DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The Amendment filed on 08/08/2025 has been entered. Claims 1-6, 8, 10-11 and 13-26 are pending in the instant patent application. Claims 1, 3-6, 8, 11, 15-21 and 25 are amended. Claims 7, 9 and 12 are cancelled. This Final Office Action is in response to the claims filed.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments to the claims are sufficient to overcome the 35 U.S.C. §102 rejections. The rejections have been withdrawn.
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and newly cited art.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive.
Regarding Applicant’s arguments that claim 1 is not directed towards an abstract idea, Examiner respectfully disagrees. The amended language still recites limitations that fall into abstract idea groupings for they are concepts that can still be practically performed by a human. Examiner will remind Applicant that the courts have found claims requiring a generic computer or nominally reciting a generic computer can still recite a mental process even though the claim limitations are not performed entirely in the human mind. Furthermore, general purpose computer elements/structure, similar to the claimed invention, used to apply a judicial exception, by use of instruction implemented on a computer, has not been found by the courts to integrate the abstract idea into a practical application; see MPEP 2106.05(f).
Regarding Applicant’s arguments that claim 1 improves the functioning of a computer, Examiner respectfully disagrees. The claim language recites elements that are being implemented in their generic capacity performing generic functions. It is not evident with the claim language of any improvements to the technology. Furthermore, Applicant makes reference to Example 47, however, Example 47 pertains to anomaly detection using artificial intelligence. Examiner assumes that the referenced example is Example 37. In Example 37, the claim as a whole integrated the mental process because the additional elements recited a specific manner of automatically displaying icons to the user based on usage which provided a specific improvement over prior system, resulting in an improved user interface for electronic devices. The claims as presently do not recite any improvements to the user interface other than displaying data on a screen.
Regarding Applicant’s argument that Claim 1 provides an inventive concept under Step 2B, Examiner respectfully disagrees. The elements presented do not amount to significantly more mainly due to their generic use and implementation. Furthermore, Claim 1 recites computer functions that the courts have recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)…at least, 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))).
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.
Regarding Claims 1-6, 8, 10-11 and 13-26, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 1-6, 8, 10-11 and 13-26 are directed to the abstract idea of optimizing business workflows.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites (a) generating a workflow map by selecting one or more processes from a plurality of processes of one or more model business maps, wherein the one or more model business maps comprise the plurality of processes and a predicted time of completion for at least a portion of the plurality of processes corresponding to a model business; (b) modifying the workflow map for a target business based on a business characteristic of the target business, wherein the target business is different than the model business, wherein modifying the workflow map comprises (i) eliminating, modifying, or adding, the one or more tasks of the selected one or more processes, or (ii) generating a modified predicted time of completion of the one or more modified tasks based at least in part on the business characteristic of the target business, or both (i) and (ii).wherein the predicted time of completion is continuously updated based at least in part on receiving feedback data transmitted to one or more agents; and (c) updating a position of one or more graphical representations of at least a portion of the one or more processes of the modified workflow map based at least in part on the updated predicted time of completion of the one or more modified tasks.
These claim limitations fall within the Mental Processes grouping of abstract ideas because each limitation can be practically performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Accordingly, the claim recites an abstract idea and dependent claims 2-6, 8, 10-11 and 13-26 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a graphical user interface, a network and a processor. The graphical user interface, a network and a processor are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 1 includes various elements that are not directed to the abstract idea under 2A. These elements include a graphical user interface, a network, a processor generic computing elements described in the Applicant's specification in at least Para 0217-0227. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 1 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-6, 8, 10-11, 13-22 and 25-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Knijnik et al. (US 2017/0249574 A1) in view of McGee et al. (US 2021/0278841 A1) further in view of Grimaldi et al. (US 2016/0162817 A1).
Regarding Claim 1, Knijnik teaches the limitations of Claim 1 which state
(a) generating a workflow map by selecting one or more processes from a plurality of processes of one or more model business maps, wherein the one or more model business maps comprise the plurality of processes and a predicted time of completion for at least a portion of the plurality of processes corresponding to a model business (Knijnik: Para 0030 via a modeling module that configures the processor to generate one or more models for predicting resource performance for each of the plurality of task requirements using the resource characteristics associated with a plurality of resources and the project data; an analysis module that configures the processor to generate one or more ordered sequences of tasks to be completed by the first resource, wherein each of the one or more ordered sequences begins with the first resources’ start time, ends with the first resource's end time, includes at least a portion of the plurality of task requirements, and wherein consecutive tasks in a ordered sequence define the availability of the first resource);
(b) modifying the workflow map for a target business by adjusting the workflow map based on a business characteristic of the target business, wherein the target business is different than the model business (Knijnik: Para 0030 via wherein the analysis module further configures the processor to identify based on the calculated total durations, an optimal ordered sequence from among the one or more ordered sequences, wherein the optimal ordered sequence has the lowest calculated total duration; and wherein the communication module further configures the processor to provide over a network to the remote device associated with the first resource, the optimal ordered sequence);
wherein modifying the workflow map comprises (i) eliminating, modifying, or adding, utilizing a processor, the one or more tasks of the selected one or more processes, or (ii) generating a modified predicted time of completion of the one or more modified tasks based at least in part on the business characteristic of the target business, or both (i) and (ii) (Knijnik: Para 0074 via Using the system 100, a user is able to create a project object in the system. A user, such as a project manager or sales representative, accesses the system 100 through a GUI presented on the display of the user device. The server machine 102 provides the user with an interface and prompts the user to input information concerning a new project, including a description of the project, the type of project, the total labor force (in hours or days) needed for the project, the total cost for the project, the start date, and the deadline date. After a user enters certain information corresponding to the creation of a project, such as the type of project, the description of the project, or both, the server machine 102 calls the best practices engine to determine, based on the type of project and an analysis of the description of the project, tasks to complete the project and provides the proposed tasks to the user to confirm their use in the project. The user confirms that the tasks are correct or makes modifications to the tasks or portions thereof in accordance with the task creation process, whereupon the tasks are created in the system, assigned to resources, and monitored in accordance with the description herein).
However, Knijnik does not explicitly disclose the limitations of Claim 1 which state (ii) generating a modified predicted time of completion of the one or more modified tasks based at least in part on the business characteristic of the target business, wherein the predicted time of completion is continuously updated based at least in part on receiving feedback data transmitted to the processor over a network from one or more agents.
McGee though, with the teachings of Knijnik, teaches of
(ii) generating a modified predicted time of completion of the one or more modified tasks based at least in part on the business characteristic of the target business (McGee: Para 0042 via While the machine 100 is performing the task in the autonomous mode, the controller 114 may determine an updated estimated completion time and/or an updated progress of the task. The updated estimated completion time is based on an elapsed time since initiation of the task (e.g., the updated estimated completion time is the estimated completion time reduced by the elapsed time). The updated progress of the task is based on a movement of the machine 100. For example, the controller 114 may determine a percentage of the task that has been completed, since initiation of the task, based on the movement of the machine 100. The controller 114 may determine the movement of the machine 100 based on location data (e.g., latitude and longitude coordinates) relating to a location of the machine 100 since initiation of the task. Thus, the updated progress of the task may be associated with location data that identifies a location of the machine 100 at a time that the progress of the task is updated),
wherein the predicted time of completion is continuously updated based at least in part on receiving feedback data transmitted to the processor over a network from one or more agents (McGee: Para 0046-0047 via Based on a command (e.g., an operator command) to continue performance of the task (e.g., via the operator controls 202), the controller 114 may obtain the stored information identifying the association between the updated estimated completion time and the updated progress, and cause the machine 100 to continue performance of the task in the autonomous mode. The controller 114 may cause the task to be continued with the updated estimated completion time (e.g., the task is continued with the updated estimated completion time displayed, or otherwise notified to a human operator, a supervisor, a device that performs scheduling, and/or the like). Moreover, performance of the task is continued using the association between the updated estimated completion time and the updated progress of the task. For example, the controller 114 may determine a location associated with the amount of progress, and determine that the estimated completion time from the location (based on the amount of progress) is the updated estimated completion time. After the task is completed, the controller 114 may perform one or more actions. An action may include generating, or updating, a schedule based on completion of the task. An action may include transmitting a notification (e.g., to a user device, a server device, and/or the like) that indicates completion of the task. An action may include determining an actual time that was taken to complete the task, which may be used to refine an algorithm or model used to determine an estimated completion time. An action may include generating a report that details the task, the estimated completion time, an actual completion time, discontinuations and/or continuations of the task, and/or the like, which may be used to identify optimal machine utilization, inefficiencies, and/or the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Knijnik with the teachings of McGee in order to have (ii) generating a modified predicted time of completion of the one or more modified tasks based at least in part on the business characteristic of the target business and wherein the predicted time of completion is continuously updated based at least in part on receiving feedback data transmitted to the processor over a network from one or more agents. The motivation behind this being to incorporate the teachings of task completion time estimations. Furthermore, combining prior art elements according to known methods will yield predictable results and would be obvious to try.
Furthermore, Knijnik does not explicitly disclose the limitation of Claim 1 which states updating a position of one or more graphical representations of at least a portion of the one or more processes of the modified workflow map on the GUI based at least in part on the updated predicted time of completion of the one or more modified tasks.
Grimaldi though, with the teachings of Knijnik/McGee, teaches of
updating a position of one or more graphical representations of at least a portion of the one or more processes of the modified workflow map on the GUI based at least in part on the updated predicted time of completion of the one or more modified tasks (Grimaldi: Para 0055 via the user interface of computing device 104 may provide a graphical overview to users 102 who log in to system 100 to check the status of vehicle 138. The graphical overview may show the list of services that have been requested for vehicle 138, which services have been completed, which services are currently being performed, which department/vendor currently has possession of vehicle 138, and any similar information as may become apparent to those skilled in the relevant art(s) after reading the description herein. Likewise, the length of time vehicle 138 has spent in the workflow of services may be displayed, as well as how long each completed service has taken, how long the currently performed service is taking, and approximately how long the remaining services will take, either individually or as a whole).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Knijnik/McGee with the teachings of Grimaldi in order to have updating a position of one or more graphical representations of at least a portion of the one or more processes of the modified workflow map on the GUI based at least in part on the updated predicted time of completion of the one or more modified tasks. The motivations behind this being to incorporate the teachings of updating status information in real-time. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 2, Knijnik/McGee/Grimaldi teaches the limitations of Claim 2 which state
wherein the business characteristic comprises a size of the target business, a client base of the target business, financial data of the target business, or one or more operational procedures of the target business (Knijnik: Para 0070 via The machine learning engine is also capable of generating information for use with forecasting and optimizing decisions (including allocation of tasks). The system databases 106, 104 contain all relevant information pertaining to the tasks that have been and are to be performed, how the tasks were performed, and by what resources the tasks were performed and managed. The machine learning engine is capable of analyzing this information to develop associations between the information and generate forecasts corresponding to, among other things, task completion, resource utilization, and financial inlays and outlays. As additional information is provided to the system, the machine learning engine continually updates and improves its ability to forecast what is likely to happen with respect to the selection of a particular parameter for a particular action (e.g., the assignment of a particular resource to a particular task), and can therefore determine, for example, the best resources to allocate for each task, which resources are likely to work best together for completion of a particular task, how long each kind of engagement, project, or task will take, and forecast demand for services and sales).
Regarding Claim 3, Knijnik/McGee/Grimaldi teaches the limitations of Claim 3 which state
further comprising generating or updating one or more training programs based at least in part on receiving the transmitted feedback data ((Knijnik: Para 0022 via a project management system is provided for efficient management of workflow data among resources in one or more networks built on top of an underlying network, wherein the workflow data includes a plurality of projects and wherein the plurality of projects includes a plurality of tasks, the project management system comprising: a memory that stores (i) a plurality of project data records, and (ii) a plurality of characteristics of a plurality of resources; a project divider that separates a project into a set of tasks based on said project data records and the characteristics of said resources; a performance tracker that generates metrics from network traffic along the one or more overlay networks, including the completion of one or more tasks from the set of tasks in the project; a learning engine that continuously quantifies the characteristics of the resources based upon an analysis of the metrics; and a feedback loop that (i) generates a new project data record when the project is completed, (ii) stores said new project data record in said memory, and (iii) dynamically updates the characteristics of said resources).
Regarding Claim 4, Knijnik/McGee/Grimaldi teaches the limitations of Claim 4 which state
wherein the feedback data is configured to modify the workflow map or update the one or more training programs based on (i) feedback data transmitted from the one or more agents performing the at least portion of the processes or (ii) one or more performance metrics of the target business (Knijnik: Para 0022 via a project management system is provided for efficient management of workflow data among resources in one or more networks built on top of an underlying network, wherein the workflow data includes a plurality of projects and wherein the plurality of projects includes a plurality of tasks, the project management system comprising: a memory that stores (i) a plurality of project data records, and (ii) a plurality of characteristics of a plurality of resources; a project divider that separates a project into a set of tasks based on said project data records and the characteristics of said resources; a performance tracker that generates metrics from network traffic along the one or more overlay networks, including the completion of one or more tasks from the set of tasks in the project; a learning engine that continuously quantifies the characteristics of the resources based upon an analysis of the metrics; and a feedback loop that (i) generates a new project data record when the project is completed, (ii) stores said new project data record in said memory, and (iii) dynamically updates the characteristics of said resources).
Regarding Claim 5, Knijnik/McGee/Grimaldi teaches the limitations of Claim 5 which state
wherein the one or more performance metrics comprise (i) an efficiency of the target business or (ii) an amount of time in which the one or more agents complete the one or more tasks of the selected one or more processes (Knijnik: Para 0086 via If the final duration of the task is longer than a particular threshold, then the system 100 divides the task to ensure that the task is under the given threshold. In the preferred embodiment, the threshold is 40 hours, although other thresholds may be given depending on the type of task. In the preferred embodiment, if the total calculated task is greater than 40 hours, the system divides the task approximately in half, and allows the user to review the items included in each new task, in order to determine whether it makes sense to move certain items to one new task or to the other, depending upon production efficiencies).
Regarding Claim 6, Knijnik/McGee/Grimaldi teaches the limitations of Claim 6 which state
wherein the efficiency of the target business corresponds to the one or more tasks of the selected one or more processes completed per unit time (Knijnik: Para 0029, 0085, 0142 via wherein the task requirements includes a deadline for task completion; automatically determine a period of time associated with the execution of the task by available production time of one or more resources, wherein the period of time is calculated for each available resource and is based, at least in part, on a current utilization of the available resource in relation to the remaining capacity of the available production time; exclude available resources when the determined period of time exceeds the deadline for task completion; automatically select a resource of the one or more resources based, at least in part on the task allocation and the period of time associated with the task;... the system 100 sums the time necessary to complete each of the items of the task and determines the total expected time to complete the task. The system 100 then displays this information to the user, and prompts the user to re-evaluate the time he has allotted for each item of the task to insure that the user has not provided any time estimates for the individual items that are unreasonably long or short. The system 100 further prompts the user to re-evaluate whether the time to complete the entire task is reasonable so that the deadline agreed to with the client can be met... Comparing results in a ranking, instead of creating goals to the production team, is a fair means for evaluating resources that stimulates healthy competition and helps to achieve a meritocratic comparison of performances. Another benefit of the system is that, by just comparing the completion rate of resources, the duration of each task is less important, as everyone is compared to a benchmark. Theoretically the durations could all be set as, for example, one day, and the results would be the same when comparing to a position in the ranking, because any mistakes would be equally distributed along the resources in the course of the period of evaluation).
Regarding Claim 8, Knijnik/McGee/Grimaldi teaches the limitations of Claim 8 which state
wherein the one or more tasks are automatically or manually assigned to the one or more agents (Knijnik: Para 0104 via After the task creation process is concluded, the system automatically starts the task allocation process. In this process, there are three core constraints recognized by the system 100: the system never assigns two tasks at the same time to a single person (in other words, each resources works on only one task at a time); the system assigns each task to a single professional; and no resource is to be assigned any task that is not in the schedule (in other words, no resources works on a task unless it has been scheduled)).
Regarding Claim 10, Knijnik/McGee/Grimaldi teaches the limitations of Claim 10 which state
wherein the one or more tasks are manually claimed by the one or more agents or by a recommendation generated utilizing one or more algorithms (Knijnik: Para 0122 via The system 100 prevents changes to schedules on the same day (D.sub.0). In a preferred embodiment, the system 100 also prevents changes to schedules on the next working day (D.sub.0+1). The system 100 permits only users with sufficient access privileges and rights, such as top management, to override these restrictions and to make changes to schedules on D.sub.0. Unlike the creation and allocation of tasks in other systems, in this case the project manager is the only one that can create a task in the present system, and each such task must undergo the entire process of creation and allocation).
Regarding Claim 11, Knijnik/McGee/Grimaldi teaches the limitations of Claim 11 which state
wherein the one or more model business maps comprise (i) a collection of the one or more tasks for completing the selected one or more processes of the target business and (ii) operational data associated with the selected one or more processes (Knijnik: Para 0030 via models for predicting resource performance for each of the plurality of task requirements using the resource characteristics associated with a plurality of resources and the project data; an analysis module that configures the processor to generate one or more ordered sequences of tasks to be completed by the first resource, wherein each of the one or more ordered sequences begins with the first resources’ start time, ends with the first resource's end time, includes at least a portion of the plurality of task requirements, and wherein consecutive tasks in a ordered sequence define the availability of the first resource).
Regarding Claim 13, Knijnik/McGee/Grimaldi teaches the limitations of Claim 13 which state
(Knijnik: Para 0136 via Another variation is having the project manager and production manager role being done by the same person. It is also possible to have another individual in charge of reviewing and adjusting schedules online for a project team, a business unit or the entire company).
Regarding Claim 14, Knijnik/McGee/Grimaldi teaches the limitations of Claim 14 which state
wherein the operational data comprises an order in which the one or more tasks are completed or performed (Knijnik: Para 0030 via an analysis module that configures the processor to generate one or more ordered sequences of tasks to be completed by the first resource, wherein each of the one or more ordered sequences begins with the first resources’ start time, ends with the first resource's end time, includes at least a portion of the plurality of task requirements, and wherein consecutive tasks in a ordered sequence define the availability of the first resource, wherein the analysis module further configures the processor to calculate a total duration for each of the one or more ordered sequences).
Regarding Claim 15, Knijnik/McGee/Grimaldi teaches the limitations of Claim 15 which state
the predicted time of completion of the one or more modified tasks is continuously updated utilizing one or more algorithms (McGee: Para 0046-0047 via Based on a command (e.g., an operator command) to continue performance of the task (e.g., via the operator controls 202), the controller 114 may obtain the stored information identifying the association between the updated estimated completion time and the updated progress, and cause the machine 100 to continue performance of the task in the autonomous mode. The controller 114 may cause the task to be continued with the updated estimated completion time (e.g., the task is continued with the updated estimated completion time displayed, or otherwise notified to a human operator, a supervisor, a device that performs scheduling, and/or the like). Moreover, performance of the task is continued using the association between the updated estimated completion time and the updated progress of the task. For example, the controller 114 may determine a location associated with the amount of progress, and determine that the estimated completion time from the location (based on the amount of progress) is the updated estimated completion time. After the task is completed, the controller 114 may perform one or more actions. An action may include generating, or updating, a schedule based on completion of the task. An action may include transmitting a notification (e.g., to a user device, a server device, and/or the like) that indicates completion of the task. An action may include determining an actual time that was taken to complete the task, which may be used to refine an algorithm or model used to determine an estimated completion time. An action may include generating a report that details the task, the estimated completion time, an actual completion time, discontinuations and/or continuations of the task, and/or the like, which may be used to identify optimal machine utilization, inefficiencies, and/or the like).
Regarding Claim 16, Knijnik/McGee/Grimaldi teaches the limitations of Claim 16 which state
updating the position of the at least portion of the one or more processes of the modified workflow map comprises adjusting an order relating to the position of the one or more graphical representations of the one or more processes on the GUI (Grimaldi: Para 0055 via In some aspects, the user interface of computing device 104 may provide a graphical overview to users 102 who log in to system 100 to check the status of vehicle 138. The graphical overview may show the list of services that have been requested for vehicle 138, which services have been completed, which services are currently being performed, which department/vendor currently has possession of vehicle 138, and any similar information as may become apparent to those skilled in the relevant art(s) after reading the description herein. Likewise, the length of time vehicle 138 has spent in the workflow of services may be displayed, as well as how long each completed service has taken, how long the currently performed service is taking, and approximately how long the remaining services will take, either individually or as a whole).
Regarding Claim 17, Knijnik/McGee/Grimaldi teaches the limitations of Claim 17 which state
further comprising updating staffing of the one or more agents for the one or more tasks to generate the one or more modified tasks (Knijnik: Para 0136 via There is a possibility of setting the system for randomly choosing quality assurance tasks. Another variation is having the project manager and production manager role being done by the same person. It is also possible to have another individual in charge of reviewing and adjusting schedules online for a project team, a business unit or the entire company).
Regarding Claim 18, Knijnik/McGee/Grimaldi teaches the limitations of Claim 18 which state
further comprising modifying a scheduling of the one or more agents for one or more tasks to generate the one or more modified tasks (Knijnik: Para 0131 via If the production manager thinks the duration for the task has to be changed, the production manager may submit a change request (more time or less time) to the system 100 and the system 100 will adjust the deadline for that task and make any necessary adjustments for related tasks and any necessary adjustments to the schedule of the professional. The production manager can also change the order of items. Every day the system only allows task duration change requests until a particular time (Such as 2 pm). At that point, the system stops accepting change requests for some time and adjusts the schedules and deadlines for affected professionals and related tasks).
Regarding Claim 19, Knijnik/McGee/Grimaldi teaches the limitations of Claim 19 which state
further comprising utilizing one or more algorithms to modify the scheduling of the one or more agents for the one or more tasks to generate the one or more modified tasks (Knijnik: Para 0070 via the machine learning engine continually updates and improves its ability to forecast what is likely to happen with respect to the selection of a particular parameter for a particular action (e.g., the assignment of a particular resource to a particular task), and can therefore determine, for example, the best resources to allocate for each task, which resources are likely to work best together for completion of a particular task, how long each kind of engagement, project, or task will take, and forecast demand for services and sales).
Regarding Claim 20, Knijnik/McGee/Grimaldi teaches the limitations of Claim 20 which state
wherein modifying the scheduling of the one or more tasks comprises dynamic task scheduling that is implemented via an algorithm, machine learning, artificial intelligence, or heuristics, wherein said dynamic task scheduling permits real-time modifications or adjustments of customer appointment times to enhance service team and customer timing coordination and efficiency (Knijnik: Para 0008 via The system disclosed herein is capable of collecting feedback based on historical data and tracking variable resource characteristics, and applying machine learning algorithms in order to automatic the allocation of resources to tasks. In this way, the system avoids the need for manual task allocation. The system learns each resource's capability and customizes scheduling needs based on a resource's characteristics and availability).
Regarding Claim 21, Knijnik/McGee/Grimaldi teaches the limitations of Claim 21 which state
wherein the one or more training programs comprise tools, lessons, or reference materials on how to complete or perform one or more tasks associated with the extracted one or more processes or the business characteristic of the target business (Knijnik: Para 0084 via Once the list of items to be completed to accomplish the task is received by the system 100, the system 100 prompts the user to go through a checklist to ensure that all information necessary to complete each item of the task is available for use by the individual assignees of the items. For example, as shown in FIG. 8, the system prompts the user go through a checklist to ensure that the actual architectural design materials provide sufficient information to complete each item of the task, so that none of the resources assigned to a task must go looking for additional information to complete an item after starting the item).
Regarding Claim 22, Knijnik/McGee/Grimaldi teaches the limitations of Claim 22 which state
wherein the one or more training programs are assignable to one or more agents of the target business, wherein the one or more agents comprise an employee, an independent contractor, a worker, a manager, or a business owner of the target business (Knijnik: Para 0074 via Using the system 100, a user is able to create a project object in the system. A user, such as a project manager or sales representative, accesses the system 100 through a GUI presented on the display of the user device. The server machine 102 provides the user with an interface and prompts the user to input information concerning a new project, including a description of the project, the type of project, the total labor force (in hours or days) needed for the project, the total cost for the project, the start date, and the deadline date. After a user enters certain information corresponding to the creation of a project, such as the type of project, the description of the project, or both, the server machine 102 calls the best practices engine to determine, based on the type of project and an analysis of the description of the project, tasks to complete the project and provides the proposed tasks to the user to confirm their use in the project. The user confirms that the tasks are correct or makes modifications to the tasks or portions thereof in accordance with the task creation process, whereupon the tasks are created in the system, assigned to resources, and monitored in accordance with the description herein).
Regarding Claim 25, Knijnik/McGee/Grimaldi teaches the limitations of Claim 25 which state
further comprising providing dynamic coupled multitask guidance based on the modified workflow map to enhance service team timing, coordination, and efficiency (Knijnik: Para 0108 via The system 100 analyzes the level of expertise required for a particular task and generates a list of all possible resources. In the next step, the system 100 chooses the preferred resource for that task, based on the following guidelines: the resource shall have at least the level of skills needed; the resource shall be from the team that is involved in the project; the resource shall have completed the last task done in the project (assuming that this is not the first task of the project, in which case this guideline would not apply)).
Regarding Claim 26, Knijnik/McGee/Grimaldi teaches the limitations of Claim 26 which state
wherein the multitask guidance is provided by or generated using one or more algorithms, machine learning, artificial intelligence, or heuristics (Knijnik: Para 0069 via The program controller 128 in the server machine 120 coordinates readings of the database storage 104 and warehouse database 106 through the use of a machine learning engine, which may be based upon a commercial artificial intelligence (AI) tool 136. The machine learning engine monitors inputs to the system and monitors associations between inputs at all times. The machine learning tool also receives and executes pre-defined rules customized for the creation of system objects (e.g., projects, tasks, resources) and variables associated with those objects, and the execution and association of objects with one another, such as, for example, the task allocation process. These pre- determined rules may be continuously improved by experts to create best practices with the assistance of the machine learning engine. The machine learning engine analyzes data corresponding to the input and creation of tasks and the results thereof, and identifies associations between inputs and positive results. The results of this analysis and identification are stored by the machine learning engine as optimized parameters (also referred to as recommended behavior parameters) for each action taken by a user in connection with the project management system (e.g., the task generation or task allocation actions). When a user executes any action within the system, the program controller 128 in the server machine 120 runs a best practice engine 134 that searches the database storage 104 for the optimized parameters generated and stored by the machine learning engine. The best practices engine 134 reads the optimized parameters from the database storage 104 and displays such parameters on the GUI of the user device as recommended best practices for a particular action. Alternatively, a user may utilize the GUI to call up an action “wizard” for a particular action, which causes the system to invoke the best practices engine and provide optimized parameters for the action).
Claim(s) 23-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Knijnik et al. (US 2017/0249574 A1) in view of McGee et al. (US 2021/0278841 A1) in view of Grimaldi et al. (US 2016/0162817 A1) further in view of Marin (US 2009/0132309 A1).
Regarding Claim 23, while Knijnik/McGee/Grimaldi teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 23 which state wherein the one or more training programs are provided or accessible via wearable hardware.
Marin though, with the teachings of Knijnik/McGee/Grimaldi, teaches of
wherein the one or more training programs are provided or accessible via wearable hardware (Marin: Para 0022 via The user interface 104 includes a combination of input and output devices for interfacing with the host system 102. For example, user interface 104 inputs can include a keyboard, a keypad, a touch sensitive screen for inputting alphanumerical information, a VR glove, a motion- sensing device, a camera, a microphone, or any other device capable of producing input to the host system 102. Similarly, the user interface 104 outputs can include a monitor, a terminal, a liquid crystal display (LCD), stereoscopic technology, speakers, headphones, or any other device capable of outputting visual and/or audio information from the host system 102).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Knijnik/McGee/Grimaldi with the teachings of Marin in order to have wherein the one or more training programs are provided or accessible via wearable hardware. The motivations behind this being to incorporate the teachings of generating a 3-D environment. In addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 24, the combination of Knijnik/McGee/Grimaldi/Marin, teaches the limitation of Claim 24 which states
wherein the wearable hardware comprises a watch, augmented reality (AR) or virtual reality (VR) glasses, AR or VR goggles, or an AR or VR headset (Marin: Para 0022 via The user interface 104 includes a combination of input and output devices for interfacing with the host system 102. For example, user interface 104 inputs can include a keyboard, a keypad, a touch sensitive screen for inputting alphanumerical information, a VR glove, a motion-sensing device, a camera, a microphone, or any other device capable of producing input to the host system 102. Similarly, the user interface 104 outputs can include a monitor, a terminal, a liquid crystal display (LCD), stereoscopic technology, speakers, headphones, or any other device capable of outputting visual and/or audio information from the host system 102).
Conclusion
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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/T.E.S./ Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623