DETAILED OFFICE ACTION
Status of the Application
This Office Action is in response to Application Serial 18/747,517. In response to Examiner’s action mail dated September 23, 2025, Applicant submitted arguments and amendments mail dated January 13, 2026. Claims 1-4, 6-7 are examined 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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
Applicant did not submit an information disclosure statement (IDS) for consideration.
Response to Amendments
Claims 1-4, 6-7 are pending in this application. The claim(s) 1,6, and 7 are amended. Claim 5 is cancelled.
Regarding the 35 U.S.C. 112 rejection, Applicant’s amendments are persuasive.
Regarding the 35 U.S.C. 101 rejection, Applicant’s arguments and amendments are not persuasive. The claims 1-4, 6-7 are pending.
Regarding the 35 U.S.C. 103 rejection, Applicant’s arguments and amendments are not persuasive. The claims 1-4, 6-7 are pending.
Response to Arguments
Applicant’s arguments filed on January 13, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below.
35 U.S.C. 112 Rejections
On page 5 of the Applicant’s 35 U.S.C. 112 argument, Applicant amended the claim 1 to clarify the terms “tti”, as True To Interval.
Examiner submits, the Applicant amendments to the claims are persuasive. The 35 U.S.C. 112 rejection is withdrawn.
35 U.S.C. 101 Rejections
On page 6 of the Applicant’s 35 U.S.C. 101 arguments, the Applicant traverses, the amended claims 1 as a whole, integrates an abstract idea into a practical application, by automatically scheduling agents for the one or more future-schedules in the period based on the general forecast. The Applicant asserts that all limitation of independent claim 1 is allowable. Claims 2-4 and 6-7 depend, directly or indirectly, from claim 1 and therefore include all limitations of this claim. Therefore, Applicant respectfully asserts that claims 2-4 are allowable. Accordingly, Applicant respectfully requests that the Examiner withdraw the rejections to independent claim 1 and to claims 2-4 and 6-8, depend therefrom.
Examiner respectfully disagrees with Applicant’s 35 U.S.C. 101 argument. Examiner submits the claims do not amount to significantly more, and therefore, the claims are not patentable under the Subject Matter Eligibility Guidance.
The claims recite abstract concepts. The claims recite the abstract concept of calculating a true-to-interval analytic, which is a mathematical concepts. Furthermore, the claims recite polling data-feed, calculating a true-to-interval analytic in a contact center to schedule agents, thus, the claims recite a commercial activity. Polling is a method of collecting data that can be completed by a human gathering information like a questionnaire. Therefore, the claims recite certain methods of organizing human activity. The pending claims recite the abstract groupings of mathematical concepts and certain methods of organizing human activity, and therefore, are directed to a judicial exception at Step 2A prong one.
The claims are not integrated into a practical application. The claims are evaluated to determine if the additional elements are integrated into the judicial exception. The claims are evaluated to determine if there is an improvement. The claims recite the additional elements of an Automatic Contact Distributor (ACD), True-To-Interval database, Workforce Management (WFM) application. Examiner reviewed the application to determine if the additional elements are integrated into the judicial exception. Here, the additional elements are used to conduct the judicial exception, which is MPEP 2106.05(f) (Adding the words “apply it” (or an equivalent with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. ) )
The claims do not recite an improvement that is rooted in technology. The claims recite an improvement to the abstract idea of scheduling as recited in the application when, for example, obtaining and storing true-to-interval parameters, calculating total interval-handle-time, and configuring a Workforce Management application to generate a forecast period having one or more future schedules. Instead, the analytics obtained, stored, polled, and retrieved improve the accuracy of forecasting and scheduling data, and thus, improve the judicial exception. The claims are using a computer to improve the gathering of analytical data that is used to improve scheduling. The additional elements are not indicative of integration into a practical application at Step 2A prong two.
At Step 2B, the claims are evaluated to determine if the claims provide an inventive concept, i.e., does the claim recite additional elements(s) or a combination of elements that amount to significantly more. As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using a generic component (polling data-feed from the Automatic Contact Distributor application.) The claims are using mere instruction to apply the judicial exception is not an inventive concept.
At Step 2B, the claims are reviewed for a technical solution to a technological problem. Here the same conclusion is reached. That is, simply implementing the abstract idea on a computer or merely using a computer as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The Applicant’s claims are polling data-feed from the Automatic Contact Distributor application and obtaining and storing true-to-interval parameters, calculating total interval-handle-time, and configuring a Workforce Management application to generate a forecast period having one or more future schedules the computer are merely executing an instruction. Mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A, or provide an inventive concept in Step 2B.
At Step 2B, as discussed in Step 2A, the claims do not recite an improvement that is rooted in technology. It is MPEP 2106.05(f), see above. The claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception.
The claims pending claims remain rejected under 35 U.S.C. 101.
35 U.S.C. 103 Rejections
On pages 7-8 of the Applicant’s 35 U.S.C. 103 arguments, the Applicant traverses, the application provides a technical solution that will convert data received from Automatic Contact Distributor (ACD) applications, including time based work items to be consumed by a Workforce Management WFM system. The Time to Interval (TTI) analytics, aims to improve accuracy of forecasting and scheduling data.
Applicant traverses Kosiba doesn’t operate real-time calculation and transformation of ACD event data. Applicant traverses Kosiba uses simulation models for predictive analytics whereas the current application implements an interval based comparison and calculation. Applicant traverses Kosiba are long-term forecasts which are used for strategic planning and the current application yields true-to interval staffing calculation, short-term.
Examiner respectfully disagrees with the Applicant’s arguments. The claims do not necessitate a short-term staffing calculation. The claims do not necessitate real-time calculations. The claims recite the use of databases where data is stored.
Applicant amended the claims. Applicant asserts the rejection should be withdrawn.
Examiner submits, the Applicant’s amendments to the claims necessitate grounds for a new rejection. See pending prior art rejection below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-7 are process.
Claims 1-4, 6-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims (claim 1) recites, “… during a shift-schedule having one or more time-intervals, (i) calculating total interval-handle-time and total interval-hold-time for each contact for each time-interval by: every preconfigured time-period in the time-interval:
a. … data-feed … ; and b. obtaining true-to-interval parameters from the … data-feed, and storing the true-to-interval parameters with start-time of the preconfigured time-period, … , wherein the true-to-interval parameters comprising for each contact:
1) a state of activity; 2) handle-time duration; and 3) hold-time duration,
(ii) at the end of the time-interval, calculating number of contacts having the activity state, based on the true-to-interval parameters…; (iii) storing the calculated total interval-handle-time and total interval-hold-time …; (iv) calculating a total handle-time for all contacts during the time-interval, and storing the total handle-time for all contacts in the tti-database; (v) repeating operations (i) - (iv) for each time-interval in the shift-schedule; and (vi) retrieving the calculated total handle-time and total hold-time of each time- interval and a total handle-time of one or more shift-schedules … and transmitting it…., over a communication channel to enable … true-to-interval analytics … to generate a forecast for a period having one or more future-schedules base on the transmitted total handle-time and total hold-time of each time-interval and the total handle-time for each contact of one or more shift-schedules, and automatically schedule agents for the one or more future-schedules in the period. The claims recite the abstract concept of using interval- handle-time and total interval-hold-time for calculating operations analytics and using the analytics to create a schedule.
The limitations “i) calculating total interval-handle-time and total interval-hold-time for each contact for each time-interval by: every preconfigured time-period in the time-interval: … (ii) at the end of the time-interval, calculating number of contacts having the activity state, based on the true-to-interval parameters…; … (iv) calculating a total handle-time for all contacts during the time-interval, and storing the total handle-time for all contacts ; …… to generate a forecast for a period having one or more future-schedules base on the transmitted total handle-time and total hold-time of each time-interval and the total handle-time for each contact of one or more shift-schedules …are mathematical concepts – calculation.
The limitations retrieving the calculated total handle-time and total hold-time of each time- interval and a total handle-time of one or more shift-schedules … and transmitting it…., over a communication channel to enable … true-to-interval analytics … to generate a forecast for a period having one or more future-schedules base on the transmitted total handle-time and total hold-time of each time-interval and the total handle-time for each contact of one or more shift-schedules, and automatically schedule agents for the one or more future-schedules in the period are certain methods of organizing human activity – managing personal behavior and commercial activities.
The claims recite the abstract concepts that are within the mathematical grouping and a certain method of organizing human activity groupings, therefore the claims are directed to a judicial exception. The claims are directed to judicial exception under the first prong of Step 2A.
This judicial exception are not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of, “A computerized-method for enabling true-to-interval analytics from an Automatic Contact Distributor (ACD) application, said computerized- method comprising:”, “polling”, “a True To Interval (tti)-database”, “a Workforce Management (WFM) application”, in claim 1; however, when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05 (f)
The dependent claims do not recite additional elements that are not recited in the dependent claims.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also 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 transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, because the additional elements when considered both individually and as an ordered combination do not amount to significantly more. (See MPEP 2106.05 (f) Mere Instruction to Apply an Exception – Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct at 235).
At Step 2B, it is MPEP 2106.05 (d) – Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function (s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified exception (the abstract idea). Looking at the limitation as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent claims 2-4, 6-7 further narrow the abstract idea of independent claim 1. The claims 1-4, 6-7 are not patent eligible.
Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-4, 6-7 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-4, 6-7 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claim(s) 1-4, 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (2019, Predicting Call Center Performance with Machine Learning) in view of McSwiggan (US 11501229 B2).
Regarding Claim 1,
A computerized-method for enabling true-to-interval analytics from an Automatic Contact Distributor (ACD) application, said computerized-method comprising: during a shift-schedule having one or more time-intervals,
Li evaluates the performance of call centers., Li [abstract]. Li discloses quality of service (QoS) in each period (e.g., periods)., Li [p. 193].
calculating total interval-handle-time and total interval-hold-time for each contact for each time-interval by: every preconfigured time-period in the time-interval:
a. polling data feed from the ACD application; and
b. obtaining true-to-interval parameters from the polled data-feed, and storing the true-to-interval parameters with start-time of the preconfigured time-period, in a True-To-Interval tti -database, wherein the true-to-interval parameters comprising for each contact:
1) a state of activity;
2) handle-time duration;
and 3) hold-time duration,
Li discloses a simulation model for a call center with multi-skill agents and multi-class customers to evaluate the performance of call centers., Li [abstract]. Categories Li considers in the workforce scheduling to predict QoS are forecasting, staffing, shift scheduling, and rostering., Li [p.193-194].
Li discloses with respect to the QoS measurement, we choose the service level (SL), which is most commonly used in call centers. It is defined as the proportion of customers who wait less than a given time threshold, also known as the acceptable waiting time (AWT), over a time period., Li [p. 195].
Although polling data/information can be done by a human using pen and paper and communication, Examiner understands in this application polling data is done to determine whether the computer has data to transmit. Therefore, polling data feed is known to one of ordinary skill in the art as transmitting data. Examiner points to Takagi (1991, Application of polling to computer networks).
Examiner acknowledges the True to Interval (TTI) is the name of the analytic used by NICE, the Assignee. See NPL White paper Nice True to Interval (TTI) FAQs and CXone WFM’s True to Interval.
(ii) at the end of the time-interval, calculating number of contacts having the activity state, based on the true-to-interval parameters in the tti-database;
(iii) storing the calculated total interval-handle-time and total interval-hold-time in the tti-database;
(iv) calculating a total handle-time for all contacts during the time-interval, and storing the total handle-time for all contacts in the tti-database;
(v) repeating operations (i) - (iv) for each time-interval in the shift-schedule;
and (vi) retrieving the calculated total handle-time and total hold-time of each time-interval and a total handle-time of one or more shift-schedules from the tti-database
In Li, the service levels (SL) are evaluated, Li [abstract]. Evaluation considers the SL of given schedules of shift types. Li [p.196]. Li develops a simulation model. The Simulation models run scenarios to predict performance; therefore, the process is iterative., Li [0198]
Li introduce[es] a machine learning algorithm that is trained on simulation results to predict the QoS measurements … for call centers, … generat[ing] a number of possible schedules with their corresponding QoS. … Li train[s] a machine learning algorithm on these schedules and are then able to quickly produce a “look-up table” with the QoS of all possible schedules., Li [p. 194]. The “look-up-table” is a database.
Service level (SL) which is most commonly used in call centers. service level (SL) examples include method of service and wait time thresholds., See Li [p.195].
Li approximate[s] the simulation with a machine learning algorithm so that a near optimal schedule can be found efficiently., Li [p.195]. Gradient Boosted Decision Tree is an iterative algorithm and it works by training a new regression tree for every iteration to minimize the residual of predictions made by the previous iteration. The predictions of the new iteration are then the sum of the predictions made by the previous iteration and the prediction of the residual made by the newly trained regression tree in the new iteration., Li [p.196].
and transmitting it to a Workforce Management (WFM) application, over a communication channel to enable the WFM application true-to-interval analytics.
Li develop[s] a general simulation model for call centers, and under any given scenario, … randomly generate a number of possible schedules with their corresponding QoS, Li [p.194]. ; Once the simulation model is validated, … model the simulation outcomes as a prediction problem, and then train a machine learning algorithm to predict SL outcomes for each scenario and schedule., Li [p.195].
One of ordinary skill of the art would understand, running simulation on a computer, require a computer application. Therefore, it would be obvious that Li teaches the functions of running a simulation application.
Examiner does not rely on Li to teach:
configuring a Workforce Management (WFM) application to generate a forecast for a period having one or more future-schedules base on the transmitted total handle-time and total hold-time of each time-interval and the total handle-time for each contact of one or more shift-schedules, and automatically schedule agents for the one or more future-schedules in the period.
Although highly suggested in Li, Examiner relies on McSwiggin to teach:
True-to-Interval …. configuring a Workforce Management (WFM) application to generate a forecast for a period having one or more future-schedules base on the transmitted total handle-time and total hold-time of each time-interval and the total handle-time for each contact of one or more shift-schedules, and automatically schedule agents for the one or more future-schedules in the period.
McSwiggin [column 4 lines 46-48] discloses the number of work items 103 in the work queue 102 may continually change and the system will dynamically adjust to include all waiting work items in the work queue 102 when received by the Work Allocation Enginee (WAE 110). McSwiggin [column 4 lines 49-59] discloses the list of agents 104 contains the customer service representatives (CSR) for the Customer Engagement Center (CEC) system. The list of agents 104 may be a list of all agents for the CEC system, scheduled to work the current work shift, the list of agents 104 may be dynamically generated based on the CSRs who have signed into the CEC system, and/or the list of agents 104 may be at various stages of preforming work and may or may not be available to be assigned work. McSwiggin [column 4 lines 46-59], [Figure 2].
McSwiggin Figure 3 items 102, 104, 103, 105, 115, 127 and the associated text teach work allocation optimization.
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Li teaches simulated-based machine learning framework to evaluate the performance of call centers having heterogeneous servers and demand. McSwiggin teaches analytics are applied to work items while the work items are waiting in a work queue in order to optimize the routing and allocation of work items to agents It would have been obvious to combine before the effective filing date, implementing machine learning algorithms to produce a “look-up table” with the QoS of schedules using a computer to complete the computation, as taught by Li, with performing a look ahead at more than the initial work item, the system assesses the agent skills required by imminent work items in the work queue, as taught by McSwiggin, to optimize[ing] the assignment of agents to work items they are most qualified to handle and thereby minimizing the routing of work items to agents not qualified to handle those work items, while at the same time minimizing tying up specialty agents with work items they are overqualified to handle. McSwiggan [column 2 lines 4-11]
Regarding Claim 2,
The computerized-method of claim 1, wherein the state of activity is at least one of: (a) received; (b) answered; (c) active; and (d) hold.
See above. See measured Services Levels (SL) and Quality of Service (QoS) (e.g., wait time)., Li [p.195]. Li teaches conditions such as heavy-traffic systems, short service times, impatient customers, and specific routing policies., Li [0194].
Within claim 2, Li discloses wait times and service times, and thus, Li discloses (a) received; (b) answered; (c) active; and (d) hold. Claim 2 a "Markush" claim recites a list of alternatively useable members. In re Harnisch, 631 F.2d 716, 719-20, 206 USPQ 300, 303 (CCPA 1980); Ex parte Markush, 1925 Dec. Comm'r Pat. 126, 127 (1924). The listing of specified alternatives within a Markush claim is referred to as a Markush group or a Markush grouping. Abbott Labs v. Baxter Pharmaceutical Products, Inc., 334 F.3d 1274, 1280-81, 67 USPQ2d 1191, 1196 (Fed. Cir. 2003) (citing to several sources that describe Markush groups)- See MPEP 706.03.
Regarding Claim 3,
The computerized-method of claim 2, wherein the total interval-handle-time for each contact is calculated by summing one or more preconfigured time-periods in the time-interval that the contact is in active state, and wherein the total interval-hold-time for each contact is calculated by summing one or more preconfigured time-periods in the time-interval that the contact is in hold state.
See above. See measured services levels (SL) and quality of Service (QoS)., Li [p.195]. Li discloses forecasting and QoS intervals., Li [0193].
Regarding Claim 4,
The computerized-method of claim 3, wherein the total handle-time of each shift-schedule in the one or more shift-schedules is calculated by summing the total handle-time for all contacts during the one or more time-intervals of the shift-schedule.
Li discloses a simulation model for a call center with multi-skill agents and multi-class customers to evaluate the performance of call centers., Li [abstract]. Categories Li considers in the workforce scheduling to predict QoS are forecasting, staffing, shift scheduling, and rostering., Li [p.193-194].
Regarding Claim 5, (Cancelled)
Regarding Claim 6, (Currently Amended)
The computerized-method of claim 1, wherein the computerized-method further comprising displaying the generated forecast for the period via a User interface (UI) that is associated to the WFM application and upon user-click on an icon on the UI, automatically sending a notification of the generated forecast to a computerized-device of a user, for review.
See above. Li teaches historical and actual data is feed into a machine learning model to simulate performance and predict staff schedules., Li [p. 194- 195], [0196].
One of ordinary skill of the art would understand, running simulation on a computer, require a computer selection. Therefore, it would be obvious that Li teaches the functions of a user interface such as clicking buttons, to run a simulation.
Regarding Claim 7, (Currently Amended)
The computerized-method of claim 1, wherein the forecast for the period includes agents requirement for each future-time-interval in each future-schedule in the one or more future-schedules.
See above. Li teaches historical and actual data is feed into a machine learning model to simulate performance and predict staff schedules., Li [p. 194- 195], [0196].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Takagi (1991, Application of polling models to computer networks.) provides an example of polling data using a computer.
Kosiba (WO 0,2093,321 A2) reaches receiving performance information from a performance monitoring system associated with the processing center system.
CXone WFM’s True to Interval, Copyright date 2024 retrieved 03/22/202.
NICE TRUE TO INTERVAL (TTI) FAQ Whitepaper copyright 2024
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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/THEA LABOGIN/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624