The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Notice to Applicant
In response to the communication received on 02/26/2026 the following is a Final Office Action for Application No. 18148741.
Status of Claims
Claims 1-20 are pending.
Claims 15-20 are withdrawn.
Response to Amendments
Applicant’s amendments have been fully considered.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are not persuasive:
Applicant argues that Yang in view of Hedlund fails to teach as recited in independent claim 1 (and similar claims):
optimizing the schedule for location, wherein optimizing the schedule for location further comprises: for each of the one or more agents, determining an agent performance factor, wherein the agent performance factor represents agent performance when in-in office and agent performance when remote and an agent adherence factor, wherein the agent adherence factor is generalized for both in-office and remote work. The Examiner respectfully disagrees. The BRI of this limitation includes optimizing the schedule for location comprising determining an agent performance factor which represents performance in-office and when remote and an agent adherence factor which is generalized for both in-office and remote work. Hedlund ¶0011 states that “the present invention creates an optimized workforce schedule for a set of local or remote human resources to insure optimum staff schedules based on forecasted demand, past schedules, employee skill sets, and employee preferences.” Here, Hedlund teaches optimizing the schedule for location either local and/or remote to insure optimal staff schedule based on inter alia employee performance skill sets. Hedlund ¶0040 states that “FIG. 4 is an example user interface 403 displaying schedules for a set of staff members according to an embodiment of the present invention. The interface 403 shows a plurality of schedules 405-408 for the various staff members 401 at a single location or multiple locations. Each staff member 401 can be scheduled for multiple positions 405-408 on a single day. Using templates, an operator can specify when individual staff members are available to work and when individual staff members have requested time off. For example, schedule 404 shows requested time off. When the ANN 301 recognizes patterns in past schedules, such patterns are considered when creating the initial workforce schedule.” Here, Hedlund teaches ANN 301 recognizes patterns in past schedules of an employee and each staff member can be scheduled for multiple positions on a single day and at single locations or multiple locations. Thus, ANN 301 optimizes the schedule for location using employee performance factors at various locations including local or remote. Hedlund ¶0042 states that “FIG. 6 is an example user interface 602 for selecting the positions to be scheduled according to an embodiment of the present invention. The operator/user can select a single position or multiple positions using checkboxes 601. Advantageously, the various options for single or multiple positions may also provide for a plurality of forecasted demands. For example, a first option may have a forecasted demand that can be satisfied by a single employee while a second option may have a forecasted demand that requires multiple employees.” Here, Hedlund teaches an adherence factor that considers forecasted demand and a particular combination of employees necessary to handle the location based forecasted demand which is generalized for all locations of work. See also Hedlund Figure 9 and paragraph 0045. Thus, Yang in view of Hedlund teaches the above limitation that optimizes the schedule for location.
For the reasons detailed above, Examiner is not persuaded that the claims are patentably distinguishable over the Yang in view of Hedlund disclosure. Rather, Examiner maintains that the Yang in view of Hedlund combination renders obvious the claimed invention. Accordingly, the previous prior art rejection is maintained.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 11531939 B1) hereinafter referred to as Yang in view of Hedlund et al. (US 20040267591 A1) hereinafter referred to as Hedlund.
Yang teaches:
Claim 1. A system comprising:
at least one processor; and memory storing instructions that, when executed by the at least one processor, causes the system to perform a set of operations, the set of operations comprising (C.4 L.3 A system for simultaneous shift design and shift assignment is disclosed. The system comprises an interface and a processor. The interface is configured to receive labor demand data, receive worker data, and receive scheduling configuration data. The processor is configured to: generate a set of shift candidates…):
determining one or more hybrid scheduling attributes (C.4 L.41 The scheduling input data includes labor demand data, worker data and scheduling configuration data. The labor demand data includes a worker's qualification, a number of workers needed, and a demand start time and a demand end time (i.e., what is needed, how many are needed and when). The worker data includes worker availability data, worker qualification data, worker cost data and worker personal preference data. The scheduling configuration data includes the relevant labor law (such as meal breaks, overtime pay, etc.), the relevant union contractual rules (such as minimum number of weekly hours, minimum and maximum shift length, etc.), company polices/business restrictions (such as weekly budget amount, delivery truck arrives every two weeks and such the business cycle is every two weeks, etc.), and the configuration data that drives the schedule quality (such as for every worker preference not respected in one shift X amount of penalty cost is incurred, etc.).);
receiving one or more agent's schedules (C.13 L.45 FIG. 6 is a flow diagram illustrating an embodiment of a process for schedule creation by the Scheduling Engine 106 of FIG. 1. In the example shown, in 602 almost all the scheduling input data illustrated in FIG. 3A-D (with the only exception of employee preference data) can be edited by an administrator or a manager. In 604, worker preference and availability data can be edited by individual workers. In 606, a set of shift candidates is generated as illustrated in FIG. 5.);
generating a schedule based on the one or more agent's schedules and hybrid scheduling attributes (C.5 L.63 Category One data are those used to describe the relevant labor laws that govern the meal breaks, short break, overtime pay, shift change notification time requirement and penalty cost etc. at a particular locale …FIG. 3B is a diagram illustrating an embodiment of labor laws and union contract data. In the examples shown, labor laws that govern meal break and short break in certain locales are listed in rows of a labor law table. Labor laws that govern overtime pay, shift change notification rules, and penalty payments are also listed in rows of a labor law table C.13 L.45 In 608, a set of decision variables are formed with the primary categories of decision variables being the shift candidate selection binary decision variables and the shift assignment binary decision variables. From there, control passes to 610 and 612 to build a set of constraints and an objective function respectively using the decision variables and the scheduling input data. Once the constraints and objective function are built, the solver 614 is invoked with the optimization model (e.g., the decision variables, the set of constraints and the objective function). The solver will try to find a solution that optimize the objective function (e.g., minimizing the total cost) while respecting all the given constraints.);
generating a report for the schedule; and distributing the optimized report via a network (C.14 L.13 If the schedule quality is not good enough (e.g., only 40% of workers' preferences are respected), the control is passed to 622 to adjust the corresponding penalty cost weight of a particular term in the objective function and new objective function is formed in 612 (as will be illustrated in FIG. 9 later). In the event that the schedule quality is good, the final schedule is presented to the user in 624. For any practical scheduling problem, once certain number of constraints are relaxed (such as demand coverage constraint and minimum weekly hour constraints), a feasible solution is always found.).
Although not explicitly taught by Yang, Hedlund teaches in the analogous art of system for dynamic scheduling of personnel:
optimizing, based on the one or more hybrid scheduling attributes, the schedule for best coverage and staffing for in-office operations (¶0052 Upon obtaining the employee preferences and the initial workforce schedule, the schedule is modified in step 474 based on the employee preferences. Employee preference may include such information as requested time off, requests to not be scheduled with other particular employees or managers, requests to be scheduled with other particular employees or managers, requests for certain assignments, and the like. ¶0053 Next, as shown in step 476, the forecasted demand for the particular schedule is obtained. This information may be entered by an operator or it may be fetched from volatile or persistent memory. Additionally, the forecasted demand may be calculated from historical data and refined with predictive data entered by an operator either recently or in real time. ¶0054 After the forecasted demand is obtained, in step 478 the schedule is modified based on the forecasted demand. Modification of the schedule based on the demand may include reducing the number of employees scheduled, increasing the number of employees scheduled, or modifying the scheduled employees to include more experienced employees capable of handling increased demand or modifying the scheduled employees to include less experienced employees (i.e., employees with reduced pay requirements) that are capable of more efficiently handling the forecasted demand at an appropriate level of service);
optimizing the schedule for location, wherein optimizing the schedule for location further comprises: for each of the one or more agents, determining an agent performance factor, wherein the agent performance factor represents agent performance when in-in office and agent performance when remote and an agent adherence factor, wherein the agent adherence factor is generalized for both in-office and remote work; and optimizing the schedule for location based upon the agent performance factor (¶0011 The present invention creates an optimized workforce schedule for a set of local or remote human resources to insure optimum staff schedules based on forecasted demand, past schedules, employee skill sets, and employee preferences. Upon a request to create a schedule the system uses a pattern recognition procedure to create the initial workforce schedule. The pattern recognition procedure considers staff attributes and skills as well as past schedules to create the initial workforce schedule. The initial workforce schedule is then refined via a neighborhood search algorithm that incorporates user input, employee preferences, and historical scheduling patterns to generate an optimized schedule that meets the forecasted demand for selected positions while still satisfying employee preferences ¶0027 Certain embodiments as disclosed herein provide for an improved workforce scheduling system and method that provides for the automated creation of workforce schedules based on employee availability, preferences, skill sets, past performance, and staffing requirements such as forecasted demand for certain positions ¶0040 FIG. 4 is an example user interface 403 displaying schedules for a set of staff members according to an embodiment of the present invention. The interface 403 shows a plurality of schedules 405-408 for the various staff members 401 at a single location or multiple locations. Each staff member 401 can be scheduled for multiple positions 405-408 on a single day. Using templates, an operator can specify when individual staff members are available to work and when individual staff members have requested time off. For example, schedule 404 shows requested time off. When the ANN 301 recognizes patterns in past schedules, such patterns are considered when creating the initial workforce schedule ¶0042 FIG. 6 is an example user interface 602 for selecting the positions to be scheduled according to an embodiment of the present invention. The operator/user can select a single position or multiple positions using checkboxes 601. Advantageously, the various options for single or multiple positions may also provide for a plurality of forecasted demands. For example, a first option may have a forecasted demand that can be satisfied by a single employee while a second option may have a forecasted demand that requires multiple employees).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for dynamic scheduling of personnel of Hedlund with the system for shift design and assignment of Yang for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Yang C.1 L.5 teaches that it is desirable to efficiently schedule workers to cover demand at a business;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Yang Abstract teaches A system for shift design and assignment comprises an interface configured to receive scheduling input data which includes labor demand data, worker data, and scheduling configuration data and determine a final schedule, and Hedlund Abstract teaches a method and system for creating an optimized workforce schedule for a set of local or remote human resources to insure optimum staff schedules; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Yang at least the above cited paragraphs, and Hedlund at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the system for dynamic scheduling of personnel of Hedlund with the system for shift design and assignment of Yang. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Yang teaches:
Claim 2. The system of claim 1, further comprising: determining that the schedule satisfies the minimum schedule requirements (C.6 L.39 In the rest of the steps of scheduling engine's processing, it will select the most effective subset of the shift candidates to use in the final schedule and to assign appropriate workers to staff the shifts in such as a way that all relevant constraints are respected and the objective function (of the total cost, including the penalty costs) is minimized.).
Yang teaches:
Claim 3. The system of claim 1, further comprising: reporting an optimized schedule (C.14 L.18 In the event that the schedule quality is good, the final schedule is presented to the user in 624. For any practical scheduling problem, once certain number of constraints are relaxed (such as demand coverage constraint and minimum weekly hour constraints), a feasible solution is always found.).
Yang teaches:
Claim 4. The system of claim 3, further comprising: adjusting the optimized schedule based on post-scheduling rules (C.17 L.35 FIG. 9 is a diagram illustrating an embodiment of a user interface for adjusting relative importance of certain penalty cost terms in the objective function to achieve the desired schedule quality as indicated in step 622 of FIG. 6. In the example shown, the objective function used by the solver to find an optimal solution may contain information along many dimensions in addition to the actual schedule cost.).
Yang teaches:
Claim 5. The system of claim 1, wherein the hybrid scheduling attributes comprise agent-defined attributes and enterprise-defined attributes (Figs. 3A-D and C.11 L.47 FIG. 3C is a diagram illustrating an embodiment of company policy and operation conditions that impact scheduling. In the example shown, a table includes rows describing a company policy for rest days, a budget limit, and an organization cycle (e.g., a schedule repeat pattern—for example, every two weeks). The table includes data that can impact what constraints should be enforced and how. FIG. 3D is a diagram illustrating an embodiment of penalty cost configurations that are used to influence the schedule quality. In the example shown, a table includes rows describing penalty costs associated with schedule consistency, a worker's timing preference, a worker's total weekly hours' preference, a worker's role preference, and leaving demand uncovered. By varying the penalty costs, different shapes of the final schedule can be obtained from the solver.).
Yang teaches:
Claim 6. The system of claim 5, wherein agent-defined attributes further comprise which day or days the agent prefers to work from a remote location and in-office, status of the agent's role within the business, what time the agent prefers to work, regular schedule conflicts that the agent prefers to work around, what shift the agent prefers to work, and other miscellaneous agent constraints (C.14 L.55 FIG. 3D is a diagram illustrating an embodiment of penalty cost configurations that are used to influence the schedule quality. In the example shown, a table includes rows describing penalty costs associated with schedule consistency, a worker's timing preference, a worker's total weekly hours' preference, a worker's role preference, and leaving demand uncovered. By varying the penalty costs, different shapes of the final schedule can be obtained from the solver.).
Yang teaches:
Claim 7. The system of claim 5, wherein enterprise-defined attributes further comprise enterprise-defined attributes relating to enterprise specific standard operating procedures, office requirements, and other internal metrics defined by the enterprise (C.7 L.17 A generated shift candidate is subject to labor law legal constraints, union contract constraints, company policy constraints, etc. For example, a union contract can dictate that the duration of a shift has to be between four and nine hours, or a state law can require a half hour unpaid meal break for every 5 hours consecutive work. When a shift candidate that requires a meal break is generated, the exact timing of the meal break is placed on the shift. If alternative placements of the meal break are allowed by the law, each alternative is generated as an alternative shift candidate. The later optimization process will select the right one to use.).
As per claims 8-14, the method tracks the system of claims 1-7, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-7 are applied to claims 8-14, respectively.
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KURTIS GILLS whose telephone number is (571) 270-3315. The examiner can normally be reached on M-F, 8am-5pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KURTIS GILLS/Primary Examiner, Art Unit 3624
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