work (e.g., call volume) is less than what can be handled by the number of employees (e.g., agents) that are scheduled to work during the time intervals. These detected intervals are referred to herein as "slack periods". (Abstract and [0003]). FIG. 1 is an example system architecture 100, and illustrates example components, functional capabilities and optional modules that may be included in a cloud-based contact center infrastructure solution. Depending on the embodiment, the contact routing system 153 could be any of a contact center as a service (CCaS) system, an automated call distributor (ACD) system, or a case system, for example [0014]. Once the slack periods are predicted, rather than some of agents 120 be idle or underutilized during the slack period, the slack engine 210 may notify the agents 120 scheduled to work during the slack period and may offer them incentives to work on one or more QM tasks during the slack period [0019]. The notification engine 230 may send notifications 245 to one or more agents 120 in response to a predicted (and confirmed) slack period. The notification 245 may be an electronic message (e.g., email or SMS) and may be sent to each agent 120 associated with the slack period [0035]. Each notification 245 sent to an agent 120 may include identifiers of one or more tasks 257 that the agent 120 could complete during the slack period. The tasks 257 may be QM tasks such as performing a self-evaluation, meeting with a supervisor, receiving coaching, or taking one or more classes or courses [0036]. The notification engine 230 may automatically schedule agents 120 to QM tasks 257 during a slack period [0046].)
The Examiner submits that before the effe