DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office Action for Application Serial Number: 8/231,142, filed on August 07, 2023. In response to Examiner's Non-Final Office Action dated December 15, 2025, Applicant on March 16, 2026, amended claims 1, 11 and 21. Claims 1-5, 7-15 and 17-21 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to Applicants amendments to claims 1, 11 and 21.
Response to Arguments
Applicant's Arguments/Remarks filed March 16, 2026 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks will be addressed herein below in the order in which they appear in the response filed March 16, 2026.
Regarding the 35 U.S.C. 101 rejection, Applicant states Without acquiescing in the rejection and to expedite prosecution, the independent claims are amended based on the features of the independent claims of the parent application that were identified in the Interview Summary as being the features that made those independent claims patent-eligible under 35 U.S.C. § 101. For at least these reasons, the amended independent claims, and the claims that depend thereon, are patent-eligible under 35 U.S.C. § 101.
In response, Examiner respectfully disagrees. In the Interview Summary following the interview of March 05, 2026, Examiner explicitly stated “Examiner suggest amending the claim to include the following limitations from the parent claim "generating a schedule summary displaying one or more selectable portions of each block of availability, wherein each schedule for each driver of the drivers comprises one or more selectable portions for each block of availability submitted by each driver via a respective driver device, wherein each schedule for each driver of the one or more schedules for the drivers is displayed as a row with a unique driver identification label identifying each driver, and wherein: the one or more selectable portions of each block of availability on the schedule summary not scheduled are labeled as unfilled portions; and the one or more selectable portions of each block of availability on the schedule summary as scheduled are labeled as filled portions; selecting an unfilled portion of each schedule for a respective driver of the drivers adds the respective driver to a list of respective drivers to be scheduled on the schedule summary, wherein each selection causes one or more respective details of the respective driver to be displayed on a graphical user interface (GUI) of an electronic device of a user; receiving one or more selections of the one or more selectable portions of each block of availability from the user, wherein each of the one or more selections selection causes one or more changes to occur on the schedule summary as displayed on the GUI of the electronic device of the user; and coordinating displaying the one or more schedules for the drivers on the electronic device of the user”. With the reason stating the ordered combination of the above claim limitations would be sufficient in overcoming the current 101.
Upon review of the amended claims, Examiner finds Applicant has amended each independent claim differently, with variations of the limitations Examiner suggested would overcome the pending 35 U.S.C. 101 rejection. Examiner notes none of the claims were amended to explicitly include the suggested claim language presented in the summary. Please see the 35 U.S.C. 101 Rejection below for an updated analysis and rationale.
Regarding the 35 U.S.C. 101 rejection, Applicant states even assuming the amended independent claims can reasonably be determined to recite an abstract idea under Prong One of Step 2A, which Applicant does not concede, Applicant respectfully asserts that the claims integrate the alleged abstract idea into a practical application under Prong Two of Step 2A.
Paragraph 3 of the specification describes that conventional systems for computer assistant scheduling resulted in problems with 'stale schedules that can be further complicated when an optimization is performed on a mobile device with a less powerful processor. Paragraphs 50, 69, and 74 of the specification describe a technical solution "allowing an optimization process to be run on a mobile device with reduced ... processing power or a smaller portion of a system's cloud computing infrastructure" by using a schedule optimization algorithm for "optimization of large datasets (e.g., updating or re-optimizing a schedule with one or more new drivers) ... using minimal computational power and at a reasonable speed.
Thus, as presented in the previous response, the specification explicitly discusses a technical solution that reduces the processing power required for computer assisted scheduling, which improves the functioning of a computer used for computer assisted scheduling.
Representative amended independent claim 1 further reflects the disclosed improvements in the functioning of the claimed computer system by transmitting "generat[ing] schedules for the multiple drivers by performing, a specified periodicity after a previous optimization, optimization based on a schedule optimization algorithm that allows the schedules to be generated in a batch for the multiple drivers, based on a previous schedule generated during the previous optimization, and based on a selection, via the GUI of the electronic device, of a final assignment of two potential assignments for the driver" (emphasis added). First, by generating "based on a schedule optimization algorithm that allows the schedules to be generated in a batch," the computer system requires substantially less processor resources relative to a total amount of processing resources that would be required by a conventional computer system to generate each schedule individually. Second, by generating "based on a previous schedule generated during the previous optimization," the computer system requires substantially less processing resources relative to a total amount of processing resources that would be required by a conventional computer system to generate a schedule from fresh each time without the benefit of referencing the previously generated schedule. This reduction in the processing resources that are required allows the claimed optimization process to be run on a computing system with reduced processing power, as discussed in the portions of the specification cited above.
In response, Examiner respectfully disagrees. Examiner finds computer assisted scheduling of drivers using an optimization algorithm previous schedules and assignment selections, as claimed, is merely an automation of a manual process. Examiner respectfully reminds Applicant claims are evaluated to ensure that the claim itself reflects the disclosed improvement; MPEP 21060.04(d)(1). Examiner finds the pending claims do not reflect how this arrangement provides technical improvements in reduces the processing power as Applicant describes. For instance, paragraph 50, discloses various optimization logics and a comparison in execution times, however the pending claims do not make clear what optimization technique is being implemented to carry out the functions of the claim. As stated in the previous Office Action and as presented, the optimization of driver schedules is not an improvement to a technology, technological field or computer-related technology. Examiner finds generating schedules for the multiple drivers by performing, a specified periodicity after a previous optimization, optimization based on a schedule optimization algorithm that allows the schedules to be generated in a batch for the multiple drivers, based on a previous schedule generated during the previous optimization, and based on a selection of a final assignment of two potential assignments for the driver recites mathematic concepts. Specifically, mathematical relationships directed to organizing information and manipulating information through mathematical correlations; see MPEP 2106.04(a)(2)(A), mathematical formulas or equations; See MPEP2106.04(a)(2)(B), and mathematical calculations; See MPEP 2106.4(a)(2)(C). Therefore, Examiner finds algorithm involved here has no substantial practical application except in connection with a digital computer and thus maintains the claim is directed to an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states the Response to Arguments section does not explain why this reduction in processing power is not analogous to the technical improvement recognized in Ex Parte Desjardins, simply stating that there are "no similar improvements to take into consideration here". Applicant respectfully submits that the same principle from Ex Parte Desjardins applies here.
Ex Parte Desjardins identifies "us[e] less of their storage capacity" as an improvement to computer functionality, due to an improvement in a process, that demonstrates integration into a practical application under Step 2A, Prong Two based on claim limitations of a process (emphasis added). Similarly, paragraphs 50, 69, and 74 of the specification of this application explicitly discuss how the claimed invention provides technical improvements by requiring the computer system to use reduced "processing power." The pending Office Action does not provide any explanation regarding why these are not similar technical improvements.
Therefore, for at least some of the reasons discussed above, the independent claims, and the claims that depend thereon, integrate the alleged abstract idea into a practical application and are patent-eligible under 35 U.S.C. § 101.
Accordingly, Applicant respectfully requests that the Examiner reconsider and withdraw this rejection.
In response, Examiner respectfully disagrees and maintains the pending claims are not similar to the improvements in Desjardins. As stated in the previous Office Action, in regards to the recent PTAB decision, Examiner notes this particular circumstance vacated the PTAB’s new ground of rejection under 35 U.S.C. 101 when evaluating eligibility related to machine learning or artificial intelligence. Specifically, in Desjardins, the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”, and that the claims reflected the improvement identified in the specification. The improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. Examiner finds no similar improvements to take into consideration here. Examiner finds the optimization algorithm presented in the claims is not currently limited to a machine learning technology. As presented, the current optimization algorithm is used to organize and manipulate information through mathematical correlations. Even if the optimization algorithm was limited to a machine learning technology, Examiner finds there is currently no improvement to how the optimization algorithm functions. Thus, Examiner maintains the claims are directed to an abstract idea. For at least these reasons, the pending claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
9. Regarding the 35 U.S.C. 103 rejection, Applicant states the cited sections of YANG recite that "[s]cheduling engine 106 manages the scheduling input data using the transition engine 108 to persist data, generates a set of shift candidates based at least in part on the labor demand, creates an optimal schedule by invoking a MIP solver, produces an incremental solution if a portion of the scheduling input data is changed." Merely "[c]reates an optimal schedule by invoking a MIP solver" does not disclose or suggest generating "schedules for the multiple drivers by performing, a specified periodicity after a previous optimization, optimization based on a schedule optimization algorithm that allows the schedules to be generated in a batch for the multiple drivers, based on a previous schedule generated during the previous optimization," let also doing it "based on a selection, via the GUI of the electronic device, of a final assignment of two potential assignments for the driver," as recited in amended claim 1 (emphasis added).
Accordingly, Applicant respectfully requests that the Examiner reconsider and withdraw this rejection.
In response, Examiner respectfully disagrees. First, Examiner notes if the prior art structure is capable of performing the intended use, then it meets the claim. Yang discloses, in part, an optimization model is invoked in warm start mode, e.g., the base solution from the previous optimal schedule is used to generate a new solution (see, [col. 19, ln. 37-42]), the solver comprises an MIP solver, and wherein the MIP solver determines simultaneously a subset of the shift candidates selected in the final schedule and a set of shift assignments of which worker is assigned to which selected shift candidate of the subset of shift candidates such that the hard constraints are fully respected, violations to the soft constraints are minimized, and the new cost function is minimized (see, Yang, [col. 2, ln. 63]-[col. 3, ln. 2]) and the final optimal solution also select 1 shift candidate from 9:00am to 5:00pm and 1 shift candidate from 1:00pm to 5:00pm. At the time of generating the shift candidates, it is more relevant to choose possible candidates. It is the later optimization process that will decide which subset gets selected in order the get the optimal solution. In some embodiments, the heuristic generates shift candidates based on the availability of types of workers (e.g., only full time workers are available so only full time shifts are generated, only part time workers are available so only part time shifts are generated, etc.) (see Yang, [col. 6, ln. 28-38]). Examiner finds Yang sufficiently teaches the aforementioned limitation because the reference discloses using a MIP solver to determine an optimal schedule in terms of what shifts the final schedule should have and which worker is assigned to which shift, with the use of previous optimal schedule to generate the new solution. For at least these reasons, the pending claims remain rejected under 35 U.S.C. § 103 as being unpatentable over the prior art of record.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-5 and 7-10 are directed towards a system, claims 11-15 and 17-20 are directed towards a method, and claim 21 is directed towards a non-transitory computer-readable media, which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-5, 7-15 and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite scheduling available drivers.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity, mathematical concepts and mental processes. Specifically, generate schedules for the multiple drivers by performing, a specified periodicity after a previous optimization, optimization based on a schedule optimization algorithm that allows the schedules to be generated in a batch for the multiple drivers, based on a previous schedule generated during the previous optimization, and based on a selection of a final assignment of two potential assignments for the driver constitutes methods based on managing personal behavior or relationships between people and commercial interactions, mathematical relationships, formulas or equations and calculations, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of non-transitory computer-readable media storing instructions executable by processor and a GUI of an electronic device does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea.
Claim 11 recites limitations directed to an abstract idea based on certain methods of organizing human activity mathematical concepts and mental processes. Specifically, generating a schedule summary comprising a plurality of selectable portions of availability for drivers, wherein the schedule summary comprises, for a driver of the drivers, a unique driver identification label, and wherein the plurality of selectable portions of availability comprises one or more unfilled portions and one or more filled portions; receiving a selection of an unfilled portion, of the one or more unfilled portions, for the driver, wherein the selection of the unfilled portion is configured to cause information associated with the driver to be displayed; generate schedules for multiple drivers, of the drivers, by performing, a specified periodicity after a previous optimization, optimization based on a schedule optimization algorithm that allows the schedules to be generated in a batch for the multiple driver, based on a previous schedule generated during the previous optimization, and based on a selection of a final assignment of two potential assignments for the driver; and coordinate displaying one or more schedules, of the schedules, for one or more drivers, of the multiple drivers, based on based on generating the schedules for the multiple drivers constitutes methods based on managing personal behavior or relationships between people and commercial interactions, mathematical relationships, formulas or equations and calculations, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of processors executing computing instructions configured to be stored at non-transitory computer-readable media does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea.
Claim 21 recites limitations directed to an abstract idea based on certain methods of organizing human activity, mathematical concepts and mental processes. Specifically, generate, a schedule summary displaying a plurality of selectable portions of availability, wherein the schedule summary comprises schedules for drivers, wherein a schedule, of the schedules, comprises a plurality of selectable portions for a block of availability submitted by a driver, of the drivers, via a driver device, and wherein the plurality of selectable portions comprise: one or more unfilled portions that are not scheduled, and one or more filled portions that are scheduled; receive, a selection of a selectable portion, of the plurality of selectable portions, for the driver, generate optimized schedules for multiple drivers, of the drivers, by performing optimization based on a selection of a final assignment of two potential assignments for the driver, wherein the multiple drivers include the driver; and coordinate displaying one or more optimized schedules, of the optimized schedules, for one or more drivers, of the multiple drivers, based on generating the optimized schedules for the multiple drivers constitutes methods based on managing personal behavior or relationships between people and commercial interactions, mathematical relationships, formulas or equations and calculations, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of non-transitory computer-readable media storing instructions executable by processor and a GUI of an electronic device does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites generate, on a graphical user interface (GUI) of an electronic device, a schedule summary comprising a plurality of selectable portions of availability for drivers , wherein the schedule summary comprises, for a driver of the drivers, a schedule as a row with a unique driver identification label, and wherein the plurality of selectable portions of availability comprises one or more first selectable portions labeled as unfilled portions and one or more second selectable portions labeled as filled portions; receive, via the GUI of the electronic device, a selection of an unfilled portion, of the one or more first selectable portions labeled as unfilled portions, for the driver; add, based on receiving, via the GUI of the electronic device, the selection of the unfilled portion, information indicating the driver to information regarding multiple drivers, of the drivers, to be scheduled, wherein the multiple drivers include the driver, and wherein the selection of the unfilled portion is configured to cause the information indicating the driver to be displayed on the GUI of the electronic device, which merely confines the abstract idea to a particular technological environment or field of use; see MPEP 2106.05(h). Claim 1 also recites coordinate displaying, via the electronic device, one or more schedules, of the schedules, for one or more drivers, of the multiple drivers, based on generating the schedules for the multiple drivers, which are limitations considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 1 recite a system comprising processors and non-transitory computer-readable media storing computing instructions executable by the processors and GUI of an electronic device at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea.
Claim 11 recites processors executing computing instructions configured to be stored at non-transitory computer-readable media at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Thus, the additional elements do not integrate the abstract idea into practical application because it does not impose any meaningful limitations on practicing the abstract idea. Claim 11 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The non-transitory computer-readable media storing instructions executable by processor and a GUI of an electronic device recited in claim 21 also amount to no more than mere instructions to apply the exception using a generic computer component; see MPEP 2106.05(f). Thus, the additional elements recited in claim 21 do not integrate the abstract idea into practical application for similar reasons as claim 11.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including a system comprising processors and non-transitory computer-readable media storing computing instructions executable by the processors and GUI of an electronic device amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, 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); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and 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, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 2, 3, 12 and 13 recites receiving and displaying elements respectively, which are limitations considered to be insignificant extra-solution activities of collecting and delivering data; see MPEP 2106.05(g) and does not integrate the abstract idea into practical application. Claims 2, 3, 12 and 13 recite additional elements at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; MPEP 2106.05(f). Additionally, claims 2-4, 7-10, 12-14 and 17-20 recite steps that further narrow the abstract idea constituting methods based on organizing human activities and mental processes. Claims 5 and 15 are directed to an abstract idea based on mathematical concepts, specifically mathematical formulas and equations. Therefore claims 2-5, 7-10, 12-15 and 17-20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 11, 12, 14 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Hunter et al., U.S. Publication No. 2022/0092521 [hereinafter Hunter], and further in view of Yang, U.S. Patent No. 11,763,220 [hereinafter Yang].
Referring to Claim 1, Hunter teaches:
A system for reducing processing power required for optimization, comprising:
one or more processors (Hunter, [0121]; [0315]-[0316]); and
one or more non-transitory computer-readable media storing instructions that, when executed on the one or more processors (Hunter, [0061]; [0316]), cause the one or more processors to:
generate, on a graphical user interface (GUI) of an electronic device, a schedule summary comprising a plurality of selectable portions of availability for drivers (Hunter, Figures 39 and 41-46), “Scheduling interface with selectable timeslots of availability”; (Hunter, [0129]), “Memory 110 also includes a user interface generator 120, configured to dynamically generate, modify, and/or update graphical user interfaces that present a variety of data. For example, user interface generator 120 may generate user interfaces for entering or modifying information regarding merchants and workers (e.g., deliverers), configuring fleets, processing delivery tasks, scheduling… user interface generator 120 may generate dynamic and interactive user interfaces that may be presented on a user device, such as user device 134. Various user interfaces generated by user interface generator 120”; (Hunter, [0128]), “Scheduling management tool 116 can also manage and/or partially automate the process of scheduling workers for shifts or tasks. In some embodiments, a worker or a supervising user (e.g., a manager) may be able to configure availability parameters for a worker, which define when the worker is available to work”; (Hunter, [0124]; [0116]),
wherein the schedule summary comprises, for a driver of the drivers, a schedule as a row with a unique driver identification label (Hunter, Fig. 27, [0202]), “the schedule includes a row for each worker, and columns for each day. A user may select a particular range of dates to view, and may be presented with event blocks that indicate an assigned shift or other event for a given day”, and
wherein the plurality of selectable portions of availability comprises one or more first selectable portions labeled as unfilled portions and one or more second selectable portions labeled as filled portions (Hunter, [0254]), “multiple open shifts may be represented by a single shift tile, shown as open shifts tile 4104, which may also be color-coded or otherwise differentiated (e.g., via shade, pattern, size, etc.) from the other shift tiles. In this example, open shifts tile 4104 is a neutral-colored tile (e.g., grey) which can, in some embodiments, also indicate the position types associated with the open shift”; (Hunter, [0253]), “… open shift tile 4102 is color-coded or otherwise differentiated (e.g., shade, pattern, size, etc.) from the other shift tiles based on the position type… or based on any other parameter. In this example, open shift tile 4102 is colored red based on the position type to match the color of other shift tiles”; (Hunter, Figures 39 and 41-46), “Scheduling interface with scheduled and unscheduled blocks” (Hunter, [0264]);
receive, via the GUI of the electronic device, a selection of an unfilled portion, of the one or more first selectable portions labeled as unfilled portions, for the driver (Hunter, [0246]-[0247]), “FIGS. 39-55, example interfaces for generating, manipulating, and/or assigning open shifts are shown, according to various embodiments. An open shift is a block of time (i.e., a time period) that is not assigned to a particular worker (e.g., a driver). In other words, an open shift is a period of time that is available for assignment to a worker or for a worker to select…open shift can be automatically assigned or a manager can choose to moderate which workers can select open shifts… Any of the example interfaces described herein may be generated by user interface generator 120, for example, as described in detail above. More generally, system 100 may be configured to perform any of the functions and/or generate any of the interfaces described herein, although it will be appreciated that in some other embodiments, other systems, devices, etc. can perform the functions described below”; (Hunter, Fig. 52, [0269]), “…if workers have applied for the open shift, interface 5200 may include a list of eligible workers…”; (Hunter, [0124]; [0146]; [0260]);
add, based on receiving, via the GUI of the electronic device, the selection of the unfilled portion, information indicating the driver to information regarding multiple drivers, of the drivers, to be scheduled, wherein the multiple drivers include the driver (Hunter, Fig. 6, Item 616; [146]), “assigning drivers to fleets”; (Hunter, [0201]-[0202]; [0133]), and
wherein the selection of the unfilled portion is configured to cause the information indicating the driver to be displayed on the GUI of the electronic device (Hunter, Fig. 44, [0257]- [0258]), “…in FIG. 44 the user has selected multiple open shift tile 4404 and has dragged tile 4404 to an open block on the following day. In some embodiments, the user can drag-and-drop open shift tiles from the “Open Shifts” row to the schedules of individual workers. For example, the user has selected an open “Accounting Clerk” tile 4406 and has dropped tile 4406 in the schedule of Jennifer Smith, thereby assigning that shift to Jennifer… the user can drag-and-drop tiles within the same row of schedule 3904. For example, in FIG. 44 the user has selected multiple open shift tile 4404 and has dragged tile 4404 to an open block on the following day. In some embodiments, the user can drag-and-drop open shift tiles from the “Open Shifts” row to the schedules of individual workers. For example, the user has selected an open “Accounting Clerk” tile 4406 and has dropped tile 4406 in the schedule of Jennifer Smith, thereby assigning that shift to Jennifer”; (Hunter, [0204]); and
coordinate displaying, via the electronic device, one or more schedules, of the schedules, for one or more drivers, of the multiple drivers, based on generating the schedules for the multiple drivers (Hunter, [0259]), “a manager may be able to view all of the assigned shifts and/or may be able to publish certain shifts (e.g., the manager may publish all shifts for a one or two-week period)”; (Hunter, Fig. 27, [0091]), “interface for viewing and modifying a schedule”; (Hunter, [0202]), “interface 2700 may present a high-level overview of the complete schedule for a company or merchant. As shown, the schedule includes a row for each worker, and columns for each day. A user may select a particular range of dates to view, and may be presented with event blocks that indicate an assigned shift or other event for a given day”.
Hunter teaches a delivery management system that can collect and analyze disparate delivery and driver data in near real-time and determine optimized delivery solutions across multiple merchants and multiple delivery fleets (see par. 0115) and the automatically generated schedule may be optimized to ensure that an adequate number of workers, with appropriate capabilities (e.g., position assignments), are scheduled for each shift (see par. 0178), but Hunter does not explicitly teach:
generate schedules for the multiple drivers by performing, a specified periodicity after a previous optimization, optimization based on a schedule optimization algorithm that allows the schedules to be generated in a batch for the multiple drivers, based on a previous schedule generated during the previous optimization and based on a selection, via the GUI of the electronic device, of a final assignment of two potential assignments for the driver.
However Yang teaches:
generate schedules for the multiple drivers by performing, a specified periodicity after a previous optimization, optimization based on a schedule optimization algorithm that allows the schedules to be generated in a batch for the multiple drivers, based on a previous schedule generated during the previous optimization and based on a selection, via the GUI of the electronic device, of a final assignment of two potential assignments for the driver (Yang, Abstract), “wherein the solver comprises a mixed integer programming (MIP) solver, and wherein the MIP solver determines simultaneously a subset of the shift candidates selected in the final schedule and a set of shift assignments of which worker is assigned to which selected shift candidate of the subset of shift candidates”; (Yang, [col. 19, ln. 37-42]), “the optimization model is updated with the new objective function which include the schedule disruption penalty cost term. Then the control is passed onto 1012 where a MP solver is invoked in warm start mode, e.g., the base solution from the previous optimal schedule is used to generate a new solution”; (Yang, [col. 2, ln. 63]-[col. 3, ln. 2]), “wherein the solver comprises an MIP solver, and wherein the MIP solver determines simultaneously a subset of the shift candidates selected in the final schedule and a set of shift assignments of which worker is assigned to which selected shift candidate of the subset of shift candidates such that the hard constraints are fully respected, violations to the soft constraints are minimized, and the new cost function is minimized”; (Yang, [col. 6, ln. 28-38]), “The final optimal solution also select 1 shift candidate from 9:00am to 5:00pm and 1 shift candidate from 1:00pm to 5:00pm. At the time of generating the shift candidates, it is more relevant to choose possible candidates. It is the later optimization process that will decide which subset gets selected in order the get the optimal solution. In some embodiments, the heuristic generates shift candidates based on the availability of types of workers (e.g., only full time workers are available so only full time shifts are generated, only part time workers are available so only part time shifts are generated, etc.)”; (Yang, [col. 7, ln. 10-42]), “…Two categories of decision variables are constructed that correspond to the two types decisions that are being made simultaneously, i.e., which subset of the shift candidates should be used in the final optimal schedule and which worker should be assigned to which shift…”; (Yang, [col. 20, ln. 49-62]), “a set of decision variables is determined representing whether a particular shift candidate is selected in a final schedule and whether a particular worker is assigned to the particular shift candidate. For example, the set of decision variables includes which subset of the shift candidates should be used in the final optimal schedule and which worker should be assigned to which shift. In some embodiments, for a worker i of the set of N workers and a shift candidate j of the set of M shift candidates … ”; (Yang, [col. 11, ln. 7-12]), “Scheduling engine 106 manages the scheduling input data using the transition engine 108 to persist data, generates a set of shift candidates based at least in part on the labor demand, creates an optimal schedule by invoking a MIP solver, produces an incremental solution if a portion of the scheduling input data is changed”; (Yang, [col. 10, ln. 12-41]; [col. 22, ln. 1-16]; [col. 24, ln. 6-5]), Examiner notes “Mere duplication of parts has no patentable significance unless a new and unexpected result is produced”; see MPEP 2144.04.32.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimized delivery solutions in Hunter to include the schedule limitations as taught by Yang. The motivation for doing this would have been to improve the method of a process for integrated scheduling and delivery management in Hunter (see par. 0161) to efficiently include the results of an effective way to obtain an incremental solution without causing large disruption to the current schedule while using the efficient warm start of a MIP solver to solve the incremental scheduling problem (see Yang col. 3, ln. 3-6).
Referring to Claim 2, Hunter in view of Yang teaches the system of claim 1. Hunter further teaches:
wherein to generate the schedule summary , the instructions cause the one or more processors to:
receive from respective driver devices of the drivers, a respective availability of each of the drivers, wherein the respective availability comprises a respective time, a respective date, and one or more respective constraints (Hunter, [0128]), “a worker or a supervising user (e.g., a manager) may be able to configure availability parameters for a worker, which define when the worker is available to work. For example, a first worker may indicate that they are only available from Monday through Friday, from 9am to 5pm each day, while a second worker may indicate that they are available 24/7…Scheduling management tool 116 may be configured to generate a declaration for a worker, including deliverers or drivers based on a number of factors (e.g., fleet assignment, vehicle type, job assignment, task assignment)”; (Hunter, [0133]), “A deliverer data set may include attributes such as hourly wage or compensation, availability, skills or certifications, transportation identifier, delivery boundary area”; (Hunter, [0137]; [0169]-[0170]).
Referring to Claim 4, Hunter in view of Yang teaches the system of claim 1. Hunter further teaches:
wherein: one or more first selectable portions, of the plurality of selectable operations, of each block of availability on the schedule summary not scheduled are labeled as unfilled portions, the unfilled portions includes the unfilled portion, and one or more second selectable portions, of the plurality of selectable portions of each block of availability on the schedule summary as scheduled are labeled as filled portions (Hunter, [0254]), “multiple open shifts may be represented by a single shift tile, shown as open shifts tile 4104, which may also be color-coded or otherwise differentiated (e.g., via shade, pattern, size, etc.) from the other shift tiles. In this example, open shifts tile 4104 is a neutral-colored tile (e.g., grey) which can, in some embodiments, also indicate the position types associated with the open shift”; (Hunter, [0253]), “… open shift tile 4102 is color-coded or otherwise differentiated (e.g., shade, pattern, size, etc.) from the other shift tiles based on the position type… or based on any other parameter. In this example, open shift tile 4102 is colored red based on the position type to match the color of other shift tiles”; (Hunter, [0249]), “assigned shifts are represented as tiles indicating a position associated with the shift… and a start/stop time for the shift… Interface 3900 may also indicate open shifts, which are displayed along the top row of schedule 3904 in this example”; (Hunter, Figures 39 and 41-46), “Scheduling interface with scheduled and unscheduled blocks” (Hunter, [0264]).
Referring to Claim 11, Hunter teaches:
A method implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media (Hunter, [0121]; [0315]-[0316]), the method comprising:
generating a schedule summary comprising a plurality of selectable portions of availability for drivers (Hunter, Figures 39 and 41-46), “Scheduling interface with selectable timeslots of availability”; (Hunter, [0129]), “Memory 110 also includes a user interface generator 120, configured to dynamically generate, modify, and/or update graphical user interfaces that present a variety of data. For example, user interface generator 120 may generate user interfaces for entering or modifying information regarding merchants and workers (e.g., deliverers), configuring fleets, processing delivery tasks, scheduling… user interface generator 120 may generate dynamic and interactive user interfaces that may be presented on a user device, such as user device 134. Various user interfaces generated by user interface generator 120”; (Hunter, [0128]), “Scheduling management tool 116 can also manage and/or partially automate the process of scheduling workers for shifts or tasks. In some embodiments, a worker or a supervising user (e.g., a manager) may be able to configure availability parameters for a worker, which define when the worker is available to work”; (Hunter, [0124]; [0116]),
wherein the schedule summary comprises, for a driver of the drivers, a unique driver identification label (Hunter, Fig. 27, [0202]), “the schedule includes a row for each worker, and columns for each day. A user may select a particular range of dates to view, and may be presented with event blocks that indicate an assigned shift or other event for a given day”, and
wherein the plurality of selectable portions of availability comprises one or more unfilled portions and one or more filled portions (Hunter, [0254]), “multiple open shifts may be represented by a single shift tile, shown as open shifts tile 4104, which may also be color-coded or otherwise differentiated (e.g., via shade, pattern, size, etc.) from the other shift tiles. In this example, open shifts tile 4104 is a neutral-colored tile (e.g., grey) which can, in some embodiments, also indicate the position types associated with the open shift”; (Hunter, [0253]), “… open shift tile 4102 is color-coded or otherwise differentiated (e.g., shade, pattern, size, etc.) from the other shift tiles based on the position type… or based on any other parameter. In this example, open shift tile 4102 is colored red based on the position type to match the color of other shift tiles”; (Hunter, Figures 39 and 41-46), “Scheduling interface with scheduled and unscheduled blocks” (Hunter, [0264]);
receiving a selection of an unfilled portion, of the one or more unfilled portions, for the driver (Hunter, [0246]-[0247]), “FIGS. 39-55, example interfaces for generating, manipulating, and/or assigning open shifts are shown, according to various embodiments. An open shift is a block of time (i.e., a time period) that is not assigned to a particular worker (e.g., a driver). In other words, an open shift is a period of time that is available for assignment to a worker or for a worker to select…open shift can be automatically assigned or a manager can choose to moderate which workers can select open shifts… Any of the example interfaces described herein may be generated by user interface generator 120, for example, as described in detail above. More generally, system 100 may be configured to perform any of the functions and/or generate any of the interfaces described herein, although it will be appreciated that in some other embodiments, other systems, devices, etc. can perform the functions described below”; (Hunter, Fig. 52, [0269]), “…if workers have applied for the open shift, interface 5200 may include a list of eligible workers…”; (Hunter, [0124]; [0146]; [0260]),
wherein the selection of the unfilled portion is configured to cause information associated with the driver to be displayed (Hunter, Fig. 44, [0257]- [0258]), “…in FIG. 44 the user has selected multiple open shift tile 4404 and has dragged tile 4404 to an open block on the following day. In some embodiments, the user can drag-and-drop open shift tiles from the “Open Shifts” row to the schedules of individual workers. For example, the user has selected an open “Accounting Clerk” tile 4406 and has dropped tile 4406 in the schedule of Jennifer Smith, thereby assigning that shift to Jennifer… the user can drag-and-drop tiles within the same row of schedule 3904. For example, in FIG. 44 the user has selected multiple open shift tile 4404 and has dragged tile 4404 to an open block on the following day. In some embodiments, the user can drag-and-drop open shift tiles from the “Open Shifts” row to the schedules of individual workers. For example, the user has selected an open “Accounting Clerk” tile 4406 and has dropped tile 4406 in the schedule of Jennifer Smith, thereby assigning that shift to Jennifer”; (Hunter, [0204]); and
coordinate displaying one or more schedules, of the schedules, for one or more drivers, of the multiple drivers, based on based on generating the schedules for the multiple drivers (Hunter, [0259]), “a manager may be able to view all of the assigned shifts and/or may be able to publish certain shifts (e.g., the manager may publish all shifts for a one or two-week period)”; (Hunter, Fig. 27, [0091]), “interface for viewing and modifying a schedule”; (Hunter, [0202]), “interface 2700 may present a high-level overview of the complete schedule for a company or merchant. As shown, the schedule includes a row for each worker, and columns for each day. A user may select a particular range of dates to view, and may be presented with event blocks that indicate an assigned shift or other event for a given day”.
Hunter teaches a delivery management system that can collect and analyze disparate delivery and driver data in near real-time and determine optimized delivery solutions across multiple merchants and multiple delivery fleets (see par. 0115) and the automatically generated schedule may be optimized to ensure that an adequate number of workers, with appropriate capabilities (e.g., position assignments), are scheduled for each shift (see par. 0178), but Hunter does not explicitly teach:
generate schedules for multiple drivers, of the drivers, by performing, a specified periodicity after a previous optimization, optimization based on a schedule optimization algorithm that allows the schedules to be generated in a batch for the multiple drivers, based on a previous schedule generated during the previous optimization, and based on a selection of a final assignment of two potential assignments for the driver.
However Yang teaches:
generate schedules for multiple drivers, of the drivers, by performing, a specified periodicity after a previous optimization, optimization based on a schedule optimization algorithm that allows the schedules to be generated in a batch for the multiple drivers, based on a previous schedule generated during the previous optimization, and based on a selection of a final assignment of two potential assignments for the driver (Yang, Abstract), “wherein the solver comprises a mixed integer programming (MIP) solver, and wherein the MIP solver determines simultaneously a subset of the shift candidates selected in the final schedule and a set of shift assignments of which worker is assigned to which selected shift candidate of the subset of shift candidates”; (Yang, [col. 19, ln. 37-42]), “the optimization model is updated with the new objective function which include the schedule disruption penalty cost term. Then the control is passed onto 1012 where a MP solver is invoked in warm start mode, e.g., the base solution from the previous optimal schedule is used to generate a new solution”; (Yang, [col. 2, ln. 63]-[col. 3, ln. 2]), “wherein the solver comprises an MIP solver, and wherein the MIP solver determines simultaneously a subset of the shift candidates selected in the final schedule and a set of shift assignments of which worker is assigned to which selected shift candidate of the subset of shift candidates such that the hard constraints are fully respected, violations to the soft constraints are minimized, and the new cost function is minimized”; (Yang, [col. 6, ln. 28-38]), “The final optimal solution also select 1 shift candidate from 9:00am to 5:00pm and 1 shift candidate from 1:00pm to 5:00pm. At the time of generating the shift candidates, it is more relevant to choose possible candidates. It is the later optimization process that will decide which subset gets selected in order the get the optimal solution. In some embodiments, the heuristic generates shift candidates based on the availability of types of workers (e.g., only full time workers are available so only full time shifts are generated, only part time workers are available so only part time shifts are generated, etc.)”; (Yang, [col. 7, ln. 10-42]), “…Two categories of decision variables are constructed that correspond to the two types decisions that are being made simultaneously, i.e., which subset of the shift candidates should be used in the final optimal schedule and which worker should be assigned to which shift…”; (Yang, [col. 20, ln. 49-62]), “a set of decision variables is determined representing whether a particular shift candidate is selected in a final schedule and whether a particular worker is assigned to the particular shift candidate. For example, the set of decision variables includes which subset of the shift candidates should be used in the final optimal schedule and which worker should be assigned to which shift. In some embodiments, for a worker i of the set of N workers and a shift candidate j of the set of M shift candidates … ”; (Yang, [col. 11, ln. 7-12]), “Scheduling engine 106 manages the scheduling input data using the transition engine 108 to persist data, generates a set of shift candidates based at least in part on the labor demand, creates an optimal schedule by invoking a MIP solver, produces an incremental solution if a portion of the scheduling input data is changed”; (Yang, [col. 10, ln. 12-41]; [col. 22, ln. 1-16]; [col. 24, ln. 6-5]), Examiner notes “Mere duplication of parts has no patentable significance unless a new and unexpected result is produced”; see MPEP 2144.04.32.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimized delivery solutions in Hunter to include the schedule limitations as taught by Yang. The motivation for doing this would have been to improve the method of a process for integrated scheduling and delivery management in Hunter (see par. 0161) to efficiently include the results of an effective way to obtain an incremental solution without causing large disruption to the current schedule while using the efficient warm start of a MIP solver to solve the incremental scheduling problem (see Yang col. 3, ln. 3-6).
Claim 12 disclose substantially the same subject matter as claim 2, and is rejected using the same rationale as previously set forth.
Claim 14 disclose substantially the same subject matter as claim 4, and is rejected using the same rationale as previously set forth.
Referring to Claim 21, Hunter teaches:
One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors (Hunter, [0061]; [0316]), cause the one or more processors to:
generate, a schedule summary displaying a plurality of selectable portions of availability, wherein the schedule summary comprises schedules for drivers (Hunter, Figures 39 and 41-46), “Scheduling interface with selectable timeslots of availability”; (Hunter, [0129]), “Memory 110 also includes a user interface generator 120, configured to dynamically generate, modify, and/or update graphical user interfaces that present a variety of data. For example, user interface generator 120 may generate user interfaces for entering or modifying information regarding merchants and workers (e.g., deliverers), configuring fleets, processing delivery tasks, scheduling… user interface generator 120 may generate dynamic and interactive user interfaces that may be presented on a user device, such as user device 134. Various user interfaces generated by user interface generator 120”; (Hunter, [0128]), “Scheduling management tool 116 can also manage and/or partially automate the process of scheduling workers for shifts or tasks. In some embodiments, a worker or a supervising user (e.g., a manager) may be able to configure availability parameters for a worker, which define when the worker is available to work”; (Hunter, [0124]; [0116]),
wherein a schedule, of the schedules, comprises a plurality of selectable portions for a block of availability submitted by a driver, of the drivers, via a driver device, and wherein the plurality of selectable portions comprise: one or more unfilled portions that are not scheduled, and one or more filled portions that are scheduled (Hunter, [0254]), “multiple open shifts may be represented by a single shift tile, shown as open shifts tile 4104, which may also be color-coded or otherwise differentiated (e.g., via shade, pattern, size, etc.) from the other shift tiles. In this example, open shifts tile 4104 is a neutral-colored tile (e.g., grey) which can, in some embodiments, also indicate the position types associated with the open shift”; (Hunter, [0253]), “… open shift tile 4102 is color-coded or otherwise differentiated (e.g., shade, pattern, size, etc.) from the other shift tiles based on the position type… or based on any other parameter. In this example, open shift tile 4102 is colored red based on the position type to match the color of other shift tiles”; (Hunter, Figures 39 and 41-46), “Scheduling interface with scheduled and unscheduled blocks” (Hunter, [0264]);
receive, a selection of a selectable portion, of the plurality of selectable portions, for the driver (Hunter, [0246]-[0247]), “FIGS. 39-55, example interfaces for generating, manipulating, and/or assigning open shifts are shown, according to various embodiments. An open shift is a block of time (i.e., a time period) that is not assigned to a particular worker (e.g., a driver). In other words, an open shift is a period of time that is available for assignment to a worker or for a worker to select…open shift can be automatically assigned or a manager can choose to moderate which workers can select open shifts… Any of the example interfaces described herein may be generated by user interface generator 120, for example, as described in detail above. More generally, system 100 may be configured to perform any of the functions and/or generate any of the interfaces described herein, although it will be appreciated that in some other embodiments, other systems, devices, etc. can perform the functions described below”; (Hunter, Fig. 52, [0269]), “…if workers have applied for the open shift, interface 5200 may include a list of eligible workers…”; (Hunter, [0124]; [0146]; [0260]),
wherein the selection is configured to cause one or more details of the driver to be displayed on a graphical user interface (GUI) of an electronic device (Hunter, Fig. 22-23, [0086]-[0087]), “…spreadsheet for managing workers, according to some embodiments… FIG. 23 is an example interface for a viewing and modifying worker attributes”; (Hunter, [0195]), “a user has selected an “employment information” tab 2302 while viewing a profile for a worker named “Luke.” The user may also navigate to other tabs to view or edit basic information, login credentials, notification preferences, etc.…”; (Hunter, [0191]; [0194]);
coordinate displaying one or more optimized schedules, of the optimized schedules, for one or more drivers, of the multiple drivers, based on generating the optimized schedules for the multiple drivers (Hunter, [0259]), “a manager may be able to view all of the assigned shifts and/or may be able to publish certain shifts (e.g., the manager may publish all shifts for a one or two-week period)”; (Hunter, Fig. 27, [0091]), “interface for viewing and modifying a schedule”; (Hunter, [0202]), “interface 2700 may present a high-level overview of the complete schedule for a company or merchant. As shown, the schedule includes a row for each worker, and columns for each day. A user may select a particular range of dates to view, and may be presented with event blocks that indicate an assigned shift or other event for a given day”.
Hunter teaches a delivery management system that can collect and analyze disparate delivery and driver data in near real-time and determine optimized delivery solutions across multiple merchants and multiple delivery fleets (see par. 0115) and the automatically generated schedule may be optimized to ensure that an adequate number of workers, with appropriate capabilities (e.g., position assignments), are scheduled for each shift (see par. 0178), but Hunter does not explicitly teach:
generate optimized schedules for multiple drivers, of the drivers, by performing optimization based on a selection of a final assignment of two potential assignments for the driver, wherein the multiple drivers include the driver.
However Yang teaches:
generate optimized schedules for multiple drivers, of the drivers, by performing optimization based on a selection of a final assignment of two potential assignments for the driver, wherein the multiple drivers include the driver (Yang, Abstract), “wherein the solver comprises a mixed integer programming (MIP) solver, and wherein the MIP solver determines simultaneously a subset of the shift candidates selected in the final schedule and a set of shift assignments of which worker is assigned to which selected shift candidate of the subset of shift candidates”; (Yang, [col. 7, ln. 10-42]), “…Two categories of decision variables are constructed that correspond to the two types decisions that are being made simultaneously, i.e., which subset of the shift candidates should be used in the final optimal schedule and which worker should be assigned to which shift…”; (Yang, [col. 20, ln. 49-62]), “a set of decision variables is determined representing whether a particular shift candidate is selected in a final schedule and whether a particular worker is assigned to the particular shift candidate. For example, the set of decision variables includes which subset of the shift candidates should be used in the final optimal schedule and which worker should be assigned to which shift. In some embodiments, for a worker i of the set of N workers and a shift candidate j of the set of M shift candidates … ”; (Yang, [col. 11, ln. 7-12]), “Scheduling engine 106 manages the scheduling input data using the transition engine 108 to persist data, generates a set of shift candidates based at least in part on the labor demand, creates an optimal schedule by invoking a MIP solver, produces an incremental solution if a portion of the scheduling input data is changed “(Yang, [col. 19, ln. 37-42]), “the optimization model is updated with the new objective function which include the schedule disruption penalty cost term. Then the control is passed onto 1012 where a MP solver is invoked in warm start mode, e.g., the base solution from the previous optimal schedule is used to generate a new solution”; (Yang, [col. 10, ln. 12-41]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the optimized delivery solutions in Hunter to include the schedule limitations as taught by Yang. The motivation for doing this would have been to improve the method of a process for integrated scheduling and delivery management in Hunter (see par. 0161) to efficiently include the results of an effective way to obtain an incremental solution without causing large disruption to the current schedule while using the efficient warm start of a MIP solver to solve the incremental scheduling problem (see Yang col. 3, ln. 3-6).
Claims 3, 4, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hunter et al., U.S. Publication No. 2022/0092521 [hereinafter Hunter], in view of Yang, U.S. Patent No. 11,763,220 [hereinafter Yang], and further in view of Arena et al., U.S. Publication No. 2019/0196502 [hereinafter Arena].
Referring to Claim 3, Hunter in view of Yang teaches the system of claim 1. Hunter further teaches:
wherein, to generate the schedules, the instructions cause the one or more processors to:
each schedule for each driver, of the multiple drivers is displayed as a row with a unique driver identification label identifying each driver (Hunter, Fig. 27, [0202]), “Referring now to FIG. 27, an example interface 2700 for viewing and modifying a schedule is shown… the schedule includes a row for each worker, and columns for each day. A user may select a particular range of dates to view, and may be presented with event blocks that indicate an assigned shift or other event for a given day. In this regard, interface 2700 is dynamic, updating as the user navigates between dates and workers”; (Hunter, Figures 39 and 41-46), “Scheduling interface with selectable timeslots of availability”; (Hunter, [0258]);
Hunter teaches a generated schedule may identify workers that are certified or available as drivers (i.e., deliverers), and may populate a merchant schedule with a sufficient number of drivers to meet the merchant's needs (see par. 0162), but Hunter does not explicitly teach:
iteratively assign at least one driver of one or more remaining drivers of the multiple drivers, to at least one other tractor using a first set of rules of the schedule optimization algorithm.
However Arena teaches:
iteratively assign at least one driver of one or more remaining drivers of the multiple drivers, to at least one other tractor using a first set of rules of the schedule optimization algorithm (Arena, [0106]; [0034]; [0087]); (Arena, [0009]), “the logistics management platform can receive, from a user device, a request for a schedule that assigns a team of drivers and a fleet of vehicles to a set of deliveries. In this case, assume the request is a request to generate a new schedule”; (Arena, [0027]-[0028]), “… the user device can deploy the existing schedule, such that the existing schedule is sent to one or more devices associated with drivers and vehicles tasked with carrying out the set of deliveries”; (Arena, [0011]), “… determines one or more optimal routes that the team of drivers and the fleet of vehicles can use to perform the set of deliveries…provide the new schedule for display on a user interface of the user device and/or to one or more devices associated with drivers and vehicles tasked with carrying out the set of deliveries”; (Arena, [0033]), “the logistics management platform can determine one or more delivery routes using a list of available drivers that excludes Bob. In this example, the delivery destinations previously assigned to Bob are allocated between Sam and Jim, and are assigned in a way that allows both Sam and Jim to perform deliveries in a timely, cost effective manner”; (Arena, [0099]-[0100]), “… logistics management platform 220 can modify the schedule based on a request from user device 210… the request can be associated with a real-time change to the schedule, such as a driver calling in sick, a new delivery being added to the schedule or removed from the schedule, and/or the like. Additionally, logistics management platform 220 can modify the schedule using information included in the request…; (Arena, [0102]), “can modify the new schedule or the existing schedule based on a forecasting technique… logistics management platform 220 can determine that the likelihood of the particular scheduling modification occurring satisfies a threshold value, and can modify the new schedule or the existing schedule prior to the particular scheduling modification occurring”, Examiner considers the threshold value to sufficiently teach a rule, (Arena, [0019]; [0065]; [0067]; [0071; [0101]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the generated schedule for delivery drivers in Hunter to include the assignment limitation as taught by Arena. The motivation for doing this would have been to improve the method of a process for integrated scheduling and delivery management in Hunter (see par. 0161) to efficiently include the results of scheduling a team of drivers to perform a set of deliveries (see Arena par. 0001).
Claim 13 disclose substantially the same subject matter as claim 3, and is rejected using the same rationale as previously set forth.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Hunter et al., U.S. Publication No. 2022/0092521 [hereinafter Hunter], in view of Yang, U.S. Patent No. 11,763,220 [hereinafter Yang], in view of Arena et al., U.S. Publication No. 2019/0196502 [hereinafter Arena], and further in view of Qu, U.S. Patent No. 7,257,459 [hereinafter Qu].
Referring to Claim 5, Hunter in view of Yang in view of Arena teaches the system of claim 3. Hunter teaches executing a machine learning model specific to the at least one key parameter using the task information to generate a delivery assignment for the specific task (see par. 0063), but Hunter does not explicitly teach:
wherein the first set of rules comprises an integer programming model and linear programming optimization.
However Qu teaches:
wherein the first set of rules comprises an integer programming model and linear programming optimization (Qu, [col. 10, ln. 66]-[col. 11, ln. 26]), “The output of the mixed integer linear program optimization using the previously described objectives and constraints is a processing order and designated pilot lot that seeks to maximize throughput of the associated process tool 105… The scheduling unit 130 may run the optimization at fixed intervals to generate a schedule that is followed for a predetermined number of runs of the process tool 105. Alternatively, the scheduling unit 130 may execute the optimization prior to each dispatching decision to react to changes in the current queue… The scheduling problem may still be resolved using the linear mixed-integer programming model described above in a simplified manner…; (Qu, [col. 8, ln. 3-9]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the modeling of key parameters to generate delivery assignments in Hunter to include the programming limitations as taught by Qu. The motivation for doing this would have been to improve the method of a process for integrated scheduling and delivery management in Hunter (see par. 0161) to efficiently include the results of optimizing throughput (see Qu col. 8, ln. 52-53).
Claim 15 disclose substantially the same subject matter as claim 5, and is rejected using the same rationale as previously set forth.
Claims 7, 10, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hunter et al., U.S. Publication No. 2022/0092521 [hereinafter Hunter], in view of Yang, U.S. Patent No. 11,763,220 [hereinafter Yang], in view of Pike et al. U.S. Publication No. 2019/0095859 [hereinafter Pike].
Referring to Claim 7, Hunter in view of Yang teaches the system of claim 1. Hunter teaches a generated schedule may identify workers that are certified or available as drivers (i.e., deliverers), and may populate a merchant schedule with a sufficient number of drivers to meet the merchant's needs (see par. 0162), but Hunter does not explicitly teach:
wherein to generate the schedules, the instructions cause the one or more processors to:
determine one or more respective day cab schedules for each respective day cab driver of the multiple drivers by:
segmenting each respective work week of each respective day cab driver into one or more segments; and
marking one or more day cab vehicles.
However Pike teaches:
wherein to generate the schedules, the instructions cause the one or more processors to:
determine one or more respective day cab schedules for each respective day cab driver of the multiple drivers by:
segmenting each respective work week of each respective day cab driver into one or more segments (Pike, [0039]), “The log of service hours tracks the number of hours in which the freight operator has operated a freight vehicle continuously, and/or the number of hours which the freight operator operated the freight vehicle over a recent time interval (e.g., number of hours in the past day)”; (Pike, [0054]), “the matching component 230 may recognize that a freight operator that is coming off a rest interval may be the more suitable for some longer freight assignments than an otherwise comparable freight operator who is nearing the limit of the continuous duration of vehicle operation by the freight operator”; and
marking one or more day cab vehicles (Pike, [0053]), “The matching component 230 can select a freight operator for a freight request using selection criteria 235 that are based on the service parameters 209 of the service request 201…the freight request 201 may specify a timing parameter for when the load is to be delivered at the destination. From the delivery time, the matching component 230 may determine the pick up window for the load. In some examples, the matching component 230 determines a maximum window for specifying pick up of the packed trailer 222 based on the timing parameters specified by the freight request 201 for arrival at the destination (e.g., delivery window)”;
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the generated schedule for delivery drivers in Hunter to include the segment limitations as taught by Pike. The motivation for doing this would have been to improve the method of a process for integrated scheduling and delivery management in Hunter (see par. 0161) to efficiently include the results of identifying freight operators that satisfy selection criteria (see Pike par. 0060).
Referring to Claim 10, Hunter in view of Yang teaches the system of claim 1. Hunter teaches a generated schedule may identify workers that are certified or available as drivers (i.e., deliverers), and may populate a merchant schedule with a sufficient number of drivers to meet the merchant's needs (see par. 0162), but Hunter does not explicitly teach:
wherein to generate the schedules, the instructions cause the one or more processors to:
assign a rotation driver, of one or more rotation drivers of the multiple drivers, to one or more rotation tractors; and
when the one or more rotation tractors are unavailable, assign a different rotation driver, of the one or more rotation drivers, to at least one tractor, of the one or more rotation tractors, along with at least one other driver.
However Pike teaches:
wherein to generate the schedules, the instructions cause the one or more processors to:
assign a rotation driver, of one or more rotation drivers of the multiple drivers, to one or more rotation tractors (Pike, [0060]), “the matching component 230 may implement a selection process of progressively identifying freight operators that satisfy selection criteria based on an optimization consideration. By way of example, the matching component 230 may identify a first freight operator (or set of freight operators), using a first set of criteria, which may include optimization considerations”; (Pike, [0012]); and
when the one or more rotation tractors are unavailable, assign a different rotation driver, of the one or more rotation drivers, to at least one tractor, of the one or more rotation tractors, along with at least one other driver (Pike, [0049]), “the service state can correspond to (i) deadhead, corresponding to when the freight operator is operating the freight vehicle with no trailer, or an empty trailer…More or fewer states may also be defined, such as (v) idle, when the freight operator is not operating a freight vehicle, but is ready for assignment”; (Pike, [0056]), “implements a progressive selection process that utilizes a set of selection criteria (e.g., distance, or calculated deadhead cost from a current location of the freight operator)… a candidate freight operator, or a set of candidate freight operators is selected based on a determination that the freight operator is available. In such variations, the availability of the freight operators may be based on the service state of the freight operator and/or the load state of the freight vehicle which the freight operator is operating”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the generated schedule for delivery drivers in Hunter to include the assigning limitations as taught by Pike. The motivation for doing this would have been to improve the method of a process for integrated scheduling and delivery management in Hunter (see par. 0161) to efficiently include the results of identifying freight operators that satisfy selection criteria (see Pike par. 0060).
Claim 17 disclose substantially the same subject matter as claim 7, and is rejected using the same rationale as previously set forth.
Claim 10 disclose substantially the same subject matter as claim 20, and is rejected using the same rationale as previously set forth.
Claims 8, 9, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hunter et al., U.S. Publication No. 2022/0092521 [hereinafter Hunter], in view of Yang, U.S. Patent No. 11,763,220 [hereinafter Yang], in view of Pike et al. U.S. Publication No. 2019/0095859 [hereinafter Pike], and further in view of Silzer et al., U.S. Publications 2013/0021174 [hereinafter Silzer].
Referring to Claim 8, Hunter in view of Yang in view of Pike teaches the system of claim 7. Hunter teaches a generated schedule may identify workers that are certified or available as drivers (i.e., deliverers), and may populate a merchant schedule with a sufficient number of drivers to meet the merchant's needs (see par. 0162), but Hunter does not explicitly teach:
wherein segmenting each respective work week of each respective day cab driver comprises: creating one or more segments by removing one or more domicile times.
However Silzer teaches:
wherein segmenting each respective work week of each respective day cab driver comprises:
creating one or more segments by removing one or more domicile times (Silzer, [0061]), “the server can generate reports to track and monitor the activity and costs of to manage a facility. For example, an area work activity report can include the time spent maintaining a facility by each vehicle group or vehicle type (e.g., greens, fairways, or roughs) over a selected period to time (e.g., a week or month), the time spent outside work zone (e.g., travel time between work zones, but excluding time in home zones)…”; (Silzer, [0024]), “Each of these features can constitute different zones or be grouped into zones, sub-zones, or other category (e.g., work groups). In a particular aspect, code is embodied on the server by which zones can be added, deleted, and modified”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the generated schedule for delivery drivers in Hunter to include the home zone as taught by Silzer. The motivation for doing this would have been to improve the method of a process for integrated scheduling and delivery management in Hunter (see par. 0161) to include the results of monitor efficiency for various activities, vehicles, equipment, work zones, and/or personnel (see Silzer par. 0026).
Referring to Claim 9, Hunter in view of Yang in view of Pike in view of Silzer teaches the system of claim 8. Hunter teaches a generated schedule may identify workers that are certified or available as drivers (i.e., deliverers), and may populate a merchant schedule with a sufficient number of drivers to meet the merchant's needs (see par. 0162), but Hunter does not explicitly teach:
wherein the one or more respective day cab schedules are determined further by:
assigning each respective day cab driver to a respective marked day cab vehicle of the one or more day cab vehicles for use during the one or more segments.
However Pike teaches:
wherein the one or more respective day cab schedules are determined further by:
assigning each respective day cab driver to a respective marked day cab vehicle of the one or more day cab vehicles for use during the one or more segments (Pike, [0028]), “the computer system 110 may make a determination as to which freight operators are available and sufficiently proximate to the site of the shipper to pick up the packaged trailer 122 within the requested time interval”; (Pike, [0054]), “the matching component 230 may recognize that a freight operator that is coming off a rest interval may be the more suitable for some longer freight assignments than an otherwise comparable freight operator who is nearing the limit of the continuous duration of vehicle operation by the freight operator”; (Pike, [0012]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the generated schedule for delivery drivers in Hunter to include the assigning limitations as taught by Pike. The motivation for doing this would have been to improve the method of a process for integrated scheduling and delivery management in Hunter (see par. 0161) to efficiently include the results of identifying freight operators that satisfy selection criteria (see Pike par. 0060).
Claim 18 disclose substantially the same subject matter as claim 8, and is rejected using the same rationale as previously set forth.
Claim 19 disclose substantially the same subject matter as claim 9, and is rejected using the same rationale as previously set forth.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Buckzkowski et al. (US 20100299177 A1) – A method of dynamic bus dispatching and labor assignments based on real time vehicle and passenger data. The method includes running a transportation services module on a computer of a dispatch command center. The method includes receiving, at the computer system, current location information for a plurality of buses. The transportation services module determines a route completion time period for the vehicles and then generates a dispatch schedule for each of the vehicles based on the determined route completions. The dispatch schedules are transmitted to the buses and displayed on a monitor to the bus driver, thereby allowing real time or dynamic updating of dispatching based on collected location information. The method also includes determining and reporting a set of labor assignments for drivers of the buses based on the current location information, the route completion time periods, and break and shift information associated with each of the drivers.
Vasanth et al. (US 12008496 B1) – A fleet management system may create and manage tasks within an environment associated with fulfilling, sorting, inducting, and/or distributing packages, such as a warehouse, packaging facility, sortation center, or distribution center. The fleet management system may assign the tasks to agents within the environment. Each of the agents may have capabilities or tasks that the agents are configured to perform and the fleet management system may use these capabilities to assign the tasks to the agents. Additionally, tasks may be assigned based on a location of the agents within the environment. As tasks are generated, the fleet management system may determine a suitable agent to perform the task and may transmit instructions to the agent for carrying out the task. The fleet management system may provide a centralized platform to manage agents, optimize the assignment of tasks, and increase productivity within the environments.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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