DETAILED ACTION
1. 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 the Application
2. Examiner Note: Please note that due to the preliminary amendments filed on 12/10/2024, Applicant has canceled Claims 13, 17-18 and 23-42 and has currently amended Claims 5-6, 8, 10-12, 14-16, 19, 21 and 43. Therefore, Claims 1-12, 14-16, 19-22 and 43 have been examined in this application. This communication is the first action on the merits.
Priority
3. The Examiner has noted the Applicants claiming Priority from Provisional Application 63/528,172 filed on 07/21/2023 and 371 of PCT/CA2024/050961 filed on 07/19/2024. Therefore, Examiner notes the earliest effective filing date of this application examined on the record is 07/21/2023.
IDS Statements
4. The 1 Information Disclosure Statement (IDS) filed on 12/10/2024 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and are considered by the Examiner.
Drawing Objections
5. The drawings are objected to because of the following minor informalities:
(A). The drawing flowchart in Fig. 2 recites “[[Bugeting]] System” 110 component in which the word “budgeting” is spelling incorrectly. Examiner suggests to Applicant to amend the “Bugeting System” 110 component to reflect “Budgeting System” 110.
(B). The drawing in Fig. 35 recites “Budget [[Too]] 3516” component in which the word “tool” is spelled incorrectly. Examiner suggests to Applicant to amend the “Budget Too 3516” component to reflect “Budget Tool 3516”.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Appropriate corrections are required.
Claim Objections
6. Claim 11 is objected to because of the following informalities:
(A). Claim 11 recites the following limitation: “The workforce optimization system of claim 1, wherein the agent suite tool is further configured to determine recommended coaching and/or training for employees based on the performance data.” Examiner notes that Dependent Claim 11 previously refers back to Independent Claim 1 which recites in the 2nd limitation of Independent Claim 1 as “employee performance data” and Dependent Claim 11 recites “the performance data” which raises a minor claim informality and is not consistent here. For the purposes of examination, Examiner suggests to Applicant to amend Dependent Claim 11 to recite the following: “The workforce optimization system of claim 1, wherein the agent suite tool is further configured to determine recommended coaching and/or training [[ based on the employee performance data.” Appropriate corrections are required.
35 U.S.C. § 112 (f) Claim Interpretation
7. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
8. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
These claim limitation(s) are shown below and reflected for Claims 1-2, 8, 11-12 and 19-21 which appear to invoke 35 U.S.C. § 112 (f).
Such claim limitation(s) are shown below in Independent Claim 1:
(A1): The 1st claim limitation of Independent Claim 1 recites the following: “a hierarchy tool configured to collect and store employment information about employees of a workforce” that uses the generic placeholder “a hierarchy tool” functionally modified by verb “configured” via the linking term “to”.
(A2): The 2nd claim limitation of Independent Claim 1 recites the following: “an agent suite tool configured to collect and store employee performance data and scheduling information for the employees” that uses the generic placeholder “an agent suite tool” functionally modified by verb “configured” via the linking term “to”.
(A.3): The 3rd claim limitation of Independent Claim 1 recites the following: “a workforce planning tool configured to determine forecasted employee requirements” that uses the generic placeholder “a workforce planning tool” functionally modified by verb “configured” via the linking term “to”.
(A.4): The 4th claim limitation of Independent Claim 1 recites the following: “a budget tool configured to determine an expected budget based on the employment information and the forecasted employee requirements” that uses the generic placeholder “a budget tool” functionally modified by verb “configured” via the linking term “to”.
(A.5): The 5th claim limitation of Independent Claim 1 recites the following: “a customer experience monitoring tool configured to determine a service level provided by the employees to customers” that uses the generic placeholder “a customer experience monitoring tool” functionally modified by verb “configured” via the linking term “to”.
(A.6): The 6th claim limitation of Independent Claim 1 recites the following: “a virtual manager configured to analyze the employment information, the employee performance data, the scheduling information, the forecasted employee requirements, the expected budget, and the service level, and determine one or more recommended actions for optimizing the workforce while maintaining the service level at or above a minimum service level” that uses the generic placeholder “a virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 2:
(B). Dependent Claim 2 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to determine a gap between a current expected demand for employee requirements and a supply of employee availability, and to determine the one or more recommended actions to minimize the gap” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 8:
(C). Dependent Claim 8 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to determine a variance between an expected actual budget for a time period and a planned budget for the time period, and to determine the one or more recommended actions to minimize the variance” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 11:
(D). Dependent Claim 11 recites the following limitation: “The workforce optimization system of claim 1, wherein the agent suite tool is further configured to determine recommended coaching and/or training for employees based on the performance data” that uses the generic placeholder “agent suite tool” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 12:
(E). Dependent Claim 12 recites the following limitation: “The workforce optimization system of claim 1, wherein the agent suite tool is further configured to set targets and incentives to the employees” that uses the generic placeholder “agent suite tool” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 19:
(F). Dependent Claim 19 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to generate and output a user interface for display to a user, the user interface displaying information of one or more of the employment information, the employee performance data, the scheduling information, the forecasted employee requirements, the expected budget, the service level, and the one or more recommended actions” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 20:
(G). Dependent Claim 20 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to implement the one or more recommended actions in response to user input” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 21:
(H). Dependent Claim 21 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to automatically implement the one or more recommended actions” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
9. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
10. Claims 1-12, 14-16 and 19-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Such claim limitation(s) are shown below in Independent Claim 1:
(A1): The 1st claim limitation of Independent Claim 1 recites the following: “a hierarchy tool configured to collect and store employment information about employees of a workforce” that uses the generic placeholder “a hierarchy tool” functionally modified by verb “configured” via the linking term “to”.
(A2): The 2nd claim limitation of Independent Claim 1 recites the following: “an agent suite tool configured to collect and store employee performance data and scheduling information for the employees” that uses the generic placeholder “an agent suite tool” functionally modified by verb “configured” via the linking term “to”.
(A.3): The 3rd claim limitation of Independent Claim 1 recites the following: “a workforce planning tool configured to determine forecasted employee requirements” that uses the generic placeholder “a workforce planning tool” functionally modified by verb “configured” via the linking term “to”.
(A.4): The 4th claim limitation of Independent Claim 1 recites the following: “a budget tool configured to determine an expected budget based on the employment information and the forecasted employee requirements” that uses the generic placeholder “a budget tool” functionally modified by verb “configured” via the linking term “to”.
(A.5): The 5th claim limitation of Independent Claim 1 recites the following: “a customer experience monitoring tool configured to determine a service level provided by the employees to customers” that uses the generic placeholder “a customer experience monitoring tool” functionally modified by verb “configured” via the linking term “to”.
(A.6): The 6th claim limitation of Independent Claim 1 recites the following: “a virtual manager configured to analyze the employment information, the employee performance data, the scheduling information, the forecasted employee requirements, the expected budget, and the service level, and determine one or more recommended actions for optimizing the workforce while maintaining the service level at or above a minimum service level” that uses the generic placeholder “a virtual manager” functionally modified by verb “configured” via the linking term “to”.
Furthermore, Dependent Claims 2-12, 14-16 and 19-21 depend from Independent Claim 1 and therefore inherit the 35 U.S.C. § 112 (a) deficiencies of Independent Claim 1 discussed above.
Such claim limitation(s) are shown below in Dependent Claim 2:
(B). Dependent Claim 2 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to determine a gap between a current expected demand for employee requirements and a supply of employee availability, and to determine the one or more recommended actions to minimize the gap” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 8:
(C). Dependent Claim 8 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to determine a variance between an expected actual budget for a time period and a planned budget for the time period, and to determine the one or more recommended actions to minimize the variance” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 11:
(D). Dependent Claim 11 recites the following limitation: “The workforce optimization system of claim 1, wherein the agent suite tool is further configured to determine recommended coaching and/or training for employees based on the performance data” that uses the generic placeholder “agent suite tool” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 12:
(E). Dependent Claim 12 recites the following limitation: “The workforce optimization system of claim 1, wherein the agent suite tool is further configured to set targets and incentives to the employees” that uses the generic placeholder “agent suite tool” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 19:
(F). Dependent Claim 19 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to generate and output a user interface for display to a user, the user interface displaying information of one or more of the employment information, the employee performance data, the scheduling information, the forecasted employee requirements, the expected budget, the service level, and the one or more recommended actions” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 20:
(G). Dependent Claim 20 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to implement the one or more recommended actions in response to user input” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 21:
(H). Dependent Claim 21 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to automatically implement the one or more recommended actions” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
The Original Specification doesn’t clearly, deliberately and sufficiently provide, for each of the generic placeholders: (e.g., “a hierarchy tool” & “agent suite tool” & “workforce planning tool” & “budget tool” & “customer experience monitoring tool” & “virtual manager”) for:
➔ each of the adequate respective structures to perform each respective functions of the claims above and to clearly link each of their respective functions.
Specifically, the Original Specification does not demonstrate that Applicant has made an
invention that achieves each and all of the respective claimed functions by each of the respective
structure because the invention is not described with sufficient detail such that one of ordinary
skills in the art can reasonably conclude that inventor had possession of claimed invention.
Clarifications and/or corrections are required.
11. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
12. Claims 1-12, 14-16 and 19-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Such claim limitation(s) are shown below in Independent Claim 1:
(A1): The 1st claim limitation of Independent Claim 1 recites the following: “a hierarchy tool configured to collect and store employment information about employees of a workforce” that uses the generic placeholder “a hierarchy tool” functionally modified by verb “configured” via the linking term “to”.
(A2): The 2nd claim limitation of Independent Claim 1 recites the following: “an agent suite tool configured to collect and store employee performance data and scheduling information for the employees” that uses the generic placeholder “an agent suite tool” functionally modified by verb “configured” via the linking term “to”.
(A.3): The 3rd claim limitation of Independent Claim 1 recites the following: “a workforce planning tool configured to determine forecasted employee requirements” that uses the generic placeholder “a workforce planning tool” functionally modified by verb “configured” via the linking term “to”.
(A.4): The 4th claim limitation of Independent Claim 1 recites the following: “a budget tool configured to determine an expected budget based on the employment information and the forecasted employee requirements” that uses the generic placeholder “a budget tool” functionally modified by verb “configured” via the linking term “to”.
(A.5): The 5th claim limitation of Independent Claim 1 recites the following: “a customer experience monitoring tool configured to determine a service level provided by the employees to customers” that uses the generic placeholder “a customer experience monitoring tool” functionally modified by verb “configured” via the linking term “to”.
(A.6): The 6th claim limitation of Independent Claim 1 recites the following: “a virtual manager configured to analyze the employment information, the employee performance data, the scheduling information, the forecasted employee requirements, the expected budget, and the service level, and determine one or more recommended actions for optimizing the workforce while maintaining the service level at or above a minimum service level” that uses the generic placeholder “a virtual manager” functionally modified by verb “configured” via the linking term “to”.
Furthermore, Dependent Claims 2-12, 14-16 and 19-21 depend from Independent Claim 1 and therefore inherit the 35 U.S.C. § 112 (b) deficiencies of Independent Claim 1 discussed above.
Such claim limitation(s) are shown below in Dependent Claim 2:
(B). Dependent Claim 2 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to determine a gap between a current expected demand for employee requirements and a supply of employee availability, and to determine the one or more recommended actions to minimize the gap” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 8:
(C). Dependent Claim 8 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to determine a variance between an expected actual budget for a time period and a planned budget for the time period, and to determine the one or more recommended actions to minimize the variance” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 11:
(D). Dependent Claim 11 recites the following limitation: “The workforce optimization system of claim 1, wherein the agent suite tool is further configured to determine recommended coaching and/or training for employees based on the performance data” that uses the generic placeholder “agent suite tool” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 12:
(E). Dependent Claim 12 recites the following limitation: “The workforce optimization system of claim 1, wherein the agent suite tool is further configured to set targets and incentives to the employees” that uses the generic placeholder “agent suite tool” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 19:
(F). Dependent Claim 19 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to generate and output a user interface for display to a user, the user interface displaying information of one or more of the employment information, the employee performance data, the scheduling information, the forecasted employee requirements, the expected budget, the service level, and the one or more recommended actions” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 20:
(G). Dependent Claim 20 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to implement the one or more recommended actions in response to user input” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
Such claim limitation(s) are shown below in Dependent Claim 21:
(H). Dependent Claim 21 recites the following limitation: “The workforce optimization system of claim 1, wherein the virtual manager is configured to automatically implement the one or more recommended actions” that uses the generic placeholder “virtual manager” functionally modified by verb “configured” via the linking term “to”.
The Original Specification doesn’t clearly, deliberately and sufficiently provide, for each of the generic placeholders: (e.g., “a hierarchy tool” & “agent suite tool” & “workforce planning tool” & “budget tool” & “customer experience monitoring tool” & “virtual manager”) for:
➔ each of the adequate respective structure to perform each respective functions of the claims above and to clearly link each of their respective functions.
Specifically, the Original Specification does not demonstrate that Applicant has made an invention that achieves each and all of the respective claimed functions by each of the respective structure, thus rendering their underlining limitations vague and indefinite.
Appropriate corrections are required.
Claim Rejections - 35 USC § 101
13. 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.
14. Claims 1-12, 14-16, 19-22 and 43 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-12, 14-16, 19-22 and 43 are each focused to a statutory category namely “a system” or an “apparatus” (Claims 1-12, 14-16 and 19-21), a “method” or a “process” (Claim 22) and “a non-transitory computer-readable medium” or an “article of manufacture” (Claim 43).
We proceed onto analyzing the claims with respect to Step 2A Prong 1 shown below.
Step 2A Prong One: Independent Claims 1, 22 and 43 recite limitations that set forth the abstract idea(s), namely (see in bold except via strikethrough):
“” (see Independent Claim 43);
“ collect and store employment information about employees of a workforce” (see Independent Claim 1);
“ collect and store employee performance data and scheduling information for the employees” (see Independent Claim 1);
“ determine forecasted employee requirements” (see Independent Claim 1);
“ determine an expected budget based on the employment information and the forecasted employee requirements” (see Independent Claim 1);
“ determine a service level provided by the employees to customers” (see Independent Claim 1);
“ analyze the employment information, the employee performance data, the scheduling information, the forecasted employee requirements, the expected budget, and the service level, and determine one or more recommended actions for optimizing the workforce while maintaining the service level at or above a minimum service level” (see Independent Claim 1);
“receiving workforce management information, the workforce management information comprising employment information about employees of a workforce, performance data of the employees, scheduling information for the employees, forecasted employee requirements, an expected budget, and a service level provided by the employees to customers” (see Independent Claims 22 and 43);
“determining one or more recommended actions for optimizing the workforce while maintaining the service level at or above a minimum service level” (see Independent Claims 22 and 43).
Here, the claim limitations for Independent Claim 1 are directed to an abstract concept of collecting, analyzing, and optimizing business and human resources operations in a call/contact center for workforce management purposes. For Independent Claim 1, the first step of “Hierarchy & Agent Suite Tools” pertains to Collecting/storing employment info (hierarchy) and collecting/storing performance data and schedules (agent suite), which is a Certain Methods of Organizing Human Activity. These steps represent the automation of fundamental organizational practices: keeping records of employees, assigning them to roles (hierarchies), and monitoring performance. Collecting, storing, and organizing data regarding human behavior and business tasks are classic "organizing human activity" exceptions. The second step of “Workforce Planning & Budget Tools” pertains to Determining forecasted employee requirements and calculating an expected budget based on this data, which is Certain Methods of Organizing Human Activities or Mathematical Concepts. These steps execute mathematical modeling. Forecasting staffing requirements requires applying mathematical formulas or algorithms to historical data. Budgeting is a foundational economic principle. Because these steps are primarily calculation-based (i.e., using formulas to balance workforce costs), they fit the mathematical and economic judicial exceptions. The third step of “Customer Experience Monitoring Tool” pertains to determining a service level provided by employees to customers, which is Mental Processes or Certain Methods of Organizing Human Activity. This step evaluates the quality of human interaction or business output. Quantifying customer satisfaction, service delivery, or performance metrics by observing interactions requires evaluating and grading data, which maps to the "mental processes" exception category. Lastly, the step of “Virtual Manager (Analysis and Action Recommendation)” pertains to Analyzing all data points (employment info, performance, scheduling, budgets, service levels) to determine recommended actions for workforce optimization, which is Mental Processes or Mathematical Concepts. This is the core data analysis step. Analyzing disparate data sets to output a recommended "optimized" action is equivalent to automating a mental process—what a human manager would do when assessing a business. It relies on rule-based or algorithmic optimization logic, which falls under mathematical concepts and mental tasks.
Here, the claim limitations for Independent Claims 22 and 43 are directed to a computerized method for allocating resources based on parameters like budget and forecasted requirements in a call/contact center for workforce management purposes.
For Independent Claims 22 and 43, the first step of “receiving workforce management information….” encompasses gathering various employment records, performance data, schedules, forecasted requirements, budgets, and customer service metrics.” This step falls into the abstract idea grouping of "Mental Processes" (concepts performed entirely in the human mind) or "Collecting Information" (gathering data without requiring a transformative, specific technical implementation). The Abstract Idea is directed to "managing human activity" or "gathering and organizing information/data." Humans have historically received and organized these exact same elements (employee performance, budgets, and schedules) using paper, whiteboards, and personal communication. Because it essentially amounts to the automated gathering of business information, it does not integrate the data into a practical application in and of itself, but rather sets the stage for analysis. The second step of “determining recommended actions for optimizing the workforce…” analyzes the data to calculate how to optimize the workforce while maintaining a minimum customer service level. This step squarely falls into the abstract idea groupings of "Mathematical Concepts" (using algorithms or calculations) and "Methods of Organizing Human Activity" (rules for managing a business, human resources, or scheduling). The Abstract Idea is directed to "optimizing a schedule or allocating resources." This is fundamentally a mathematical optimization or business strategy concept. Determining the most cost-effective or productive way to arrange workers to meet a predefined goal (customer service level) is a long-standing economic and administrative practice. Doing so via computational rules, heuristics, or formulas—rather than via a specific, physically transformative technological improvement—constitutes an abstract idea. Overall, the two independent claims are directed to the judicial exception of an Abstract Idea. The courts categorize these concepts broadly as "Mental Processes" (the logic/analysis) and "Methods of Organizing Human Activity" (managing an enterprise, HR, and business operations).
Therefore, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) fundamental economic principles or practices.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under the broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid, in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude these claims from reciting an abstract idea. See MPEP § 2106.04(a) III C.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under the broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mathematical Concepts” which pertains to (5) mathematical calculations.
That is, other than reciting the additional elements of (e.g., “hierarchy tool” & “agent suite tool” & “workforce planning tool” & “budget tool” & “customer experience monitoring tool” & “virtual manager” & “processor” & “computer executable instructions”, etc…), nothing in the claim elements precludes the steps from being performed as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) fundamental economic principles or practices and additionally or alternatively as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid and additionally or alternatively as “Mathematical Concepts” which pertains to (5) mathematical calculations.
Therefore, at step 2a prong 1, Yes, Claims 1-12, 14-16, 19-22 and 43 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claim 1 recites additional elements directed to: (e.g., “hierarchy tool” & “agent suite tool” & “workforce planning tool” & “budget tool” & “customer experience monitoring tool” & “virtual manager”). Independent Claim 22 does not recite any additional elements. Independent Claim 43 recites additional elements directed to: (e.g., “processor” & “computer executable instructions”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
Furthermore, certain/particular limitations in Independent Claims 1, 22 and 43 recite “mere data gathering” (e.g., “receiving workforce management information, the workforce management information comprising employment information about employees of a workforce, performance data of the employees, scheduling information for the employees, forecasted employee requirements, an expected budget, and a service level provided by the employees to customers” (see Independent Claims 22 and 43) & “a hierarchy tool configured to collect and store employment information about employees of a workforce” (see Independent Claim 1) & “an agent suite tool configured to collect and store employee performance data and scheduling information for the employees” (see Independent Claim 1)), which when evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-12, 14-16, 19-22 and 43 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claim 1 recites additional elements directed to: (e.g., “hierarchy tool” & “agent suite tool” & “workforce planning tool” & “budget tool” & “customer experience monitoring tool” & “virtual manager”). Independent Claim 22 does not recite any additional elements. Independent Claim 43 recites additional elements directed to: (e.g., “processor” & “computer executable instructions”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (h) and See MPEP § 2106.05 (f). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Specification ¶ [258-259]: “The non-transitory computer-readable memory 3524 comprises computer-executable instructions stored thereon at runtime which, when executed by the CPU 3522, configure the server to perform a workforce optimization method. The non-volatile storage 3526 has stored on its computer-executable instructions that are loaded into the non-transitory computer-readable memory at runtime. The input/output interface 3528 allows the server to communicate with one or more external devices (e.g. via a network). The GPU 3530 may be used to control a display and may also be used to implement aspects of the method (e.g. running the machine learning models).”).
Furthermore, certain/particular limitations in Independent Claims 1, 22 and 43 recite “mere data gathering” (e.g., “receiving workforce management information, the workforce management information comprising employment information about employees of a workforce, performance data of the employees, scheduling information for the employees, forecasted employee requirements, an expected budget, and a service level provided by the employees to customers” (see Independent Claims 22 and 43) & “a hierarchy tool configured to collect and store employment information about employees of a workforce” (see Independent Claim 1) & “an agent suite tool configured to collect and store employee performance data and scheduling information for the employees” (see Independent Claim 1)), which when evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)), and have been recognized as Well-Understood, Routine and Conventional (WURC), and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii - 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See also MPEP § 2106.05(d) ii - Storing and Retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.,793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115USPQ2d at 1092-93.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-12, 14-16 and 19-21 recite additional elements directed to: (e.g., “machine learning models” (see Dependent Claim 4) & “plurality of machine learning models” (see Dependent Claim 4) & “a machine learning model” (see Dependent Claim 10) & “a user interface” (see Dependent Claim 19), etc…), and when considered individually and as an ordered combination (as a whole) with the limitations recite the same abstract idea(s) as shown in Independent Claims 1, 22 and 43 along with further steps/details that could be performed as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) fundamental economic principles or practices and additionally or alternatively as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid and additionally or alternatively as “Mathematical Concepts” which pertains to (5) mathematical calculations.
Dependent Claims 2-3, 5-9, 11-12, 14-16 and 20-21 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and 2B for Independent Claims 1, 22 and 43. Dependent Claims 4 and 10: With respect to reliance on (e.g., “machine learning models” (see Dependent Claim 4) & “plurality of machine learning models” (see Dependent Claim 4) & “a machine learning model” (see Dependent Claim 10)) as additional elements shown in Dependent Claims 4 and 10 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) the claims as a whole are limited to a particular field of use or technological environment for forecasting the employee requirements for the current interval based on accuracy of previous forecasted employee requirements compared to actual employee requirements using a computer in a workforce enterprise management environment (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)). Dependent Claim 19: With respect to reliance on (e.g., “user interface” (see Dependent Claim 19)) as additional elements shown in Dependent Claim 19 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) the claims as a whole are limited to a particular field of use or technological environment for displaying information of one or more of the employment information, the employee performance data, the scheduling information, the forecasted employee requirements, the expected budget, the service level, and the one or more recommended actions using a computer in a workforce enterprise management environment (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)).
The additional element of “machine learning” or “machine learning models” in general in Dependent Claims 4 and 10 does not amount to significantly more than the judicial exceptions under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art. For example, see US PG Pub
See also US PG Pub (US 2022/0263703 A1) hereinafter McConnell, et. al. McConnell at [0260] teaches monitoring application 920 uses a machine learning model trained using a supervised or semi-supervised machine learning approach. During a training phase, the machine learning model could be trained with labeled data that includes intercepted packets from known conditions (e.g., packets intercepted during a state change). See also US PG Pub (US 2023/0297907 A1) hereinafter Kaplan, et. al. Kaplan at ¶ [0076] notes: “A service metric value expected to be achieved by the initial staffing requirement (block B.3) handling the forecasted workload may then be predicted using a machine learning service level prediction model (block B.4), for example by inputting the initial allocation assignment to a machine learning algorithm, wherein the machine learning algorithm has been previously trained on historic data of a plurality of past intervals.” Kaplan at ¶ [0113] notes: “For example, a forecasting process 802, as may be known in the art, may generate forecasted data such as an expected workload for a given interval.” Kaplan at ¶ [0091] notes: Method 500 may include receiving (502) a forecasted workload and at least one required service metric value for each of the plurality of time intervals. A forecasted workload may be forecasted by means known in the art, for example by simulation.
Therefore, the ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-12, 14-16, 19-22 and 43 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-12, 14-16, 19-22 and 43 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 103
15. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
16. 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.
17. 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.
18. Claims 1, 6-7, 10-11, 19-22 and 43 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2002/0184069 A1) hereinafter Kosiba, et. al., and in view of US PG Pub (US 2022/0263703 A1) hereinafter McConnell, et. al.
Regarding Independent Claim 1, Kosiba system of workforce optimization systems and methods teaches the following:
- a hierarchy tool configured to collect and store employment information about employees of a workforce (see at least Kosiba: ¶ [0055] & ¶ [0065] & ¶ [0126] & Fig. 9. Kosiba notes that the HRD 120 receives the forecast from the WFMS 115 and uses it to determine when to hire new employees and when to schedule training classes. See also Kosiba at ¶ [0065]: The amount of training required for new hires and the ramp-up time for new hires (e.g., four weeks to train a new hire and then new hire is 30% as effective as an experienced agent during the first week, 80% as effective during the second week, and 100% as effective from then on); (8) the weekly attrition rate for new hires prior to their becoming 100% effective (e.g. 25%); (9) the amount of employee vacation time planned; (10) the expected employee sick time; (11) the estimated schedule non-adherence (e.g., an agent at work is actually available to receive calls for only 70% of their paid hours); (12) the new hire cost. See also Kosiba at ¶ [0126]: The number of effective full time CSRs for the month must also be modified by the number of full-time new hires. Since new hires are not as productive as seasoned employees, the number of new hires cannot simply be added to the number of CSRs in the calculation above. Rather, referring to FIG. 11, the number of new hires is converted into an effective number of CSRs by multiplying the number of new hires by an effectiveness percentage for each week of the month. The resulting figure is then multiplied by the quantity one minus the corresponding new hire attrition rate for that week.)
- an agent suite tool configured to collect and store employee performance data (see at least Kosiba: ¶ [0031-0033] & ¶ [0117]. Kosiba teaches that the granular contact center performance data is then processed into daily, weekly, or monthly performance data (step 810). Assuming an hour is the time increment, the granular performance data is processed into daily, weekly, or monthly performance data by applying the hourly staff distribution, call volume distribution, customer patience distribution, handle time distribution, and phone line distribution developed in step 700 to a given daily, weekly, or monthly staffing level, call volume, customer patience, handle time, and number of phone lines, respectively. See also Kosiba at ¶ [0031]: “May include predicting process performance metrics of the processing center system and using the process performance metrics to generate employee schedules.” See also Kosiba at ¶ [0033]: Developing a computer model of the processing operation based on the performance information, and using the computer model to generate expected performance metrics of the processing operation over a predetermined range of inputs. The method also includes storing the expected performance metrics and the range of inputs in a performance database, and accessing the performance database and analyzing the expected performance metrics for producing the resource and performance plans.) and scheduling information for the employees (see at least Kosiba: ¶ [0054-0055] & ¶ [0065] & ¶ [0123-0124] & Fig. 9. Kosiba notes that The WFMS 115 sends schedules, shift swaps, schedule adherence, and hours worked data to the HRD 120. See also Kosiba noting that at ¶ [0065]: “the estimated schedule non-adherence (e.g., an agent at work is actually available to receive calls for only 70% of their paid hours).” See also Kosiba noting that at ¶ [0123-0124]: The values in the full-time staff fields 935, the part time staff fields 940, and the schedule adherence field 971 may be used to determine the effective staff value (field 972).)
- a workforce planning tool configured to determine forecasted employee requirements (see at least Kosiba: ¶ [0054]. Kosiba notes that the WFMS 115 then determines, using a variety of algorithms, the phone agent staffing levels by time of day that will result in the center's meeting an overall service standard goal for the forecasted number of contacts. Shift optimization algorithms are then used to calculate the shifts that will best cover the estimated staffing requirements.)
- a budget tool configured to determine an expected budget (see at least Kosiba: ¶ [0060] & ¶ [0149] & ¶ [0175]. Kosiba notes that the staffing plan includes the hiring plan; the number of vacation, sick, and training days; the number of agents transferred between management units; and the skills of the agents (e.g. able to speak Spanish or that can handle complex requests well). The WFMS 115 incorporates all of this information into the staffing schedule. The analysis system 205 also provides the staffing plan to the HRD 120 and its analysts along with a budget estimate corresponding to that staffing plan. See also Kosiba at ¶ [0149]: Multiple windows containing the fields in the user interface 900 may be active at one time. Each window represents a different scenario and, therefore, multiple scenarios may be analyzed simultaneously. This allows the user to immediately see the benefits of one staff planning or budget scenario against a different scenario. Since the performance outputs are determined by a simple query of the planning and analysis database, the user may quickly calculate performance figures for multiple scenarios simultaneously. See also Kosiba at ¶ [0175]: The analysis system 205 provides to the HRD 120 and its analyst the staffing plan and the corresponding budget for that staffing plan as estimated in steps 320 and 325. Lastly, the analysis system 205 provides the customer reports generated in step 715 to CIS 125 and its analysts.) based on the employment information (see at least Kosiba: ¶ [0055] & ¶ [0065] & ¶ [0126] & Fig. 9. Kosiba notes that the HRD 120 receives the forecast from the WFMS 115 and uses it to determine when to hire new employees and when to schedule training classes. See also Kosiba at ¶ [0065]: The amount of training required for new hires and the ramp-up time for new hires (e.g., four weeks to train a new hire and then new hire is 30% as effective as an experienced agent during the first week, 80% as effective during the second week, and 100% as effective from then on); (8) the weekly attrition rate for new hires prior to their becoming 100% effective (e.g. 25%); (9) the amount of employee vacation time planned; (10) the expected employee sick time; (11) the estimated schedule non-adherence (e.g., an agent at work is actually available to receive calls for only 70% of their paid hours); (12) the new hire cost. See also Kosiba at ¶ [0126]: The number of effective full time CSRs for the month must also be modified by the number of full-time new hires. Since new hires are not as productive as seasoned employees, the number of new hires cannot simply be added to the number of CSRs in the calculation above. Rather, referring to FIG. 11, the number of new hires is converted into an effective number of CSRs by multiplying the number of new hires by an effectiveness percentage for each week of the month. The resulting figure is then multiplied by the quantity one minus the corresponding new hire attrition rate for that week.) and the forecasted employee requirements (see at least Kosiba: ¶ [0054]. Kosiba notes that the WFMS 115 then determines, using a variety of algorithms, the phone agent staffing levels by time of day that will result in the center's meeting an overall service standard goal for the forecasted number of contacts. Shift optimization algorithms are then used to calculate the shifts that will best cover the estimated staffing requirements.)
- a customer experience monitoring tool configured to determine a service level provided by the employees to customers (see at least Kosiba: ¶ [0053] & ¶ [0061] & ¶ [0074] & ¶ [0116] & Fig. 9. Kosiba teaches that the reports contain the following for each customer segment: the service level, the call wait time, the probability of abandon, the mean time to abandon, the cost of abandon, the average handle time, the customer value, the distribution of call purpose, and a customer satisfaction measure. See also Kosiba at ¶ [0053] noting “service level (e.g. of the proportion of calls answered in less than or equal to a specified number of seconds (e.g. 80% within 20 seconds).” See also Kosiba at ¶ [0074]: These performance summaries include the following: (1) call volume; (2) the number of phone agents available; (3) the number of phone agents occupied by answering calls (occupancy); (4) the average handle time; (5) the number of abandons; (6) the mean time to abandon; (7) the average wait time; (8) the service level achieved; and (9) the average call duration. See also Kosiba at ¶ [0116]: The corresponding outputs of the model are predictions and/or forecasts of hourly contact center performance metrics. The performance metrics may include: (1) expected service level. See also Kosiba at ¶ [0131]: Specifically, the service level field 976 displays a percentage of the total calls that receive service that meets a specified service goal (e.g., 90% of calls have a wait time less than 20 seconds). The average speed of answer field 977 displays the average wait time for the customers serviced by the management unit (e.g., 7.1 seconds).)
- a virtual manager (see at least Kosiba: Figs. 1-4 & Fig. 9 & ¶ [0099-0100]. Kosiba notes developing reports on customer segments (step 715) includes processing that allows the planning and analysis system 205 to describe more accurately the bounds and dispersion of the experience of a given customer segment when interacting with the contact center. This allows managers to access information and generate reports that provide them with a better understanding of how contact center performance is distributed among different customer segments. The summary statistics are then incorporated into a report that describes the customer behavior of a given customer segment. The report may be used by managers to obtain a better understanding of how contact center performance impacts different customer segments.) configured to analyze the employment information (see at least Kosiba: ¶ [0055] & ¶ [0065] & ¶ [0126] & Fig. 9. Kosiba notes that the HRD 120 receives the forecast from the WFMS 115 and uses it to determine when to hire new employees and when to schedule training classes. See also Kosiba at ¶ [0065]: The amount of training required for new hires and the ramp-up time for new hires (e.g., four weeks to train a new hire and then new hire is 30% as effective as an experienced agent during the first week, 80% as effective during the second week, and 100% as effective from then on); (8) the weekly attrition rate for new hires prior to their becoming 100% effective (e.g. 25%); (9) the amount of employee vacation time planned; (10) the expected employee sick time; (11) the estimated schedule non-adherence (e.g., an agent at work is actually available to receive calls for only 70% of their paid hours); (12) the new hire cost. See also Kosiba at ¶ [0126]: The number of effective full time CSRs for the month must also be modified by the number of full-time new hires. Since new hires are not as productive as seasoned employees, the number of new hires cannot simply be added to the number of CSRs in the calculation above. Rather, referring to FIG. 11, the number of new hires is converted into an effective number of CSRs by multiplying the number of new hires by an effectiveness percentage for each week of the month. The resulting figure is then multiplied by the quantity one minus the corresponding new hire attrition rate for that week.), the employee performance data (see at least Kosiba: ¶ [0031-0033] & ¶ [0117]. Kosiba teaches that the granular contact center performance data is then processed into daily, weekly, or monthly performance data (step 810). Assuming an hour is the time increment, the granular performance data is processed into daily, weekly, or monthly performance data by applying the hourly staff distribution, call volume distribution, customer patience distribution, handle time distribution, and phone line distribution developed in step 700 to a given daily, weekly, or monthly staffing level, call volume, customer patience, handle time, and number of phone lines, respectively. See also Kosiba at ¶ [0031]: “May include predicting process performance metrics of the processing center system and using the process performance metrics to generate employee schedules.” See also Kosiba at ¶ [0033]: Developing a computer model of the processing operation based on the performance information, and using the computer model to generate expected performance metrics of the processing operation over a predetermined range of inputs. The method also includes storing the expected performance metrics and the range of inputs in a performance database, and accessing the performance database and analyzing the expected performance metrics for producing the resource and performance plans.), the scheduling information (see at least Kosiba: ¶ [0054-0055] & ¶ [0065] & ¶ [0123-0124] & Fig. 9. Kosiba notes that The WFMS 115 sends schedules, shift swaps, schedule adherence, and hours worked data to the HRD 120. See also Kosiba noting that at ¶ [0065]: “the estimated schedule non-adherence (e.g., an agent at work is actually available to receive calls for only 70% of their paid hours).” See also Kosiba noting that at ¶ [0123-0124]: The values in the full-time staff fields 935, the part time staff fields 940, and the schedule adherence field 971 may be used to determine the effective staff value (field 972).), the forecasted employee requirements (see at least Kosiba: ¶ [0054]. Kosiba notes that the WFMS 115 then determines, using a variety of algorithms, the phone agent staffing levels by time of day that will result in the center's meeting an overall service standard goal for the forecasted number of contacts. Shift optimization algorithms are then used to calculate the shifts that will best cover the estimated staffing requirements.), the expected budget (see at least Kosiba: ¶ [0060] & ¶ [0149] & ¶ [0175]. Kosiba notes that the staffing plan includes the hiring plan; the number of vacation, sick, and training days; the number of agents transferred between management units; and the skills of the agents (e.g. able to speak Spanish or that can handle complex requests well). The WFMS 115 incorporates all of this information into the staffing schedule. The analysis system 205 also provides the staffing plan to the HRD 120 and its analysts along with a budget estimate corresponding to that staffing plan. See also Kosiba at ¶ [0149]: Multiple windows containing the fields in the user interface 900 may be active at one time. Each window represents a different scenario and, therefore, multiple scenarios may be analyzed simultaneously. This allows the user to immediately see the benefits of one staff planning or budget scenario against a different scenario. Since the performance outputs are determined by a simple query of the planning and analysis database, the user may quickly calculate performance figures for multiple scenarios simultaneously. See also Kosiba at ¶ [0175]: The analysis system 205 provides to the HRD 120 and its analyst the staffing plan and the corresponding budget for that staffing plan as estimated in steps 320 and 325. Lastly, the analysis system 205 provides the customer reports generated in step 715 to CIS 125 and its analysts.), and the service level (see at least Kosiba: ¶ [0053] & ¶ [0061] & ¶ [0074] & ¶ [0116] & Fig. 9. Kosiba teaches that the reports contain the following for each customer segment: the service level, the call wait time, the probability of abandon, the mean time to abandon, the cost of abandon, the average handle time, the customer value, the distribution of call purpose, and a customer satisfaction measure. See also Kosiba at ¶ [0053] noting “service level (e.g. of the proportion of calls answered in less than or equal to a specified number of seconds (e.g. 80% within 20 seconds).” See also Kosiba at ¶ [0074]: These performance summaries include the following: (1) call volume; (2) the number of phone agents available; (3) the number of phone agents occupied by answering calls (occupancy); (4) the average handle time; (5) the number of abandons; (6) the mean time to abandon; (7) the average wait time; (8) the service level achieved; and (9) the average call duration. See also Kosiba at ¶ [0116]: The corresponding outputs of the model are predictions and/or forecasts of hourly contact center performance metrics. The performance metrics may include: (1) expected service level. See also Kosiba at ¶ [0131]: Specifically, the service level field 976 displays a percentage of the total calls that receive service that meets a specified service goal (e.g., 90% of calls have a wait time less than 20 seconds). The average speed of answer field 977 displays the average wait time for the customers serviced by the management unit (e.g., 7.1 seconds).).
Moreover, regarding Independent Claim 1, Kosiba system of workforce optimization systems and methods does not explicitly disclose, but McConnell in the analogous art for workforce optimization systems and methods teaches the following limitations:
- determine one or more recommended actions for optimizing the workforce (see at least McConnell: ¶ [0059] & ¶ [0107-0108] & ¶ [0112-0114]. McConnell teaches the recommendation engine 314 of the management network 300 may analyze data from the workforce management server 326 and/or may recommend or design rules (or rules modifications) that include triggers, conditions, and/or actions involving the workforce management server 326. The recommendation engine 314 accesses data stored in the database devices 306 and uses AI algorithms to analyze rules and performance of a particular end-user network, such end-user network 320 relating to those rules. Such analysis could include, for example, determining whether and how often each rule is triggered, whether and how often each rule satisfies one or more conditions stated in each rule, whether actions are successfully executed upon conditions being satisfied for each rule, what resulted from such actions being executed (e.g., changes to metrics and/or statistics, such as handle time, hold time, service level adherence, etc.), and others. The analyzed data could include stored received raw data, enriched data, or a combination of raw data and enriched data. See also McConnell notes at ¶ [0059]: “Workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch, scheduling and rescheduling training and other off-phone activities, etc. This creates an environment where various actions should take place throughout the day to effectively manage the workforce while achieving service level goals. This results in constant workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch and scheduling..”.) while maintaining the service level at or above a minimum service level (see at least McConnell: ¶ [0221-0222] & ¶ [0234-0235] & ¶ [0350-0354]. McConnell teaches that priorities, goals, requirements of the user, and relevant time periods for which granular data is needed (e.g., every agent state change) may be ascertained via web-served questionnaires, menu selections, slide-bars, or other GUI-based mechanisms, for example. Examples of goals that may be input at administrator instance 344 include the following: service level thresholds across queues (e.g., answer 75%-80% of all calls within 20 seconds), specific wait times, occupancy thresholds (agents are occupied a prescribed percent of time, helping productivity), staffing tolerances, and goals (internal constraints) specific to the management network 300 (e.g., agents have fewer than three outstanding assignments, agents receive a prescribed number of training sessions each month, etc.). McConnell also notes “whether actions are successfully executed upon conditions being satisfied for each rule, what resulted from such actions being executed (e.g., changes to metrics and/or statistics, such as handle time, hold time, service level adherence, etc.), and others. The analyzed data could include stored received raw data, enriched data, or a combination of raw data and enriched data.” See also McConnell notes at ¶ [0234]: The AI algorithm(s) utilized by recommendation engine 314 may determine that the most minutes of downtime (i.e., outliers) are found on Thursdays between 2:00 pm and 4:00 pm EST each week. This determination can be leveraged by recommendation engine 314 to recommend new rules to the administrator instance 344 on agent network 320. For example, the recommendation engine 314 may recommend a new rule that offers voluntary time off on Thursdays when queue conditions are outperforming a specified service level. Another possible recommendation for the recommendation engine 314 would be to modify an existing rule action to reduce the number of agents staffed during the 2:00 pm and 3:00 pm intervals on Thursdays. See also McConnell notes at ¶ [0235]: The recommendation engine 314 can apply AI algorithms to data from incoming data streams cached and stored in the database devices 306 to recommend to the customer what each queue threshold would have to be set at in order to a) maintain service levels, while b) finding the prescribed number of hours per agent of training time needed in the next 30 days. The recommendation engine 314 would recommend a rules modification to set the specific thresholds by queue that would ensure that (a) training could be delivered (b) without causing overall service level commitments to be missed. See also McConnell notes at ¶ [0350-0352]: Agent instance related constraints (the time in which the agent is available to handle customer interactions), service level constraints (e.g., the service level should be between 80-90%), and combinations of these types of constraints (e.g., a schedule has to cost less than $X and also the average waiting time should be less than Z seconds). Example objective inputs may include minimizing costs, maximizing a service level of end-user network, and so on. In some cases, multiple objectives can be specified. For example, objective inputs can include minimizing costs while maximizing the service level for a specific communication queue in communication distributor. See also McConnell notes at ¶ [0354]: Using the service level example above, the computing device may determine whether the values for the set of actions result in the service level being between 80-90%. This can entail, for example, adding the action value for each action in the set of actions.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba system of workforce optimization systems and methods with the aforementioned teachings of: determining one or more recommended actions for optimizing the workforce while maintaining the service level at or above a minimum service level, and in view of McConnell, whereby knowing what action to take to what agent at what time is a challenge that is typically managed by a number of employees who are part of a real-time workforce management team. The actions taken throughout the day are often limited by how quickly and effectively this team can receive and process data in order to determine what action to take and to what agents. The technical solutions set forth herein provide these customer service operators with a way to create rules to automatically handle the above-described situations to make various adjustments to the workforce throughout the day. The disclosed technology further includes applying artificial intelligence (AI) algorithms, such as machine learning, to when these rules fire and to whom they fire, in order to identify opportunities to optimize the rules to drive out operational inefficiencies, which can improve agent performance (see at least McConnell: ¶ [0059].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by McConnell, the results of the combination were predictable.
Regarding Independent Claim 22, Kosiba method of workforce optimization systems and methods teaches the following:
- receiving workforce management information (see at least Kosiba: Fig. 1 noting “a workforce management system (WFMS 115)” & Fig. 9 & ¶ [0064]. Kosiba teaches organizing calling resources into management units 400 requires an understanding of how management organizes work and allocates the workforce. Management typically divides up the workforce into teams or units that manage a common function. Since the workforce associated with contact centers are frequently spread out geographically and interconnected to form a contact center system, management also may further divide up the workforce by geographic location. Thus, management units are typically a single, local, work team performing a common function (e.g. customer retention unit in location x or customer dispute resolution unit in location y).), the workforce management information comprising employment information about employees of a workforce (see at least Kosiba: ¶ [0055] & ¶ [0065] & ¶ [0126] & Fig. 9. Kosiba notes that the HRD 120 receives the forecast from the WFMS 115 and uses it to determine when to hire new employees and when to schedule training classes. See also Kosiba at ¶ [0065]: The amount of training required for new hires and the ramp-up time for new hires (e.g., four weeks to train a new hire and then new hire is 30% as effective as an experienced agent during the first week, 80% as effective during the second week, and 100% as effective from then on); (8) the weekly attrition rate for new hires prior to their becoming 100% effective (e.g. 25%); (9) the amount of employee vacation time planned; (10) the expected employee sick time; (11) the estimated schedule non-adherence (e.g., an agent at work is actually available to receive calls for only 70% of their paid hours); (12) the new hire cost. See also Kosiba at ¶ [0126]: The number of effective full time CSRs for the month must also be modified by the number of full-time new hires. Since new hires are not as productive as seasoned employees, the number of new hires cannot simply be added to the number of CSRs in the calculation above. Rather, referring to FIG. 11, the number of new hires is converted into an effective number of CSRs by multiplying the number of new hires by an effectiveness percentage for each week of the month. The resulting figure is then multiplied by the quantity one minus the corresponding new hire attrition rate for that week.), performance data of the employees (see at least Kosiba: ¶ [0031-0033] & ¶ [0117]. Kosiba teaches that the granular contact center performance data is then processed into daily, weekly, or monthly performance data (step 810). Assuming an hour is the time increment, the granular performance data is processed into daily, weekly, or monthly performance data by applying the hourly staff distribution, call volume distribution, customer patience distribution, handle time distribution, and phone line distribution developed in step 700 to a given daily, weekly, or monthly staffing level, call volume, customer patience, handle time, and number of phone lines, respectively. See also Kosiba at ¶ [0031]: “May include predicting process performance metrics of the processing center system and using the process performance metrics to generate employee schedules.” See also Kosiba at ¶ [0033]: Developing a computer model of the processing operation based on the performance information, and using the computer model to generate expected performance metrics of the processing operation over a predetermined range of inputs. The method also includes storing the expected performance metrics and the range of inputs in a performance database, and accessing the performance database and analyzing the expected performance metrics for producing the resource and performance plans.), scheduling information for the employees (see at least Kosiba: ¶ [0054-0055] & ¶ [0065] & ¶ [0123-0124] & Fig. 9. Kosiba notes that The WFMS 115 sends schedules, shift swaps, schedule adherence, and hours worked data to the HRD 120. See also Kosiba noting that at ¶ [0065]: “the estimated schedule non-adherence (e.g., an agent at work is actually available to receive calls for only 70% of their paid hours).” See also Kosiba noting that at ¶ [0123-0124]: The values in the full-time staff fields 935, the part time staff fields 940, and the schedule adherence field 971 may be used to determine the effective staff value (field 972).), forecasted employee requirements (see at least Kosiba: ¶ [0054]. Kosiba notes that the WFMS 115 then determines, using a variety of algorithms, the phone agent staffing levels by time of day that will result in the center's meeting an overall service standard goal for the forecasted number of contacts. Shift optimization algorithms are then used to calculate the shifts that will best cover the estimated staffing requirements.), an expected budget (see at least Kosiba: ¶ [0060] & ¶ [0149] & ¶ [0175]. Kosiba notes that the staffing plan includes the hiring plan; the number of vacation, sick, and training days; the number of agents transferred between management units; and the skills of the agents (e.g. able to speak Spanish or that can handle complex requests well). The WFMS 115 incorporates all of this information into the staffing schedule. The analysis system 205 also provides the staffing plan to the HRD 120 and its analysts along with a budget estimate corresponding to that staffing plan. See also Kosiba at ¶ [0149]: Multiple windows containing the fields in the user interface 900 may be active at one time. Each window represents a different scenario and, therefore, multiple scenarios may be analyzed simultaneously. This allows the user to immediately see the benefits of one staff planning or budget scenario against a different scenario. Since the performance outputs are determined by a simple query of the planning and analysis database, the user may quickly calculate performance figures for multiple scenarios simultaneously. See also Kosiba at ¶ [0175]: The analysis system 205 provides to the HRD 120 and its analyst the staffing plan and the corresponding budget for that staffing plan as estimated in steps 320 and 325. Lastly, the analysis system 205 provides the customer reports generated in step 715 to CIS 125 and its analysts.), and a service level provided by the employees to customers (see at least Kosiba: ¶ [0053] & ¶ [0061] & ¶ [0074] & ¶ [0116] & Fig. 9. Kosiba teaches that the reports contain the following for each customer segment: the service level, the call wait time, the probability of abandon, the mean time to abandon, the cost of abandon, the average handle time, the customer value, the distribution of call purpose, and a customer satisfaction measure. See also Kosiba at ¶ [0053] noting “service level (e.g. of the proportion of calls answered in less than or equal to a specified number of seconds (e.g. 80% within 20 seconds).” See also Kosiba at ¶ [0074]: These performance summaries include the following: (1) call volume; (2) the number of phone agents available; (3) the number of phone agents occupied by answering calls (occupancy); (4) the average handle time; (5) the number of abandons; (6) the mean time to abandon; (7) the average wait time; (8) the service level achieved; and (9) the average call duration. See also Kosiba at ¶ [0116]: The corresponding outputs of the model are predictions and/or forecasts of hourly contact center performance metrics. The performance metrics may include: (1) expected service level. See also Kosiba at ¶ [0131]: Specifically, the service level field 976 displays a percentage of the total calls that receive service that meets a specified service goal (e.g., 90% of calls have a wait time less than 20 seconds). The average speed of answer field 977 displays the average wait time for the customers serviced by the management unit (e.g., 7.1 seconds).)
Moreover, regarding Independent Claim 22, Kosiba method of workforce optimization systems and methods does not explicitly disclose, but McConnell in the analogous art for workforce optimization systems and methods teaches the following limitations:
- determine one or more recommended actions for optimizing the workforce (see at least McConnell: ¶ [0059] & ¶ [0107-0108] & ¶ [0112-0114]. McConnell teaches the recommendation engine 314 of the management network 300 may analyze data from the workforce management server 326 and/or may recommend or design rules (or rules modifications) that include triggers, conditions, and/or actions involving the workforce management server 326. The recommendation engine 314 accesses data stored in the database devices 306 and uses AI algorithms to analyze rules and performance of a particular end-user network, such end-user network 320 relating to those rules. Such analysis could include, for example, determining whether and how often each rule is triggered, whether and how often each rule satisfies one or more conditions stated in each rule, whether actions are successfully executed upon conditions being satisfied for each rule, what resulted from such actions being executed (e.g., changes to metrics and/or statistics, such as handle time, hold time, service level adherence, etc.), and others. The analyzed data could include stored received raw data, enriched data, or a combination of raw data and enriched data. See also McConnell notes at ¶ [0059]: “Workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch, scheduling and rescheduling training and other off-phone activities, etc. This creates an environment where various actions should take place throughout the day to effectively manage the workforce while achieving service level goals. This results in constant workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch and scheduling..”.) while maintaining the service level at or above a minimum service level (see at least McConnell: ¶ [0221-0222] & ¶ [0234-0235] & ¶ [0350-0354]. McConnell teaches that priorities, goals, requirements of the user, and relevant time periods for which granular data is needed (e.g., every agent state change) may be ascertained via web-served questionnaires, menu selections, slide-bars, or other GUI-based mechanisms, for example. Examples of goals that may be input at administrator instance 344 include the following: service level thresholds across queues (e.g., answer 75%-80% of all calls within 20 seconds), specific wait times, occupancy thresholds (agents are occupied a prescribed percent of time, helping productivity), staffing tolerances, and goals (internal constraints) specific to the management network 300 (e.g., agents have fewer than three outstanding assignments, agents receive a prescribed number of training sessions each month, etc.). McConnell also notes “whether actions are successfully executed upon conditions being satisfied for each rule, what resulted from such actions being executed (e.g., changes to metrics and/or statistics, such as handle time, hold time, service level adherence, etc.), and others. The analyzed data could include stored received raw data, enriched data, or a combination of raw data and enriched data.” See also McConnell notes at ¶ [0234]: The AI algorithm(s) utilized by recommendation engine 314 may determine that the most minutes of downtime (i.e., outliers) are found on Thursdays between 2:00 pm and 4:00 pm EST each week. This determination can be leveraged by recommendation engine 314 to recommend new rules to the administrator instance 344 on agent network 320. For example, the recommendation engine 314 may recommend a new rule that offers voluntary time off on Thursdays when queue conditions are outperforming a specified service level. Another possible recommendation for the recommendation engine 314 would be to modify an existing rule action to reduce the number of agents staffed during the 2:00 pm and 3:00 pm intervals on Thursdays. See also McConnell notes at ¶ [0235]: The recommendation engine 314 can apply AI algorithms to data from incoming data streams cached and stored in the database devices 306 to recommend to the customer what each queue threshold would have to be set at in order to a) maintain service levels, while b) finding the prescribed number of hours per agent of training time needed in the next 30 days. The recommendation engine 314 would recommend a rules modification to set the specific thresholds by queue that would ensure that (a) training could be delivered (b) without causing overall service level commitments to be missed. See also McConnell notes at ¶ [0350-0352]: Agent instance related constraints (the time in which the agent is available to handle customer interactions), service level constraints (e.g., the service level should be between 80-90%), and combinations of these types of constraints (e.g., a schedule has to cost less than $X and also the average waiting time should be less than Z seconds). Example objective inputs may include minimizing costs, maximizing a service level of end-user network, and so on. In some cases, multiple objectives can be specified. For example, objective inputs can include minimizing costs while maximizing the service level for a specific communication queue in communication distributor. See also McConnell notes at ¶ [0354]: Using the service level example above, the computing device may determine whether the values for the set of actions result in the service level being between 80-90%. This can entail, for example, adding the action value for each action in the set of actions.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba method of workforce optimization systems and methods with the aforementioned teachings of: determining one or more recommended actions for optimizing the workforce while maintaining the service level at or above a minimum service level, and in view of McConnell, whereby knowing what action to take to what agent at what time is a challenge that is typically managed by a number of employees who are part of a real-time workforce management team. The actions taken throughout the day are often limited by how quickly and effectively this team can receive and process data in order to determine what action to take and to what agents. The technical solutions set forth herein provide these customer service operators with a way to create rules to automatically handle the above-described situations to make various adjustments to the workforce throughout the day. The disclosed technology further includes applying artificial intelligence (AI) algorithms, such as machine learning, to when these rules fire and to whom they fire, in order to identify opportunities to optimize the rules to drive out operational inefficiencies, which can improve agent performance (see at least McConnell: ¶ [0059].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by McConnell, the results of the combination were predictable.
Regarding Independent Claim 43, Kosiba non-transitory computer-readable medium of workforce optimization systems and methods teaches the following:
- having computer executable instructed store thereon which, when executed by a processor, configure the processor to implement a method (see at least Kosiba: ¶ [0034] & ¶ [0176]. Kosiba teaches that the system also includes a processor configured to develop a computer model of the contact center system based on the performance information.) comprising:
- receiving workforce management information (see at least Kosiba: Fig. 1 noting “a workforce management system (WFMS 115)” & Fig. 9 & ¶ [0064]. Kosiba teaches organizing calling resources into management units 400 requires an understanding of how management organizes work and allocates the workforce. Management typically divides up the workforce into teams or units that manage a common function. Since the workforce associated with contact centers are frequently spread out geographically and interconnected to form a contact center system, management also may further divide up the workforce by geographic location. Thus, management units are typically a single, local, work team performing a common function (e.g. customer retention unit in location x or customer dispute resolution unit in location y).), the workforce management information comprising employment information about employees of a workforce (see at least Kosiba: ¶ [0055] & ¶ [0065] & ¶ [0126] & Fig. 9. Kosiba notes that the HRD 120 receives the forecast from the WFMS 115 and uses it to determine when to hire new employees and when to schedule training classes. See also Kosiba at ¶ [0065]: The amount of training required for new hires and the ramp-up time for new hires (e.g., four weeks to train a new hire and then new hire is 30% as effective as an experienced agent during the first week, 80% as effective during the second week, and 100% as effective from then on); (8) the weekly attrition rate for new hires prior to their becoming 100% effective (e.g. 25%); (9) the amount of employee vacation time planned; (10) the expected employee sick time; (11) the estimated schedule non-adherence (e.g., an agent at work is actually available to receive calls for only 70% of their paid hours); (12) the new hire cost. See also Kosiba at ¶ [0126]: The number of effective full time CSRs for the month must also be modified by the number of full-time new hires. Since new hires are not as productive as seasoned employees, the number of new hires cannot simply be added to the number of CSRs in the calculation above. Rather, referring to FIG. 11, the number of new hires is converted into an effective number of CSRs by multiplying the number of new hires by an effectiveness percentage for each week of the month. The resulting figure is then multiplied by the quantity one minus the corresponding new hire attrition rate for that week.), performance data of the employees (see at least Kosiba: ¶ [0031-0033] & ¶ [0117]. Kosiba teaches that the granular contact center performance data is then processed into daily, weekly, or monthly performance data (step 810). Assuming an hour is the time increment, the granular performance data is processed into daily, weekly, or monthly performance data by applying the hourly staff distribution, call volume distribution, customer patience distribution, handle time distribution, and phone line distribution developed in step 700 to a given daily, weekly, or monthly staffing level, call volume, customer patience, handle time, and number of phone lines, respectively. See also Kosiba at ¶ [0031]: “May include predicting process performance metrics of the processing center system and using the process performance metrics to generate employee schedules.” See also Kosiba at ¶ [0033]: Developing a computer model of the processing operation based on the performance information, and using the computer model to generate expected performance metrics of the processing operation over a predetermined range of inputs. The method also includes storing the expected performance metrics and the range of inputs in a performance database, and accessing the performance database and analyzing the expected performance metrics for producing the resource and performance plans.), scheduling information for the employees (see at least Kosiba: ¶ [0054-0055] & ¶ [0065] & ¶ [0123-0124] & Fig. 9. Kosiba notes that The WFMS 115 sends schedules, shift swaps, schedule adherence, and hours worked data to the HRD 120. See also Kosiba noting that at ¶ [0065]: “the estimated schedule non-adherence (e.g., an agent at work is actually available to receive calls for only 70% of their paid hours).” See also Kosiba noting that at ¶ [0123-0124]: The values in the full-time staff fields 935, the part time staff fields 940, and the schedule adherence field 971 may be used to determine the effective staff value (field 972).), forecasted employee requirements (see at least Kosiba: ¶ [0054]. Kosiba notes that the WFMS 115 then determines, using a variety of algorithms, the phone agent staffing levels by time of day that will result in the center's meeting an overall service standard goal for the forecasted number of contacts. Shift optimization algorithms are then used to calculate the shifts that will best cover the estimated staffing requirements.), an expected budget (see at least Kosiba: ¶ [0060] & ¶ [0149] & ¶ [0175]. Kosiba notes that the staffing plan includes the hiring plan; the number of vacation, sick, and training days; the number of agents transferred between management units; and the skills of the agents (e.g. able to speak Spanish or that can handle complex requests well). The WFMS 115 incorporates all of this information into the staffing schedule. The analysis system 205 also provides the staffing plan to the HRD 120 and its analysts along with a budget estimate corresponding to that staffing plan. See also Kosiba at ¶ [0149]: Multiple windows containing the fields in the user interface 900 may be active at one time. Each window represents a different scenario and, therefore, multiple scenarios may be analyzed simultaneously. This allows the user to immediately see the benefits of one staff planning or budget scenario against a different scenario. Since the performance outputs are determined by a simple query of the planning and analysis database, the user may quickly calculate performance figures for multiple scenarios simultaneously. See also Kosiba at ¶ [0175]: The analysis system 205 provides to the HRD 120 and its analyst the staffing plan and the corresponding budget for that staffing plan as estimated in steps 320 and 325. Lastly, the analysis system 205 provides the customer reports generated in step 715 to CIS 125 and its analysts.), and a service level provided by the employees to customers (see at least Kosiba: ¶ [0053] & ¶ [0061] & ¶ [0074] & ¶ [0116] & Fig. 9. Kosiba teaches that the reports contain the following for each customer segment: the service level, the call wait time, the probability of abandon, the mean time to abandon, the cost of abandon, the average handle time, the customer value, the distribution of call purpose, and a customer satisfaction measure. See also Kosiba at ¶ [0053] noting “service level (e.g. of the proportion of calls answered in less than or equal to a specified number of seconds (e.g. 80% within 20 seconds).” See also Kosiba at ¶ [0074]: These performance summaries include the following: (1) call volume; (2) the number of phone agents available; (3) the number of phone agents occupied by answering calls (occupancy); (4) the average handle time; (5) the number of abandons; (6) the mean time to abandon; (7) the average wait time; (8) the service level achieved; and (9) the average call duration. See also Kosiba at ¶ [0116]: The corresponding outputs of the model are predictions and/or forecasts of hourly contact center performance metrics. The performance metrics may include: (1) expected service level. See also Kosiba at ¶ [0131]: Specifically, the service level field 976 displays a percentage of the total calls that receive service that meets a specified service goal (e.g., 90% of calls have a wait time less than 20 seconds). The average speed of answer field 977 displays the average wait time for the customers serviced by the management unit (e.g., 7.1 seconds).)
Moreover, regarding Independent Claim 43, Kosiba non-transitory computer-readable medium of workforce optimization systems and methods does not explicitly disclose, but McConnell in the analogous art for workforce optimization systems and methods teaches the following limitations:
- determine one or more recommended actions for optimizing the workforce (see at least McConnell: ¶ [0059] & ¶ [0107-0108] & ¶ [0112-0114]. McConnell teaches the recommendation engine 314 of the management network 300 may analyze data from the workforce management server 326 and/or may recommend or design rules (or rules modifications) that include triggers, conditions, and/or actions involving the workforce management server 326. The recommendation engine 314 accesses data stored in the database devices 306 and uses AI algorithms to analyze rules and performance of a particular end-user network, such end-user network 320 relating to those rules. Such analysis could include, for example, determining whether and how often each rule is triggered, whether and how often each rule satisfies one or more conditions stated in each rule, whether actions are successfully executed upon conditions being satisfied for each rule, what resulted from such actions being executed (e.g., changes to metrics and/or statistics, such as handle time, hold time, service level adherence, etc.), and others. The analyzed data could include stored received raw data, enriched data, or a combination of raw data and enriched data. See also McConnell notes at ¶ [0059]: “Workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch, scheduling and rescheduling training and other off-phone activities, etc. This creates an environment where various actions should take place throughout the day to effectively manage the workforce while achieving service level goals. This results in constant workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch and scheduling..”.) while maintaining the service level at or above a minimum service level (see at least McConnell: ¶ [0221-0222] & ¶ [0234-0235] & ¶ [0350-0354]. McConnell teaches that priorities, goals, requirements of the user, and relevant time periods for which granular data is needed (e.g., every agent state change) may be ascertained via web-served questionnaires, menu selections, slide-bars, or other GUI-based mechanisms, for example. Examples of goals that may be input at administrator instance 344 include the following: service level thresholds across queues (e.g., answer 75%-80% of all calls within 20 seconds), specific wait times, occupancy thresholds (agents are occupied a prescribed percent of time, helping productivity), staffing tolerances, and goals (internal constraints) specific to the management network 300 (e.g., agents have fewer than three outstanding assignments, agents receive a prescribed number of training sessions each month, etc.). McConnell also notes “whether actions are successfully executed upon conditions being satisfied for each rule, what resulted from such actions being executed (e.g., changes to metrics and/or statistics, such as handle time, hold time, service level adherence, etc.), and others. The analyzed data could include stored received raw data, enriched data, or a combination of raw data and enriched data.” See also McConnell notes at ¶ [0234]: The AI algorithm(s) utilized by recommendation engine 314 may determine that the most minutes of downtime (i.e., outliers) are found on Thursdays between 2:00 pm and 4:00 pm EST each week. This determination can be leveraged by recommendation engine 314 to recommend new rules to the administrator instance 344 on agent network 320. For example, the recommendation engine 314 may recommend a new rule that offers voluntary time off on Thursdays when queue conditions are outperforming a specified service level. Another possible recommendation for the recommendation engine 314 would be to modify an existing rule action to reduce the number of agents staffed during the 2:00 pm and 3:00 pm intervals on Thursdays. See also McConnell notes at ¶ [0235]: The recommendation engine 314 can apply AI algorithms to data from incoming data streams cached and stored in the database devices 306 to recommend to the customer what each queue threshold would have to be set at in order to a) maintain service levels, while b) finding the prescribed number of hours per agent of training time needed in the next 30 days. The recommendation engine 314 would recommend a rules modification to set the specific thresholds by queue that would ensure that (a) training could be delivered (b) without causing overall service level commitments to be missed. See also McConnell notes at ¶ [0350-0352]: Agent instance related constraints (the time in which the agent is available to handle customer interactions), service level constraints (e.g., the service level should be between 80-90%), and combinations of these types of constraints (e.g., a schedule has to cost less than $X and also the average waiting time should be less than Z seconds). Example objective inputs may include minimizing costs, maximizing a service level of end-user network, and so on. In some cases, multiple objectives can be specified. For example, objective inputs can include minimizing costs while maximizing the service level for a specific communication queue in communication distributor. See also McConnell notes at ¶ [0354]: Using the service level example above, the computing device may determine whether the values for the set of actions result in the service level being between 80-90%. This can entail, for example, adding the action value for each action in the set of actions.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: determining one or more recommended actions for optimizing the workforce while maintaining the service level at or above a minimum service level, and in view of McConnell, whereby knowing what action to take to what agent at what time is a challenge that is typically managed by a number of employees who are part of a real-time workforce management team. The actions taken throughout the day are often limited by how quickly and effectively this team can receive and process data in order to determine what action to take and to what agents. The technical solutions set forth herein provide these customer service operators with a way to create rules to automatically handle the above-described situations to make various adjustments to the workforce throughout the day. The disclosed technology further includes applying artificial intelligence (AI) algorithms, such as machine learning, to when these rules fire and to whom they fire, in order to identify opportunities to optimize the rules to drive out operational inefficiencies, which can improve agent performance (see at least McConnell: ¶ [0059].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by McConnell, the results of the combination were predictable.
Regarding Dependent Claim 6, Kosiba / McConnell workforce optimization system teaches the limitations of Independent Claim 1 above, and McConnell further teaches the workforce optimization system comprising:
- wherein the one or more recommended actions (see at least McConnell: ¶ [0059] & ¶ [0107-0108] & ¶ [0112-0114]. McConnell teaches the recommendation engine 314 of the management network 300 may analyze data from the workforce management server 326 and/or may recommend or design rules (or rules modifications) that include triggers, conditions, and/or actions involving the workforce management server 326. The recommendation engine 314 accesses data stored in the database devices 306 and uses AI algorithms to analyze rules and performance of a particular end-user network, such end-user network 320 relating to those rules. Such analysis could include, for example, determining whether and how often each rule is triggered, whether and how often each rule satisfies one or more conditions stated in each rule, whether actions are successfully executed upon conditions being satisfied for each rule, what resulted from such actions being executed (e.g., changes to metrics and/or statistics, such as handle time, hold time, service level adherence, etc.), and others. The analyzed data could include stored received raw data, enriched data, or a combination of raw data and enriched data. See also McConnell notes at ¶ [0059]: “Workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch, scheduling and rescheduling training and other off-phone activities, etc. This creates an environment where various actions should take place throughout the day to effectively manage the workforce while achieving service level goals. This results in constant workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch and scheduling..”.) comprise one or more of:
- generating overtime offers to one or more employees (see at least McConnell: ¶ [0059]: McConnell teaches that constant workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime.)
- determining, based on the scheduling information, one or more available employees for scheduling (see at least McConnell: ¶ [0127] & ¶ [0135] & ¶ [0256]: McConnell teaches asking agents to leave the day early, sending agents to an early break or lunch, scheduling and rescheduling training and other off-phone activities, etc. See also McConnell at ¶ [0127]: Workforce management server 326 may utilize the received data to inform decisions regarding the scheduling of front-office agent instances. For example, if communication distributor 324 reports to workforce management server 326 that an influx of calls occurs every day around noon, workforce management server 326 may assign schedules for front-office agent instances that are able to satisfy such demand. See also McConnell at ¶ [0340]: Management network 300 may deal with the spike in communication volume by scheduling additional agent instances to service the extra communications or by reassigning current agent instances; for example, agent instances that are in a “training” state, to service the extra communications. Each action has its own benefits and drawbacks. For example, scheduling additional agent instances may improve response time, but may result in additional costs for end-user network 320. Reassigning current agent instances may also improve response time but may prevent the reassigned agent instances from receiving critical training modules.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the one or more recommended actions comprise one or more of: generating overtime offers to one or more employees; and determining, based on the scheduling information, one or more available employees for scheduling, and in further view of McConnell, whereby knowing what action to take to what agent at what time is a challenge that is typically managed by a number of employees who are part of a real-time workforce management team. The actions taken throughout the day are often limited by how quickly and effectively this team can receive and process data in order to determine what action to take and to what agents. The technical solutions set forth herein provide these customer service operators with a way to create rules to automatically handle the above-described situations to make various adjustments to the workforce throughout the day. The disclosed technology further includes applying artificial intelligence (AI) algorithms, such as machine learning, to when these rules fire and to whom they fire, in order to identify opportunities to optimize the rules to drive out operational inefficiencies, which can improve agent performance (see at least McConnell: ¶ [0059].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by McConnell, the results of the combination were predictable.
Regarding Dependent Claim 7, Kosiba / McConnell workforce optimization system teaches the limitations of Claims 1 and 6-7 above, and McConnell further teaches the workforce optimization system comprising:
- wherein the one or more employees and/or the one or more available employees are determined based on performance and/or based on cost (see at least McConnell: ¶ [0064] & ¶ [0234]. McConnell teaches supervisors physically observing agents and/or by supervisors periodically reviewing compiled agent performance data. The present disclosure describes the creation of rules that result in operations (actions) being taken upon certain triggers and conditions occurring. Under such rules, an operation can be automatically performed or initiated, based on a defined specification with logical directives including conditions that, if satisfied by the received agent live-monitoring data, define the operations. See also McConnell at ¶ [0234]: For example, the recommendation engine 314 may recommend a new rule that offers voluntary time off on Thursdays when queue conditions are outperforming a specified service level. Another possible recommendation for the recommendation engine 314 would be to modify an existing rule action to reduce the number of agents staffed during the 2:00 pm and 3:00 pm intervals on Thursdays. Both of these recommendations would reduce overall operational cost by avoiding unnecessary hourly expenses for the staffed agents.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the one or more employees and/or the one or more available employees are determined based on performance and/or based on cost, and in further view of McConnell, whereby knowing what action to take to what agent at what time is a challenge that is typically managed by a number of employees who are part of a real-time workforce management team. The actions taken throughout the day are often limited by how quickly and effectively this team can receive and process data in order to determine what action to take and to what agents. The technical solutions set forth herein provide these customer service operators with a way to create rules to automatically handle the above-described situations to make various adjustments to the workforce throughout the day. The disclosed technology further includes applying artificial intelligence (AI) algorithms, such as machine learning, to when these rules fire and to whom they fire, in order to identify opportunities to optimize the rules to drive out operational inefficiencies, which can improve agent performance (see at least McConnell: ¶ [0059].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by McConnell, the results of the combination were predictable.
Regarding Dependent Claim 10, Kosiba / McConnell workforce optimization system teaches the limitations of Independent Claim 1 above, and McConnell further teaches the workforce optimization system comprising:
- wherein the workforce planning tool uses a machine learning model trained (see at least McConnell: ¶ [0107] & ¶ [0224] & ¶ [0260-0262]. McConnell teaches that the machine learning models described above may similarly be trained/applied to ascertain changes on the user interface of agent instance 322A. See also McConnell noting “machine learning” at ¶ [0097] & ¶ [0107]: “Recommendation engine 314 may apply AI algorithms (e.g., machine learning, predictive algorithms, and/or quantitative analysis, among others) to historical data pertaining to end-user network 320 (and perhaps other data, such as industry data) to identify trends and/or opportunities for rules optimization.”) on historical employee requirements to determine the forecasted employee requirements (see at least McConnell: ¶ [0107] & ¶ [0112] & ¶ [0221-0223]. McConnell teaches that workforce management server 326 implements workforce management services with the end-user network 320 by forecasting labor requirements and creating and managing staff schedules to accomplish tasks according to an acceptable or preferred timeline.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the workforce planning tool uses a machine learning model trained on historical employee requirements to determine the forecasted employee requirements, and in further view of McConnell, whereby knowing what action to take to what agent at what time is a challenge that is typically managed by a number of employees who are part of a real-time workforce management team. The actions taken throughout the day are often limited by how quickly and effectively this team can receive and process data in order to determine what action to take and to what agents. The technical solutions set forth herein provide these customer service operators with a way to create rules to automatically handle the above-described situations to make various adjustments to the workforce throughout the day. The disclosed technology further includes applying artificial intelligence (AI) algorithms, such as machine learning, to when these rules fire and to whom they fire, in order to identify opportunities to optimize the rules to drive out operational inefficiencies, which can improve agent performance (see at least McConnell: ¶ [0059].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by McConnell, the results of the combination were predictable.
Regarding Dependent Claim 11, Kosiba / McConnell workforce optimization system teaches the limitations of Independent Claim 1 above, and Kosiba further teaches the workforce optimization system comprising:
- wherein the agent suite tool (see at least Kosiba: Figs. 1-3.) is further configured to determine recommended coaching and/or training for employees based on the performance data (see at least Kosiba: ¶ [0055] & ¶ [0057-0060].)
Regarding Dependent Claim 19, Kosiba / McConnell workforce optimization system teaches the limitations of Independent Claim 1 above, and McConnell further teaches the workforce optimization system comprising:
- wherein the virtual manager is configured to generate and output a user interface for display to a user, the user interface displaying information (see at least McConnell: ¶ [0262] & ¶ [0287] & Figs. 7A-7P. McConnell teaches that the monitoring application 920 operates on a user interface of an agent instance, such as agent instance 322A or 342A. For example, if communication distributor 324 shifts agent instance 322A from an “available” state to an “in-communication state,” the shift may materialize on the user interface of agent instance 322A as an image change. The machine learning models described above may similarly be trained/applied to ascertain changes on the user interface of agent instance 322A. “Examiner notes that Figs. 7A-7P of McConnell contains user interfaces display information to a user”.) of one or more of: the employment information, the employee performance data, the scheduling information, the forecasted employee requirements, the expected budget, the service level, and the one or more recommended actions (see at least McConnell: ¶ [0059] & ¶ [0168-0170] & Figs. 7A-7P. McConnell notes specifically at ¶ [0170] notes that the user selected “WFM” from provider category menu 722, then operational metrics related to workforce management server 326 may be displayed in condition menu 726, such as “Shift Start Time” and “Shift End Time”. And had the user selected “Intradiem” from provider category menu 722, then operational metrics related to management network 300 may be displayed in condition menu 726, such as “Number of Agents Logged In” and “Percent of Agents Logged In”. In FIG. 7B, provider instance menu 726 indicates, with a darker background, that the user selected the “Call Duration” metric. See also McConnell at ¶ [0184] and Fig. 7D: FIG. 7D depicts an action specification pane 740 of a rule design tool. As noted previously, actions may allow the user to specify operations that management network 300 may be perform on behalf of end-user network 320. Different types of information about the actions may be displayed.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the virtual manager is configured to generate and output a user interface for display to a user, the user interface displaying information of one or more of the employment information, the employee performance data, the scheduling information, the forecasted employee requirements, the expected budget, the service level, and the one or more recommended actions, and in further view of McConnell, whereby knowing what action to take to what agent at what time is a challenge that is typically managed by a number of employees who are part of a real-time workforce management team. The actions taken throughout the day are often limited by how quickly and effectively this team can receive and process data in order to determine what action to take and to what agents. The technical solutions set forth herein provide these customer service operators with a way to create rules to automatically handle the above-described situations to make various adjustments to the workforce throughout the day. The disclosed technology further includes applying artificial intelligence (AI) algorithms, such as machine learning, to when these rules fire and to whom they fire, in order to identify opportunities to optimize the rules to drive out operational inefficiencies, which can improve agent performance (see at least McConnell: ¶ [0059].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by McConnell, the results of the combination were predictable.
Regarding Dependent Claim 20, Kosiba / McConnell workforce optimization system teaches the limitations of Claims 1 and 19 above, and McConnell further teaches the workforce optimization system comprising:
- wherein the virtual manager is configured to implement the one or more recommended actions (see at least McConnell: ¶ [0059] & ¶ [0107-0108] & ¶ [0112-0114]. McConnell teaches the recommendation engine 314 of the management network 300 may analyze data from the workforce management server 326 and/or may recommend or design rules (or rules modifications) that include triggers, conditions, and/or actions involving the workforce management server 326. The recommendation engine 314 accesses data stored in the database devices 306 and uses AI algorithms to analyze rules and performance of a particular end-user network, such end-user network 320 relating to those rules. Such analysis could include, for example, determining whether and how often each rule is triggered, whether and how often each rule satisfies one or more conditions stated in each rule, whether actions are successfully executed upon conditions being satisfied for each rule, what resulted from such actions being executed (e.g., changes to metrics and/or statistics, such as handle time, hold time, service level adherence, etc.), and others. The analyzed data could include stored received raw data, enriched data, or a combination of raw data and enriched data. See also McConnell notes at ¶ [0059]: “Workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch, scheduling and rescheduling training and other off-phone activities, etc. This creates an environment where various actions should take place throughout the day to effectively manage the workforce while achieving service level goals. This results in constant workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch and scheduling..”.) in response to user input (see at least McConnell: ¶ [0176] & ¶ [0351] & ¶ [0381-0382] & Fig. 7B. McConnell notes that the selection from operator menu 730 and the input of operator input 732 form a logical comparison. This logical comparison may be evaluated against the operational metric specified in FIG. 7B. If the operational metric selected in FIG. 7B satisfies (i.e., evaluates to true) the logical comparison, then the rule's action(s) may be performed.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the virtual manager is configured to implement the one or more recommended actions in response to user input, and in further view of McConnell, whereby knowing what action to take to what agent at what time is a challenge that is typically managed by a number of employees who are part of a real-time workforce management team. The actions taken throughout the day are often limited by how quickly and effectively this team can receive and process data in order to determine what action to take and to what agents. The technical solutions set forth herein provide these customer service operators with a way to create rules to automatically handle the above-described situations to make various adjustments to the workforce throughout the day. The disclosed technology further includes applying artificial intelligence (AI) algorithms, such as machine learning, to when these rules fire and to whom they fire, in order to identify opportunities to optimize the rules to drive out operational inefficiencies, which can improve agent performance (see at least McConnell: ¶ [0059].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by McConnell, the results of the combination were predictable.
Regarding Dependent Claim 21, Kosiba / McConnell workforce optimization system teaches the limitations of Independent Claim 1 above, and McConnell further teaches the workforce optimization system comprising:
- wherein the virtual manager is configured to automatically implement the one or more recommended actions (see at least McConnell: ¶ [0059] & ¶ [0107-0108] & ¶ [0112-0114]. McConnell teaches the recommendation engine 314 of the management network 300 may analyze data from the workforce management server 326 and/or may recommend or design rules (or rules modifications) that include triggers, conditions, and/or actions involving the workforce management server 326. The recommendation engine 314 accesses data stored in the database devices 306 and uses AI algorithms to analyze rules and performance of a particular end-user network, such end-user network 320 relating to those rules. Such analysis could include, for example, determining whether and how often each rule is triggered, whether and how often each rule satisfies one or more conditions stated in each rule, whether actions are successfully executed upon conditions being satisfied for each rule, what resulted from such actions being executed (e.g., changes to metrics and/or statistics, such as handle time, hold time, service level adherence, etc.), and others. The analyzed data could include stored received raw data, enriched data, or a combination of raw data and enriched data. See also McConnell notes at ¶ [0059]: “Workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch, scheduling and rescheduling training and other off-phone activities, etc. This creates an environment where various actions should take place throughout the day to effectively manage the workforce while achieving service level goals. This results in constant workforce adjustments that may include, for example, moving agents across service channels, moving agents to service additional queues, asking agents to work overtime, asking agents to leave the day early, sending agents to an early break or lunch and scheduling..”.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the virtual manager is configured to automatically implement the one or more recommended actions, and in further view of McConnell, whereby knowing what action to take to what agent at what time is a challenge that is typically managed by a number of employees who are part of a real-time workforce management team. The actions taken throughout the day are often limited by how quickly and effectively this team can receive and process data in order to determine what action to take and to what agents. The technical solutions set forth herein provide these customer service operators with a way to create rules to automatically handle the above-described situations to make various adjustments to the workforce throughout the day. The disclosed technology further includes applying artificial intelligence (AI) algorithms, such as machine learning, to when these rules fire and to whom they fire, in order to identify opportunities to optimize the rules to drive out operational inefficiencies, which can improve agent performance (see at least McConnell: ¶ [0059].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by McConnell, the results of the combination were predictable.
19. Claims 2-3 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2002/0184069 A1) hereinafter Kosiba, et. al., and in view of US PG Pub (US 2022/0263703 A1) hereinafter McConnell, et. al., and in further view of US Patent # (US 9,595,016 B1) hereinafter Schwartz, et. al.
Regarding Dependent Claim 2, Kosiba / McConnell system of workforce optimization systems and methods does not explicitly disclose, but Schwartz in the analogous art for workforce optimization systems and methods teaches the following limitations:
- wherein the virtual manager is configured to determine a gap between a current expected demand for employee requirements and a supply of employee availability (see at least Schwartz: Fig. 6 & Col. 15, Lns. 35-67. Schwartz teaches that WFM systems have a limited ability to deal with situations where the actual demand or supply of agents differs from what has been forecast. For example, if the WFM system forecasts a demand of 200 agents for a given day, and schedules 200 agents accordingly, but the actual demand is 225 agents and only 175 show up to work, the system can do little to enable the company to adjust for the unexpected gap of 50 for that day, beyond offer reporting tools to track staffing level gaps.), and to determine the one or more recommended actions to minimize the gap (see at least Schwartz: Col. 16, Lns. 60-67 & Col. 17, Lns. 44-62.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell system of workforce optimization systems and methods with the aforementioned teachings of: wherein the virtual manager is configured to determine a gap between a current expected demand for employee requirements and a supply of employee availability and to determine the one or more recommended actions to minimize the gap, and in further view of Schwartz, whereby the disclosed technology could be beneficially employed in a broad variety of scenarios, and would be particularly useful in business environments having one or more of the following attributes: Large workforce with common assignment types, Variations in demand that are not always predictable, Variations in workforce supply that are not always predictable, and Business sensitivity to intraday gaps between workforce supply and demand (see at least Schwartz: (Col. 18, Lns. 14-26)).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by Schwartz, the results of the combination were predictable.
Regarding Dependent Claim 3, Kosiba / McConnell / Schwartz workforce optimization system teaches the limitations of Claims 1-2 above, and Schwartz further teaches the workforce optimization system comprising:
- wherein the virtual manager determines the current expected demand by forecasting employee requirements for a current interval (see at least Schwartz: Claim 7 & Col. 15, Lns. 29-43. Schwartz notes that to schedule large hourly workforces, most companies utilize WFM software-based products that provide forecasting and scheduling capability. The forecasting capabilities typically utilize analytics against historical patterns of demand to predict future demand. See also Claim 7 of Schwartz: “Contract requirements impacted by the detected discrepancy between the projected level of staff demand during the shift and the updated level of staff demand during the shift.”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell / Schwartz system of workforce optimization systems and methods with the aforementioned teachings of: wherein the virtual manager determines the current expected demand by forecasting employee requirements for a current interval, and in further view of Schwartz, whereby the disclosed technology could be beneficially employed in a broad variety of scenarios, and would be particularly useful in business environments having one or more of the following attributes: Large workforce with common assignment types, Variations in demand that are not always predictable, Variations in workforce supply that are not always predictable, and Business sensitivity to intraday gaps between workforce supply and demand (see at least Schwartz: (Col. 18, Lns. 14-26)).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by Schwartz, the results of the combination were predictable.
Regarding Dependent Claim 5, Kosiba / McConnell / Schwartz workforce optimization system teaches the limitations of Claims 1-2 above, and Schwartz further teaches the workforce optimization system comprising:
- wherein the virtual manager determines the supply of employee availability based on an actual and expected employee availability, wherein the actual and expected employee availability is determined based on the employment information, the scheduling information, and the forecasted employee requirements (see at least Schwartz: Col. 15, Lns. 27-44 & Col. 17, Lns. 9-15 & Col. 18, Lns. 15-24.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell / Schwartz system of workforce optimization systems and methods with the aforementioned teachings of: wherein the virtual manager determines the supply of employee availability based on an actual and expected employee availability, wherein the actual and expected employee availability is determined based on the employment information, the scheduling information, and the forecasted employee requirements, and in further view of Schwartz, whereby the disclosed technology could be beneficially employed in a broad variety of scenarios, and would be particularly useful in business environments having one or more of the following attributes: Large workforce with common assignment types, Variations in demand that are not always predictable, Variations in workforce supply that are not always predictable, and Business sensitivity to intraday gaps between workforce supply and demand (see at least Schwartz: (Col. 18, Lns. 14-26)).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by Schwartz, the results of the combination were predictable.
20. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2002/0184069 A1) hereinafter Kosiba, et. al., and in view of US PG Pub (US 2022/0263703 A1) hereinafter McConnell, et. al., in view of US Patent # (US 9,595,016 B1) hereinafter Schwartz, et. al, and in further view of US PG Pub (US 2023/0297907 A1) hereinafter Kaplan, et. al.
Regarding Dependent Claim 4, Kosiba / McConnell / Schwartz system of workforce optimization systems and methods, as applied to Claims 1-3, does not explicitly disclose, but Kaplan in the analogous art for workforce optimization systems and methods teaches the following limitations:
- wherein forecasting the employee requirements for the current interval comprises using a machine learning model selected from a plurality of machine learning models (see at least Kaplan: Fig. 3 & ¶ [0065-0066] & ¶ [0080-0081].) based on accuracy of previous forecasted employee requirements compared to actual employee requirements (see at least Kaplan: ¶ [0007] & ¶ [0076-0081] & ¶ [0099]. Kaplan teaches that a service metric value expected to be achieved by the initial staffing requirement (block B.3) handling the forecasted workload (block B.1) may then be predicted using a machine learning service level prediction model (block B.4), for example by inputting the initial allocation assignment to a machine learning algorithm, wherein the machine learning algorithm has been previously trained on historic data of a plurality of past intervals. Embodiments of the invention relate to a novel approach for using neural networks to predict the service metrics which may be provided in an interval by a certain staffing for a particular workload.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell / Schwartz system of workforce optimization systems and methods with the aforementioned teachings of: wherein forecasting the employee requirements for the current interval comprises using a machine learning model selected from a plurality of machine learning models based on accuracy of previous forecasted employee requirements compared to actual employee requirements, and in further view of Kaplan, in order to a novel search approach is applied over possible inputs to the trained model, leveraging the trained model as a means for selecting the optimal staffing requirement, so that the net staffing will be as low as possible while providing the required service levels. This may improve the technologies of machine learning. This algorithm differs from other existing methods in that it utilizes a resource unavailable until now, the historical data on workload, agents, and the contact center for service metric prediction (see at least Kaplan: ¶ [0040]).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by Kaplan, the results of the combination were predictable.
21. Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2002/0184069 A1) hereinafter Kosiba, et. al., and in view of US PG Pub (US 2022/0263703 A1) hereinafter McConnell, et. al., and in further view of US PG Pub (US 2021/0350310 A1) hereinafter Tashkin.
Regarding Dependent Claim 8, Kosiba / McConnell system of workforce optimization systems and methods does not explicitly disclose, but Tashkin in the analogous art for workforce optimization systems and methods teaches the following limitations:
- wherein the virtual manager is configured to determine a variance between an expected actual budget for a time period and a planned budget for the time period (see at least Tashkin: Figs. 6-7 & ¶ [0079-0080] & ¶ [0108]. Tashkin notes that the report 602 may include financial, schedule, estimate, and performance data. In addition, the report 602 may enable a user to compare estimated or budget values to actual values. Moreover, the report 602 and the system 200 (shown in FIG. 2) allow aggregation of costs of all personnel for a single or multiple multi-task projects. In addition, the report 602 can include data for labor productivity for one or more multi-task projects and the labor productivity may be broken down by task and individual workers. See also Tashkin at ¶ [0079]: Tashkin notes that the workforce system 200 includes a user interface which facilitates visual representations for resource planner 310. In addition, the resource planner 310 of the workforce system 200 provides access and/or editing for level project resource planning, job orders and placements, demand entry, worker assignment, planned hours and cost by resource, approved time hours and costs, comparison of task variance versus planned metrics, and resource loading by project, job order, or individual.), and to determine the one or more recommended actions to minimize the variance (see at least Tashkin: ¶ [0045] & ¶ [0079-0080] & ¶ [0108]. Tashkin notes that the WS server processes the worker assignment data outputted by the WS computing device and determines if additional action is required. For example, the WS server may compare the task data and the assigned worker data to see if any roles have not been filled with workers. The WS system may output the worker assignment schedule and a list of unassigned roles to facilitate the recruitment or identification of potential workers for the unassigned roles in the worker assignment schedule. The WS system may provide alerts when there are one or more unassigned roles in the worker assignment schedule and/or when there are changes in the availability and/or location data for at least one worker in the worker assignment schedule. In addition, the WS system may search the worker database and/or other worker sources such as online job websites to locate potential workers.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the virtual manager is configured to determine a variance between an expected actual budget for a time period and a planned budget for the time period and to determine the one or more recommended actions to minimize the variance, and in further view of Tashkin, whereby at least one of the technical problems addressed by this system may include: (i) improving availability of task assignment data that comes from multiple sources in different applications and systems; (ii) identifying workers for a multi-task project before the project begins using task specific criteria; (iii) timely identifying and providing data regarding unassigned roles in multi-task projects; (iv) providing reliable data regarding the multi-task project for workforce and task decisions; (v) enabling tracking of performance data for workers and tasks; (vi) relating the project location to locations of available workers in a manner that is simple to interpret and utilize; (vii) improving project estimates and forecasts which may be inaccurate because relevant data may not be available to estimating systems and applications; (viii) enabling accurate management and forecasting for multi-task projects which may not share data on similar tasks and a shared worker pool in an easily accessible manner (see at least Tashkin: ¶ [0046].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by Tashkin, the results of the combination were predictable.
Regarding Dependent Claim 9, Kosiba / McConnell / Tashkin workforce optimization system teaches the limitations of Claims 1 and 8 above, and Tashkin further teaches the workforce optimization system comprising:
- wherein the planned budget for the time period is determined for the time period using the budget tool (see at least Tashkin: Figs. 6-7 & ¶ [0079-0080] & ¶ [0108]. Tashkin notes that the report 602 may include financial, schedule, estimate, and performance data. In addition, the report 602 may enable a user to compare estimated or budget values to actual values. Moreover, the report 602 and the system 200 (shown in FIG. 2) allow aggregation of costs of all personnel for a single or multiple multi-task projects. In addition, the report 602 can include data for labor productivity for one or more multi-task projects and the labor productivity may be broken down by task and individual workers. See also Tashkin at ¶ [0079]: Tashkin notes that the workforce system 200 includes a user interface which facilitates visual representations for resource planner 310. In addition, the resource planner 310 of the workforce system 200 provides access and/or editing for level project resource planning, job orders and placements, demand entry, worker assignment, planned hours and cost by resource, approved time hours and costs, comparison of task variance versus planned metrics, and resource loading by project, job order, or individual.) based on the employment information (see at least Tashkin: ¶ [0088]. Tashkin notes that the placements 324 are able to be filled with existing employees, external sources, and new hires because the system 200 analyzes all available and possible worker options for assignment or recruitment.), the scheduling information (see at least Tashkin: ¶ [0007-0008].), and the forecasted employee requirements at a time of creating the planned budget and wherein the expected actual budget for the time period is determined using the budget tool based on the employment information, the scheduling information, and the forecasted employee requirements at a current time (see at least Tashkin: Figs. 6-7 & ¶ [0079-0080] & ¶ [0108]. Tashkin notes that the report 602 may include financial, schedule, estimate, and performance data. In addition, the report 602 may enable a user to compare estimated or budget values to actual values. Moreover, the report 602 and the system 200 (shown in FIG. 2) allow aggregation of costs of all personnel for a single or multiple multi-task projects. In addition, the report 602 can include data for labor productivity for one or more multi-task projects and the labor productivity may be broken down by task and individual workers. See also Tashkin at ¶ [0079]: Tashkin notes that the workforce system 200 includes a user interface which facilitates visual representations for resource planner 310. In addition, the resource planner 310 of the workforce system 200 provides access and/or editing for level project resource planning, job orders and placements, demand entry, worker assignment, planned hours and cost by resource, approved time hours and costs, comparison of task variance versus planned metrics, and resource loading by project, job order, or individual.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell / Tashkin non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the planned budget for the time period is determined for the time period using the budget tool based on the employment information, the scheduling information, and the forecasted employee requirements at a time of creating the planned budget, and wherein the expected actual budget for the time period is determined using the budget tool based on the employment information, the scheduling information, and the forecasted employee requirements at a current time, and in further view of Tashkin, whereby at least one of the technical problems addressed by this system may include: (i) improving availability of task assignment data that comes from multiple sources in different applications and systems; (ii) identifying workers for a multi-task project before the project begins using task specific criteria; (iii) timely identifying and providing data regarding unassigned roles in multi-task projects; (iv) providing reliable data regarding the multi-task project for workforce and task decisions; (v) enabling tracking of performance data for workers and tasks; (vi) relating the project location to locations of available workers in a manner that is simple to interpret and utilize; (vii) improving project estimates and forecasts which may be inaccurate because relevant data may not be available to estimating systems and applications; (viii) enabling accurate management and forecasting for multi-task projects which may not share data on similar tasks and a shared worker pool in an easily accessible manner (see at least Tashkin: ¶ [0046].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by Tashkin, the results of the combination were predictable.
22. Claims 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2002/0184069 A1) hereinafter Kosiba, et. al., and in view of US PG Pub (US 2022/0263703 A1) hereinafter McConnell, et. al., and in further view of US PG Pub (US 2014/0192970 A1) hereinafter Castellani, et. al.
Regarding Independent Claim 12, Kosiba / McConnell system of workforce optimization systems and methods does not explicitly disclose, but Castellani in the analogous art for workforce optimization systems and methods teaches the following limitations:
- wherein the agent suite tool is further configured to set targets and incentives to the employees (see at least Castellani: ¶ [0024] & ¶ [0028] & ¶ [0049-0051]. Castellani teaches that contextualized game design shifts the focus on performance metric targets chosen by a priori knowledge to targets chosen in view of continually updated information on call center performance and individual agent motivation. See also Castellani at ¶ [0024]. To improve the performance metrics as well as the motivation and morale of the agents, many call centers provide incentives in addition to a base salary or activity-based compensation mechanisms, which may take the form of competitions among the agents. Competitions may pit individual agents, teams, or entire call centers against each other for prizes and rewards that range from the nominal (a few extra minutes break time) to the substantial (flat screen TVs and laptops). See also Castellani at ¶ [0049-0051]. Castellani teaches that an agent's past reactions to competitions (e.g., the extent to which the agent improves one or more KPIs when given a specific KPI target. Whether or not the agent has competing goals with a proposed competition 88 (e.g., whether an improvement in one KPI is predicted to impact another KPI that the agent needs to improve to meet the KPI threshold). See also Castellani at ¶ [0074]. If low performers are the primary contributors to poor performance, a leader board competition may not be as effective as a competition that rewards all participants for achieving a set target. See also Castellani at ¶ [0126].).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the agent suite tool is further configured to set targets and incentives to the employees, and in further view of Castellani, whereby for challenging agents with a low value on a specific performance metric to lower that performance metric even more is not likely to yield significant improvements. The agents are not likely to have margins of improvement on the desired performance metric that will benefit the call center as a whole. They are also unlikely to appreciate being pushed on performance metrics for which they are already performing as expected. The exemplary system and method can yield improvements to both overall performance of call centers and individual agent motivation. This is particularly due to useful visual indications for contextual game design provided to supervisors, including: current values of correlated performance metrics, the predicted effect when correlated performance metrics are altered, and/or potential success rates of proposed competitions when considering characteristics particular to individual agents (see at least Castellani: ¶ [0025-0026].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by Castellani, the results of the combination were predictable.
Regarding Dependent Claim 14, Kosiba / McConnell / Castellani workforce optimization system teaches the limitations of Claims 1 and 12 above, and Castellani further teaches the workforce optimization system comprising:
- wherein the targets and incentives are set for individual employees or a group of employees (see at least Castellani: Fig. 10. Also see at least Castellani: ¶ [0024] & ¶ [0028] & ¶ [0049-0051]. Castellani teaches that contextualized game design shifts the focus on performance metric targets chosen by a priori knowledge to targets chosen in view of continually updated information on call center performance and individual agent motivation. See also Castellani at ¶ [0024]. To improve the performance metrics as well as the motivation and morale of the agents, many call centers provide incentives in addition to a base salary or activity-based compensation mechanisms, which may take the form of competitions among the agents. Competitions may pit individual agents, teams, or entire call centers against each other for prizes and rewards that range from the nominal (a few extra minutes break time) to the substantial (flat screen TVs and laptops). See also Castellani at ¶ [0049-0051]. Castellani teaches that an agent's past reactions to competitions (e.g., the extent to which the agent improves one or more KPIs when given a specific KPI target. Whether or not the agent has competing goals with a proposed competition 88 (e.g., whether an improvement in one KPI is predicted to impact another KPI that the agent needs to improve to meet the KPI threshold). See also Castellani at ¶ [0074]. If low performers are the primary contributors to poor performance, a leader board competition may not be as effective as a competition that rewards all participants for achieving a set target. See also Castellani at ¶ [0126].).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell / Castellani non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the targets and incentives are set for individual employees or a group of employees, and in further view of Castellani, whereby for challenging agents with a low value on a specific performance metric to lower that performance metric even more is not likely to yield significant improvements. The agents are not likely to have margins of improvement on the desired performance metric that will benefit the call center as a whole. They are also unlikely to appreciate being pushed on performance metrics for which they are already performing as expected. The exemplary system and method can yield improvements to both overall performance of call centers and individual agent motivation. This is particularly due to useful visual indications for contextual game design provided to supervisors, including: current values of correlated performance metrics, the predicted effect when correlated performance metrics are altered, and/or potential success rates of proposed competitions when considering characteristics particular to individual agents (see at least Castellani: ¶ [0025-0026].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by Castellani, the results of the combination were predictable.
23. Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2002/0184069 A1) hereinafter Kosiba, et. al., and in view of US PG Pub (US 2022/0263703 A1) hereinafter McConnell, et. al., and in further view of US PG Pub (US 2009/0271240 A1) hereinafter Kumar, et. al.
Regarding Independent Claim 15, Kosiba / McConnell system of workforce optimization systems and methods does not explicitly disclose, but Kumar in the analogous art for workforce optimization systems and methods teaches the following limitations:
- wherein the budget tool determines the expected budget (see at least Kumar: ¶ [0059-0061] & [0075]. Kumar notes that to plan and fund an outsourcing project, planner 14 will input first a plan for the project by setting forth the parameters (e.g., phases, time periods, costs, etc.) thereof. Based on the inputted plan, a cost of the outsourcing project can be estimated. Should the cost exceed the client's planned budget, planner 14 can plan for the excess. See also Kumar at ¶ [0059]: Should the cost exceed a planned budget of the client, the excess/investment is addressed while an agreement between the client and the outsourcer for the outsourcing project is being reached. This can involve reducing scope, restructuring the overlapping phases to reduce or eliminate the excess cost, and/or integrating funding options to address the cost as part of the agreement.) further based on fixed and variable cost components (see at least Kumar: ¶ [0073]. Kumar teaches that each client has signed service level agreements and penalties associated with not meeting the SLAs. Additional penalties are associated with over capacity (as a result of ambitious hiring). Both fixed and variable costs are used to evaluate the cost constraints. Productivity ramp-ups are used to model the level of expertise and measure performance of both new hires and current employees. Industry/market attrition rates for employees are also used as input to the model.), and agent productivity expectations (see at least Kumar: ¶ [abstract] & ¶ [0011] & ¶ [0038]. Kumar notes that for each process-batch-future month combination, the expected productivity in claims per hour. See also Kumar at ¶ [abstract]: “For planning a workforce headcount for a given business process. The method comprises the steps of providing as inputs, i) productivity ramp-ups to model the level of experience and to measure the performance of both new hires and current employees, and ii) industry/market attrition rates for employees.” See also Kumar at ¶ [0067].).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the budget tool determines the expected budget further base on fixed and variable cost components, and agent productivity expectations, and in further view of Kumar, whereby to analyze multiple scenarios and minimize the total workforce management cost across all scenarios. The workforce management cost is the sum of hiring cost, training cost, transition cost, excess hiring penalty cost and agent shortage penalty cost. Alternatively, minimize the unmet demand across all the processes. Also to generate, in a machine-readable data format, a constrained optimization model is used to improve the performance of the BPO company under different planning scenarios (see at least Kumar: ¶ [0040-0041].). Furthermore, the system of Kumar provides a model to enable BPOs to improve their operational efficiency and provide exceptional quality of service (see at least Kumar: ¶ [0026].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by Kumar, the results of the combination were predictable.
24. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2002/0184069 A1) hereinafter Kosiba, et. al., and in view of US PG Pub (US 2022/0263703 A1) hereinafter McConnell, et. al., and in further view of US PG Pub (US 2018/0191906 A1) hereinafter Moran, et. al.
Regarding Independent Claim 16, Kosiba / McConnell system of workforce optimization systems and methods does not explicitly disclose, but Moran in the analogous art for workforce optimization systems and methods teaches the following limitations:
- wherein the customer experience tool determines and stores current and historical service levels (see at least Moran: ¶ [0034] & ¶ [0043-044]. Moran teaches that the forecasting engine 132 may monitor current work item volume, current resource availability/utilization, past work item volume, past resource availability/utilization, estimated wait times, service levels, and other objectives and provides a forecast or estimate of the work item volume and required staffing levels in the contact center 102 for a desired shift period. For example, the forecasting engine 132 may be configured to monitor one or more Service Level Agreements (SLAs) between the contact center 102 and one or more clients or customers of the contact center 102 to ensure compliance with the applicable SLA(s). See also Moran at ¶ [0043-0044]: Attribute-based matching also allows the contact center to more accurately anticipate the volume and type of future incoming contacts and to forecast the required staffing levels. For example, the contact center may be able to accurately predict that a surge in a certain type of contact is expected during a certain portion of the workday, e.g., mid-morning through mid-afternoon.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Kosiba / McConnell non-transitory computer-readable medium of workforce optimization systems and methods with the aforementioned teachings of: wherein the customer experience tool determines and stores current and historical service levels, and in further view of Moran, whereby matching based on individual attributes ensures that each incoming contact is assigned to the most suitable resource, thereby improving first call resolution. Attribute-based matching also allows the contact center to more accurately anticipate the volume and type of future incoming contacts and to forecast the required staffing levels (see at least Moran: ¶ [0043].).
Further, the claimed invention is merely a combination of old elements in a similar field of workforce optimization systems and methods, 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, given the existing technical ability to combine the elements as evidenced by Moran, 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:
Foreign Patent Documents
WO-2016191311-A1 – Method for Determining Staffing Needs Based in Part on Sensor Inputs, hereinafter Tanaka, et. al. Tanaka teaches in [abstract] that the system includes (a) a user interface for each of a plurality of employees to each specify time periods that the employee is available for work assignment; (b) a system of sensors installed in the retail establishment to detect customer traffic.
NPL Documents
Robinson, George, and Clive Morley. "Call centre management: responsibilities and performance." International Journal of Service Industry Management 17.3 (2006): 284-300. (Year: 2006)
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/DERICK J HOLZMACHER/ Patent Examiner, Art Unit 3625A
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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