DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending and examined herein per Applicant’s 01/20/2026 submission to the Office. Claims 1, 10, and 20 are amended. No claims are withdrawn, canceled, or newly added.
Response to Amendment
Applicants’ amendments to the specification and drawings are sufficient to overcome the drawing objections of the previous Office action.
Applicants’ amendments to the claims are NOT sufficient to overcome the statutory claims rejections of the previous Office action.
Response to Arguments
Applicant's arguments filed with respect to the 35 USC 101 rejection of the previous Office action have been fully considered but they are not persuasive. Applicant makes the following arguments:
This limitation provides tangible control of routing data in a computer system, and cannot be considered insignificant post-solution activity. This limitation further removes the claims from the category of organizing human activity, as automatically routing a data stream representing an interaction based on the modified plan does not manage personal behavior, relationships or interactions. Remarks p.10.
Respectfully, the Office disagrees with Applicant’s position. In support of his argument, Applicant cites to specification par. [135] however the improvements provided in the specification for example “formulate plans quickly” are found to come from the capabilities of the computer itself rather than the claimed invention. For this reasons the rejection of the previous Office action is maintained as updated below.
Per Office Guidance claims are not mental steps if "the human mind is not equipped to perform the claim limitations." October 2019 Guidance at 7. A human is not equipped to, as a practical matter, perform operations in the mind or using pen and paper which, based on a plan, automatically route a data stream representing an interaction, under a reasonable interpretation. Remarks p.11.
Respectfully, the Office disagrees with Applicant’s position. The limitation in question is not identified as an element of the abstract idea, but rather insignificant extra data solution activity – see updated rejection below. Where the claim “automatically route a data stream” is view as the equivalent of transmitting or presenting the result of the analysis. Where the instant specification provides “automatic call distributor may route contacts of the contact center (e.g. distribute data streams representing interactions or calls via computer hardware as discussed herein, or other hardware) to agents.” This would be the equivalent of a human operator deciding which agent to route a call to and then sending the call to that agent (mental process done in a computing environment). Where the MPEP provides, “An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". 838 F.3d at 1318, 120 USPQ2d at 1360.” MPEP 2106.04(a).
Further MPEP provides in the case of Electric Power Group v. Alstom S.A. 830 F.3d 1350 (Fed. Cir. 2016) the court held we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. MPEP 2106.04(a)(2)(III)(case citations omitted). That is to say routing the call is the same as showing the answer to the analysis. For this reasons the rejection of the previous Office action is maintained as updated below.
The improvement provided by the amended independent claims indicates any alleged abstract idea is patent eligible. Ex parte Guillaume Desjardins, PTAB decision on Appeal 2024-000567, at 8 (based on improvement in the training of the machine learning model itself). As in Ex parte Guillaume Desjardins, the present Specification details these improvements to technology. Remarks p. 12.
It is unclear what part of the decision Applicant is arguing with respect to the 101 rejection. The Examiner was affirmed and additional grounds for rejection were added to the 101 rejection – see PTAB 03/04/2025 decision. The arguments cannot be addressed. Additionally, it is noted that this PTAB decision has no bearing on the instant claimed invention.
Applicant's arguments filed with respect to the 35 USC 102 rejection of the previous Office action have been fully considered but they are not persuasive. Applicant makes the following arguments:
D’Attilio teaches that "the system can use column generation to iteratively identify good candidate shift schedules" (D’Attilio para. [0087]). This iterative process does not teach any performance metrics of claim 1 element. D’Attilio para. [0087] does not describe a performance metric, or one metric being less than another metric. Remarks p. 13.
Respectfully the Office disagrees with Applicant’s position. D’Attilio teaches, “workload forecasting model may be defined as the best method to apply given time series data and a set of optimal parameters that yield optimal key performance indicator (KPI) metrics” (D’Attilio [58]). D’Attilio also teaches, “system may leverage a service performance calculator built using the validated and optimized contact center model that takes in the workload forecast and the number of agents to produce a predicted set of KPIs. In order to get the optimal staffing requirement level, the number of agents may be increased iteratively (e.g., using a bisection algorithm) until the KPIs predicted by the calculator meet the desired, specified KPI goals.” The Office maintains D’Attilio teaches performance metrics.
D’Attilio also teaches “the system compares various forecasting methodologies based on the identified patterns” (D’Attilio [68]) and “time series input data may be summarized and subsequently forecasted at different time hierarchies (or granularities) in order to gain better accuracy” (D’Attilio [64]). Further teaching, “the system utilizes custom mathematical modeling via a data-driven, machine learning based approach, validated against historical ACD data, by combining the best of the options described above.” (D’Attilio [79]). Finally, “the master problem lower bound may be used to terminate the algorithm. In particular, the lower bound is valid for every iteration i in the column generation algorithm, and the system uses this lower bound to terminate the algorithm if current lower bound z.sub.i.sup.LB is greater than z.sub.i.” (D’Attilio [106]). Where one of ordinary skill in the art would recognize the teaching to anticipate one metric being less than another metric.
For these reasons the rejection of the previous Office is maintained as updated below.
D’Attilio teaches that "If a valid shift schedule cannot be found for a particular agent, that agent is excluded. (D’Attilio para. [0089]). This criterion of validity of shift schedules and agent exclusion does not teach using probability of achievability and "modification to at least one of the performance metric and the workload" of claim 1 element. D’Attilio para. [0089] does not teach or suggest probability, or any metric of achievability. Remarks p. 13-14.
Respectfully the Office disagrees with Applicant’s position. D’Attilio teaches “ the pool of feasible shifts can range from just a few possible combinations and permutations to billions of possible combinations and permutations.” (D’Attillio [83]). He also teaches, “ the system utilizes an approach to shift scheduling via column generation that starts by finding feasible columns for each agent, and columns (e.g., three) are added as candidates to the relaxed master problem. In particular, the illustrative system solves the master problem and finds its dual values, which are subsequently used in the sub-problems further identify new columns that can benefit the coverage of requirements. Newly identified columns are added back to the master problem and solved. In some embodiments, this cycle may be repeated until one of the predefined termination criteria is met as described below. Then, in such embodiments, a final integer master problem may be solved to give every agent exactly one of the found shift schedules. Finally, the system may perform one or more post-processing steps to improve the quality and usability of the schedules. Table 1 presented below lists various column generation models that may be used in conjunction with the column generation algorithm described herein.” (D’Attillio [88]) Where the feasibility of the scheduling is equivalent of the claimed probability.
For these reasons the rejection of the previous Office is maintained as updated below.
D’Attilio para. [0077] merely indicates the " inputs for determining the staffing requirements and does not teach the use of AI, or generating a prompt. D’Attilio para. [0079] mentions that " the system utilizes custom mathematical modeling via a data-driven, machine learning based approach. but does not teach the creation or use of a prompt. Generally teaching machine learning cannot teach or suggest the generation or use of a prompt, two specific AI functions. D’Attilio para. [0107] teaches how "the master problem formulation may minimize (or reduce) shift cost and understaffing", but similarly does not mention AI or prompt creation. These paragraphs describe the inputs, modeling, and problem formulation of the system. They do not teach "creating a prompt for a generative artificial intelligence (AI) system" of independent claim 1 element. Remarks p.14.
Respectfully the Office disagrees with Applicant’s position. D’Attilio teaches, “knowledge system 1238 may include an artificially intelligent computer system capable of answering questions posed in natural language”. (D’Attillio [130]) D’Attillio further teaches “the processing logic of the chat server 1240 may be rules driven so to leverage an intelligent workload distribution among available chat resources. The chat server 1240 further may implement, manage, and facilitate user interfaces (UIs) associated with the chat feature, including those UIs generated at either the customer device 1205 or the agent device” (D’Attillio [131]). The maintains that one of ordinary skill in the art at the time the invention was filed that the teachings of D’Attillio would have rendered the claimed element anticipated.
For the reasons given above the rejection of the previous Office action are maintained.
While D’Attilio teaches returning " resulting schedules for display" (D' Attilio para. [0101]) and " a graphical user interface control " which shows information to a user (D’Attilio para. [0132]). These are not attributes of "the message for a user". The "message for a user" is defined in claim 1 as "stat[ing] that the first plan should be replaced with the modified plan and states the modification to the at least one of the performance metric and the workload of the modified plan". The claimed "message for a user" is not taught or suggested by D’Attilio's "resulting schedules for display" and "graphical user interface control". Remarks p. 14
Respectfully the Office disagrees with Applicant’s position. D’Attilio teaches, “knowledge system 1238 may include an artificially intelligent computer system capable of answering questions posed in natural language”. (D’Attillio [130]) The content of the message is non-functional language thus it is afforded little patentable weight, since it only contains information. See MPEP 2111.05.
For the reasons given above the rejection of the previous Office action are maintained.
Claim 20 D’Attilio teaches a forecasting method and a process " to ensure that the method selected is one that yields a workload forecast that holds well both on the short-term horizon and the long-term horizon." (D’Attilio para. [0071]). This method does not teach a score, or feasibility: rather D’Attilio para. [0071] teaches a forecast that "holds well" or is accurate, without this being tied to a score or feasibility. Remarks p. 15.
Respectfully the Office disagrees with Applicant’s position. D’Attillio teaches a simplified method 600 of performing four-fold “rolling horizon” cross-validation of data is shown. As described above, the best method may be selected using cross-validation with multiple folds (e.g., k-fold) for different validation time horizons. The criteria to be used may be based on a custom scoring that is a combination of accuracy metrics in the short term at 1- to 4-step-ahead forecasts (e.g., 1-4 weeks ahead), as well as long term over-the-horizon accuracy (e.g., 6-mo or 1-year ahead), depending on the availability of data. (D’Attillio [71]). D’Attillio further teaches [83] “working rules associated with the agents (e.g., work hours, regulatory requirements, etc.), capabilities of the respective agents (e.g., specializations in various areas, etc.), and/or other relevant criteria useful for deriving shifts from the staffing requirements and available agents. In other words, shift scheduling balances the problem of selecting what shifts are to be worked by each employee to meet workload requirements and hit certain KPI goals with adhering to various work plan constraints and state/national labor regulations (e.g., such as maximum shift duration, earliest shift starting time, latest finishing time, etc.). Depending on the constraints, agent preferences, and availability, the pool of feasible shifts can range from just a few possible combinations and permutations to billions of possible combinations and permutations.”
For the reasons given above the rejection of the previous Office action are maintained.
Claim 20 While D’Attilio teaches various criteria to achieve a solution that "holds well both on the short-term horizon and the long-term horizon" (D’Attilio para. [0071]) as well as various criteria " in order to find the optimal solution " (D’Attilio para. [0095]), these paragraphs do not teach " modifying contact center work plan " where " the feasibility score is below threshold " No threshold is taught in these sections of D’Attilio. Remarks p. 15
Respectfully the Office disagrees with Applicant’s position. D’Attilio teaches at [95] “in order to find the optimal solution, the system generally we would have to execute the sub-problems for all agent groupings in each iteration. In order to speed up the algorithm, the system may discard agents that did not add columns, and after all remaining are discarded, the system may try once more to see if any other improving shifts have been missed. In some embodiments, suboptimal criteria may include determining that a maximum number of iterations of the “column generation loop” of FIG. 9 has been executed (e.g., 15 iterations), determining that the lower bound is greater than the objective value of the relaxed master problem, MP-LP (i.e., z.sub.i.sup.LB≥z.sub.i), and/or determining that z.sub.i did not improve at least a threshold percentage (e.g., 0.1%) over the last predefined number (e.g., three) of iterations (e.g., z.sub.i-2=0.99 z.sub.i).”
For the reasons given above the rejection of the previous Office action are maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. mental processes) without practical application or significantly more when the elements are considered individually and as an ordered combination.
Step 1: Is the claimed invention to a process, machine, manufacture or composition of matter?
Yes, the claims fall within at least one of the four categories of patent eligible subject. Claims 1-9 and 20 are to a method (process) and claims 10-19 are to a system (machine).
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Yes, the claims are found to recite an abstract idea. Specifically, the abstract idea of mental processes and certain methods of organizing human activity. Where mental processes relates to concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Claim 1 (as a representative claim) recites the following, where the limitations found to contain elements of the abstract idea are in bold italics:
1. A method for generating changes to a first plan, the first plan comprising a workload and a performance metric, the method comprising:
estimating whether the first plan is achievable, comprising:
comparing the first plan to at least one instance of workplace data, the workplace data indicative of instances of workload and performance metric, the instances being previous to the time of the first plan, and
wherein the first plan is deemed to be achievable if, for at least one instance of the workplace data, the performance metric of the first plan is less than or equal to a performance metric of the past instance;
if the first plan is not deemed to be achievable, generating a modified plan with a higher probability of achievability than a probability of achievability of the first plan, the modified plan comprising a modification to at least one of the performance metric and the workload;
creating a prompt for a generative artificial intelligence (AI) system, the prompt configured to ask for a message for a user, wherein the message states that the first plan should be replaced with the modified plan and states the modification to the at least one of the performance metric and the workload of the modified plan; and
receiving from the generative AI system, the message for a user; and
automatically routing a data stream representing an interaction based on the modified plan.
The claims are directed toward determining if a generic plan is achievable based on known workloads and metrics and if the plan is determined to be unachievable then the plan is modified base on known workloads and metrics. The Office finds that these steps can be performed in the human mind given the known data of workloads and metrics using the mind’s ability to make observation about the known data, evaluate the known data, and make a judgment, e.g. whether or not the plan is achievable and if needed how to modify the plan.
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claimed invention does not recite additional elements that integrate the abstract idea into a practical application. Where a practical application is described as integrating the abstract idea by applying it, relying on it, or using the abstract idea in a manner that imposes a meaningful limit on it such that the claim is more than a drafting effort designed to monopolize it, see October 2019: Subject Matter Eligibility at p. 11.
The identified judicial exception is not integrated into a practical application. In particular, the claims recites the additional limitations see non-bold-italicized elements above. The additional elements of creating (outputting) and receiving and automatically routing (data gathering) elements are determined to be insignificant extra solution activity.
Where 2106.05(g) MPEP states, “term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.”
The Office finds that merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea; adding insignificant extra solution activity to the judicial exception; or only generally linking the use of the abstract idea to a particular technological environment or field is not sufficient to integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
No, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and as part of the ordered combination. Any expressly claimed or implied computing components are viewed as general-purpose and generic in nature, see Specification at [112] and [123].
Where 2106.05(d)(I)(2) of the MPEP states, “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").”
These limitations do NOT offer an improvement to another technology or technical field; improvements to the functioning of the computer itself; apply the judicial exception with, or by use of, a particular machine; effect a transformation or reduction of a particular article to a different state or thing; add a specific limitation other than what is well-understood, routine and conventional in the field, or add unconventional steps that confine the claim to a particular useful application; or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, these additional limitations when considered individually or in combination do not provide an inventive concept that can transform the abstract idea into patent eligible subject matter.
The other independent claims recite similar limitations and are rejected for the same reasoning given above.
The dependent claims do not further limit the claimed invention in such a way as to direct the claimed invention to statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-15, and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by D’Attilio et al (US 2022/0027837 A1).
Claims 1 and 10
D’Attilio teaches a method for generating changes to a first plan, the first plan comprising a workload and a performance metric (D’Attilio [5] “methods for performing contact center agent scheduling”), the method comprising:
estimating whether the first plan is achievable (D’Attilio [58] “workload forecasting model may be defined as the best method to apply given time series data and a set of optimal parameters that yield optimal key performance indicator (KPI) metrics”), comprising:
comparing the first plan to at least one instance of workplace data, the workplace data indicative of instances of workload and performance metric, the instances being previous to the time of the first plan (D’Attilio [60] “system predicts the workload or demand that will be introduced into the contact center system (e.g., the contact center system 1200) in future planning periods. It should be appreciated that a basic forecast may be specified as a sequence of metrics” and [84] “obtain “good” shifts that cover the workforce requirements”), and
wherein the first plan is deemed to be achievable if, for at least one instance of the workplace data, the performance metric of the first plan is less than or equal to a performance metric of the past instance (D’Attilio [87] “the system can use column generation to iteratively identify good candidate shift schedules for covering requirements (e.g., without having to enumerate all possibilities)” and [106] “the master problem lower bound may be used to terminate the algorithm. In particular, the lower bound is valid for every iteration i in the column generation algorithm, and the system uses this lower bound to terminate the algorithm if current lower bound z.sub.i.sup.LB is greater than z.sub.i.”);
if the first plan is not deemed to be achievable, generating a modified plan with a higher probability of achievability than a probability of achievability of the first plan, the modified plan comprising a modification to at least one of the performance metric and the workload (D’Attilio [83] “Depending on the constraints, agent preferences, and availability, the pool of feasible shifts can range from just a few possible combinations and permutations to billions of possible combinations and permutations”, where feasible is the equivalent of the claimed probability [89] “If a valid shift schedule cannot be found for a particular agent, that agent is excluded. Valid shift schedules are passed to the relaxed master problem of the column generation”);
creating a prompt for a generative artificial intelligence (AI) system, the prompt configured to ask for a message for a user, wherein the message states that the first plan should be replaced with the modified plan and states the modification to the at least one of the performance metric and the workload of the modified plan (D’Attilio [77] “ inputs for determining the staffing requirements may include the workload forecast, the routing configuration of the contact center system, and/or other inputs.”, [79 “ the system utilizes custom mathematical modeling via a data-driven, machine learning based approach”, [107] “the master problem formulation may minimize (or reduce) shift cost and understaffing, given parameters for labor cost and a penalty for missing requirement. In other embodiments, an objective may be substituted with the appropriate changes to the lower bound. ” and [130] “knowledge system 1238 may include an artificially intelligent computer system capable of answering questions posed in natural language”.); and
receiving from the generative AI system, the message for a user (D’Attilio [101] “the system may return the resulting schedules for display” and [132] “a widget may include a graphical user interface control that can be overlaid on a webpage displayed to a customer via the Internet. The widget may show information, such as in a window or text box, or include buttons or other controls that allow the customer to access certain functionalities, such as sharing or opening a file or initiating a communication”); and
automatically routing a data stream representing an interaction based on the modified plan (D’Attillio [45] “scheduling in which the headcount requirement is fulfilled through placement of staff throughout the planning horizon according to shift and schedule constraints, such that the final output is a schedule or roster that optimizes (or sub-optimizes) the coverage of workload with staffed agents” and [77] “inputs for determining the staffing requirements may include the workload forecast, the routing configuration of the contact center system, and/or other inputs”).
.
With respect to independent system claim that recites substantially similar limitations to those rejected above therefore this claim is rejected for the same reasons given above. D’Attilio also teaches the additionally claimed limitations of:
Claim 10. A system for generating changes to a first plan 10 (D’Attilio abstract “system for performing contact center agent scheduling”), the first plan comprising a workload and a performance metric (D’Attilio [53] “workload forecasting model may be defined as the best method to apply given time series data and a set of optimal parameters that yield optimal key performance indicator (KPI) metrics used to validate the accuracy of the forecast”), the system comprising: a memory; a processor, the processor configured to (D’Attilio [abstract] “system for performing contact center agent scheduling according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor”).
Claims 2 and 11
D’Attilio teaches all the elements of the method of claim 1, further comprising: creating, based on the modified plan, a staffing schedule for delivering the modified plan (D’Attilio [74] and [107]).
Claims 3 and 12
D’Attilio teaches all the elements of the method of claim 2, wherein creating a staffing schedule is carried out using a generative AI (D’Attilio [138-139], see machine learning).
Claims 4 and 13
D’Attilio teaches all the elements of the method of claim 1, wherein the method is carried out for each of a number of interaction types, the interaction types comprising at least one of: voice-based interactions, text-based interactions, video-based interactions, and face-to-face interactions (D’Attilio [79], [115]. And [119]).
Claims 5 and 14
D’Attilio teaches all the elements of the method of claim 4, wherein, when comparing the first plan to at least one instance of workplace data, the workplace data and the first plan both relate to a same interaction type (D’Attilio [4]).
Claims 6 and 15
D’Attilio teaches all the elements of the method of claim 1, wherein the modified plan comprises a decrease to the performance metric (D’Attilio [108]).
Claims 8 and 17
D’Attilio teaches all the elements of the method of claim 1, further comprising: outputting the modified plan for displaying to a user (D’Attilio [101] and [132]).
Claims 9 and 18
D’Attilio teaches all the elements of the method of claim 1, wherein the first plan and the workplace data relate to a contact center or call center (D’Attilio [1113]).
Claim 19
D’Attilio teaches all the elements of the system of claim 10, wherein the modified plan is to be transferred to an automatic call distributor of a contact center, and wherein the automatic call distributor is to route contacts of the contact center to agents of the contact center in accordance with the modified plan (D’Attilio [116] and [122-125]).
Claim 20
D’Attilio teaches all the elements of the method for assessing the feasibility of a proposed contact center work plan comprising a proposed workload and a proposed performance metric (D’Attilio [58]), the method comprising:
generating, based on the proposed contact center work plan and data indicative of previous feasible contact center work plans, a feasibility score for the proposed contact center work plan (D’Attilio [71]); and
where the feasibility score is below a threshold, generating, using a large language model, a modified contact center work plan for a user with a feasibility score above the threshold, the modified contact center work plan comprising a modification to one or more aspects of the contact center work plan (D’Attilio [71] and [95]); and
automatically routing a data stream representing an interaction based on the modified contact center work plan (D’Attillio [45] “scheduling in which the headcount requirement is fulfilled through placement of staff throughout the planning horizon according to shift and schedule constraints, such that the final output is a schedule or roster that optimizes (or sub-optimizes) the coverage of workload with staffed agents” and [77] “inputs for determining the staffing requirements may include the workload forecast, the routing configuration of the contact center system, and/or other inputs”).
.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over D’Attilio et al (US 2022/0027837 A1) as applied above, and further in view of Ezell (US 2023/0403357 A1).
Claims 7 and 16
D’Attilio teaches all the elements of the method of claim 1, however D’Attilio does not expressly teach wherein the generative AI system is a large language model (LLM).
Ezell in the analogous art of AI messaging teaches the claimed limitation of wherein the generative AI system is a large language model (LLM) (Ezell [172]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of D’Attilio the wherein the generative AI system is a large language model (LLM) as taught by Ezell since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Esmalifalak et al (US 2025/0272663 A1) teaches work order management system 202 can also incorporate a generative AI chat interface that allows a user to interact with the system 202 via natural language chat exchanges.
Zhao et al (US 2026/0093545 A1) teaches the system receives a query requesting a plan for a given task, and uses a generative machine learning model to process the query with one or more prompts that describe example tasks and their corresponding plans to generate a plan that specifies a sequence of actions for achieving the task.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/FOLASHADE ANDERSON/Primary Examiner, Art Unit 3623
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