DETAILED ACTION
1. Claims 1-20 are pending.
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 .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/20/2026 has been entered.
Response to Arguments
3. Applicant's amendments and arguments to claims 1, 9, and 17 filed January 20, 2026 have been fully considered but they are not persuasive. Additionally, applicant’s arguments are rejected under a new ground of rejection necessitated by the amendment.
4. Applicant argues in remarks:
Claims 1-20 stand rejected under 35 U.S.C. § 103 as allegedly being unpatentable over DUNN (U.S. Patent Publication No. 2021/0303342), SHENK (U.S. Patent Publication No. 2017/0344931), and BOS (U.S. Patent Publication No. 2017/0068934). Applicant respectfully traverses the rejection.
For at least the reasons presented in the interview and without acquiescing in the Examiner's rejection, the cited sections of the applied references, whether taken alone or in any reasonable combination, do not disclose at least "wherein the task velocity score is determined based upon task backlog, wherein the task backlog, automatically determined by the machine learning engine, is an amount of time for the first user to complete one or more previously- identified tasks that have not been completed," as recited in claim 1, as amended. Independent claims 9 and 17, as amended, recite similar features. Therefore, independent claims 1, 9, and 17, and the claims that depend thereon, are patentable over the cited sections of the applied references.
Accordingly, Applicant respectfully requests that the Examiner reconsider and withdraw the rejection of claims 1-20 under 35 U.S.C. § 103 based on DUNN, SHENK, and BOS.
5. Examiner agrees that the current rejection does not cover the newly added limitations. However new prior art, in combination with the current prior art rejection, has been added to cover the scope of the newly added limitations.
6. As described below, Dunn teaches of monitoring device communications and identifying ad-hoc tasks for NLP processing ([0027]; [0065]). Moreover, Shenk teaches of identifying a platform for the ad-hoc tasks and determining a completion status of the ad-hoc tasks ([0014]; [0029]). Lastly, Bos teaches of reserving a timeslot in a calendar/scheduling application based on whether the task should be rescheduled or if more time is needed to finish it. Together, Dunn, Shenk, and Bos teach of identifying ad-hoc tasks, using NLP in order to appropriately analyze the tasks, identify a platform for the task, and reserving a timeslot in a calendar/scheduling application based on the tasks’ determined completion status. Similarly, Song teaches of reducing a task backlog by determining the best way to prioritize tasks without needed to unnecessarily allocate additional resources. Song teaches of assigning backlog tasks a score that is determined by how much time a user needs to complete a backlog task(s). In combination with Dunn, Shenk, and Bos, it would make sense that scheduling ad-hoc tasks according to a velocity score would include accounting for how long it would take a user to complete a backlog task. This would help appropriately schedule the task in a timeslot that is long enough to complete the task, and additionally, embodiments herein can help reduce a backlog, such as without increasing the number of resources, and can help determine whether a backlog task or new task is to be performed. These embodiments can dynamically determine (e.g., in real time, near real time, or periodically), given a resource and the tasks to be performed, whether it is more beneficial to perform a backlog task or a new task, as discussing in Song ([0018]). The full rejection can be found in the U.S.C. 103 Rejection section below.
7. Additionally, claims 2-8, 10-16, and 18-20 depend from and further limit amended claims 1, 9, and 17 and are therefore also rejected under 35 U.S.C 103. The full rejection can be found in the 35 U.S.C. 103 rejection section below.
Claim Rejections - 35 USC § 103
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.
8. Claims 1-4, 6-12, 14-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. US 20210303342 A1 in view of Bos US 20170068934 A1, in further view of Shenk et al. US 20170344931 A1, and in further view of Song et al. US 20190340030 A1.
9. With regard to claim 1, Dunn teaches:
A computer-implemented method within a computer hardware system including a machine learning engine, comprising:
monitoring, by a device, a plurality of electronic communications between client devices including a first client device of a first user ([0065] The computing device 1300 may also have one or more input device(s) 1312 such as a keyboard, a mouse, a pen, a sound or voice input device, a touch or swipe input device, etc. The output device(s) 1314A such as a display, speakers, a printer, etc. may also be included. An output 1314B, corresponding to a virtual display may also be included. The aforementioned devices are examples and others may be used. The computing device 1300 may include one or more communication connections 1316 allowing communications with other computing devices 1350. Examples of suitable communication connections 1316 include, but are not limited to, radio frequency (RF) transmitter, receiver, and/or transceiver circuitry; universal serial bus (USB), parallel, and/or serial ports.);
identifying, by the device and using the machine learning engine, ad-hoc tasks for the first user by performing natural language processing of the plurality of electronic communications ([0027] The system 200 may also include determining a task intent 220 from the user provided input such that the appropriate application for the task can be identified and utilized. For example, the automation agent 106 may determine a task intent 220 based on input provided by the user or provided by the user's mobile device 102. For example, a task intent may be generated from an appointment in a user's calendar and may be coupled to or otherwise associated with a location, such as a conference room location or entity. That is, a data store including one or more appointments may be accessed and a data store including one or more conference rooms and/or conferences room locations may be accessed; based on the appointment and a location of the mobile device 102 in relation to a location associated with the appointment, one or more task intents, such as “attend meeting”, may be identified. Thus, based on the task intent 220, a task model for accomplishing the task intent 220 may be determined and/or retrieved. As another non-limiting example, semantic and/or textual analysis of a user supplied input, such as “Send flowers to mom for her birthday” may be performed such that the task intent 220, “send flowers,” may be identified. In some examples, natural language processing and/or natural language understanding may be utilized to determine a task intent.),
Dunn fails to explicitly teach wherein the identifying the ad-hoc tasks comprises: identifying, for each of the ad-hoc tasks, a platform, of one or more platforms, with which an activity is to be performed; monitoring, by the device, the one or more platforms associated with the ad-hoc tasks to determine a task completion status of the identified ad-hoc tasks; and automatically reserving, within a calendar/scheduling application of the first client device, a time slot for performing the ad-hoc tasks by the first user based upon a task velocity score.
However, in analogous art, Shenk teaches:
wherein the identifying the ad-hoc tasks comprises:
identifying, for each of the ad-hoc tasks, a platform, of one or more platforms, with which an activity is to be performed ([0014] As briefly described above, embodiments are directed to automatic task flow management across multiple platforms. In some examples, a user may be prompted to create a task upon analysis of an incoming or an outgoing communication (email, instant message, online conference, etc.) or a task automatically created upon inference; [0022] multiple platforms such as task management applications, calendar applications, communication applications; [0036] Diagram 400 shows a user experience 402 of an email application on a mobile platform. Embodiments may, of course, be implemented in other platforms and in conjunction with other applications such as instant messaging applications, online conferencing applications, application or data sharing applications, and calendar applications, for example.);
monitoring, by the device, the one or more platforms associated with the ad-hoc tasks to determine a task completion status of the ad-hoc tasks ([0029] Diagram 200 shows a communication 208 being sent or received by the user 210 through a communication application 202. The task management application 204 may include an inference engine 206, which may analyze the communication 208 to infer a task (and its attributes) and create the task upon confirmation or input from the user 210. The task management application may also receive input through a personal assistant (i.e., voice input). The task management application 206 may then instruct the calendar application 212 to create a calendar item. Upon reflow of the task by the task management application 206 in response to a user request, a conflicting calendar item being created, or the task not being completed at the scheduled time, the calendar application 212 may also reschedule the calendar item; Examiner’s Note: The task management application can schedule or reschedule tasks based on whether a task was completed at the scheduled time (task completion status).); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Dunn with the teachings of Shenk wherein the identifying the ad-hoc tasks comprises: identifying, for each of the ad-hoc tasks, a platform, of one or more platforms, with which an activity is to be performed; and monitoring, by the device, the one or more platforms associated with the ad-hoc tasks to determine a task completion status of the identified ad-hoc tasks. According to Shenk, the technical advantages of providing automatic task flow management across multiple platforms may include, among others, increased efficiency and collaboration through automated task management on multiple platforms such as task management applications, calendar applications, communication applications. User experience may also be enhanced through inference based automation of task management and scheduling operations, (Shenk, [0022]). Additionally, the set of tasks may be coordinated. For example, some tasks may depend on other tasks being completed. Thus, the scheduling and rescheduling of calendar items corresponding to the tasks may be performed with the dependency of the tasks in mind. If a task is not completed down-level tasks for the same or other users may be rescheduled or their priorities modified in response to the incomplete status of the original task. Furthermore, progress of related tasks may be provided to users to inform them and allow the users to plan their schedules (Shenk, [0034]). Not only is user experience enhanced but task dependency is taken into consideration, therefore, allowing the user’s tasks to be scheduled in an efficient manner for timely completion.
Although both Dunn and Shenk teach the idea of reserving in a calendar a timeslot to complete a task, they fail to explicitly teach automatically reserving, within a calendar/scheduling application of the first client device, a time slot for performing the ad-hoc tasks by the first user based upon a task velocity score.
However, in analogous art, Bos teaches:
automatically reserving, within a calendar/scheduling application of the first client device, a time slot for performing the ad-hoc tasks by the first user based upon a task velocity score ([0033] At the end of the time allocated for completing the task, the user receives a user interface prompt asking if the task is complete, if it should be rescheduled or deferred, or if more time is required to complete the task. Based on user feedback, the system will automatically find the next suitable time slot and move other tasks in the calendar based on the priority of those tasks or other factors. The user will also have the option to set tasks as “public” which will block off time in the calendar to prevent another meeting from conflicting with the work time or “private” which will allow other meetings automatically to move the task to another available (i.e. “open”) timeslot. Other features will be appreciated in view of the following disclosure; [0113] At 520, in response to a value other than the first value (e.g., “Not Completed” or one of “Not Started”, “In Progress”, “Waiting on someone else” or “Deferred”) being set as the value of the task status field 318, the event scheduler 290 updates the task status field 318 in the corresponding task item. It will be appreciated that a status other than the first value, i.e. other than “Completed”, indicates that the task is uncompleted and that additional time to complete the task should be allocated and/or a new due date should be set; Examiner’s Note: Because a task has not been completed, it is allocated additional time or rescheduled. This is due to its incompleteness, which under broadest reasonable interpretation is a type of backlog. Additionally, due to the fact that there are uncompleted tasks, the rate that tasks are going to be completed is slower, which is indicated by the additional time needed to complete it. These factors are analogous with the task velocity. The task can be scheduled due to these factors.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Dunn and Shenk with the teachings of Bos to automatically reserving, within a calendar/scheduling application of the first client device, a time slot for performing the ad-hoc tasks by the first user based upon a task velocity score. The task velocity score can help determine when to schedule a task. By taking into consideration backlog and how quickly a task is being performed, it ensures that tasks are completed in the most efficient way possible. For example, in Bos, it is described that the user receives a user interface prompt asking if the task is complete, if it should be rescheduled or deferred, or if more time is required to complete the task. Based on user feedback, the system will automatically find the next suitable time slot and move other tasks in the calendar based on the priority of those tasks or other factors ([0033]). By ensuring that factors of tasks are taken into consideration, the user is able to guarantee their tasks are scheduled for times that ensure their timely completion.
Although Dunn, Shenk, and Bos teach of monitoring a plurality of electronic communications between client devices; identifying ad-hoc tasks by performing NLP; identifying a platform associated with the ad-hoc tasks; determine a completion status of the task; and automatically reserving in calendar/scheduling application a time slot to perform the ad-hoc tasks based on a task velocity score, they fail to teach wherein the task velocity score is determined based upon task backlog, wherein the task backlog, automatically determined by the machine learning engine, is an amount of time for the first user to complete one or more previously-identified tasks that have not been completed.
However, in analogous art, Song teaches:
wherein the task velocity score is determined based upon task backlog, wherein the task backlog, automatically determined by the machine learning engine, is an amount of time for the first user to complete one or more previously-identified tasks that have not been completed ([0029] The task score circuitry 112 can produce a score for a given task, whether the task is a new task 104, 116, or a backlog task 106 or 117. The task score circuitry 112 can implement a machine learning model that considers a time-dependent feature (e.g., a difference between a current time of day and a time of day the task is most likely to be completed) in determining the score of the task. The task score circuitry 112 can determine the score for each of the tasks in the new task queue 108 and the backlog task queue 110. The task score circuitry 112 can determine the score periodically, such as every minute, five minutes, ten minutes, fifteen minutes, twenty minutes, etc. or some time therebetween. The task score circuitry 112 can update a task score value of a task score field 208 (see FIG, 2) of an entry in the new task queue 108 or the backlog task queue 110.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Dunn, Shenk, and Bos with the teachings of Song wherein the task velocity score is determined based upon task backlog, wherein the task backlog, automatically determined by the machine learning engine, is an amount of time for the first user to complete one or more previously-identified tasks that have not been completed. As described above, Dunn teaches of monitoring device communications and identifying ad-hoc tasks for NLP processing ([0027]; [0065]). Moreover, Shenk teaches of identifying a platform for the ad-hoc tasks and determining a completion status of the ad-hoc tasks ([0014]; [0029]). Lastly, Bos teaches of reserving a timeslot in a calendar/scheduling application based on whether the task should be rescheduled or if more time is needed to finish it. Together, Dunn, Shenk, and Bos teach of identifying ad-hoc tasks, using NLP in order to appropriately analyze the tasks, identify a platform for the task, and reserving a timeslot in a calendar/scheduling application based on the tasks’ determined completion status. Similarly, Song teaches of reducing a task backlog by determining the best way to prioritize tasks without needed to unnecessarily allocate additional resources. Song teaches of assigning backlog tasks a score that is determined by how much time a user needs to complete a backlog task(s). In combination with Dunn, Shenk, and Bos, it would make sense that scheduling ad-hoc tasks according to a velocity score would include accounting for how long it would take a user to complete a backlog task. This would help appropriately schedule the task in a timeslot that is long enough to complete the task, and additionally, embodiments herein can help reduce a backlog, such as without increasing the number of resources, and can help determine whether a backlog task or new task is to be performed. These embodiments can dynamically determine (e.g., in real time, near real time, or periodically), given a resource and the tasks to be performed, whether it is more beneficial to perform a backlog task or a new task, as discussing in Song ([0018]).
10. With regard to claim 2, Dunn further teaches:
wherein identifying the ad-hoc tasks further comprises:
identifying for each of the ad-hoc tasks a user assigned to the activity ([0027] As another non-limiting example, semantic and/or textual analysis of a user supplied input, such as “Send flowers to mom for her birthday” may be performed such that the task intent 220, “send flowers,” may be identified; Examiner’s Note: There is a user associated with the task of sending flowers.).
11. With regard to claim 3, Bos further teaches:
wherein identifying the ad-hoc tasks further comprises:
identifying for each of the ad-hoc tasks:
a timeframe during which the ad-hoc task is to be performed ([0092] For example, calendar events associated with tasks having a due date may be scheduled to be completed a threshold duration before the due date of the respective tasks to give the user a buffer in case the task takes more time to complete than initially thought, and additional time to complete the task is required. For example, a threshold duration of a number days could be applied, e.g. 2 days; Examiner’s Note: The timeframe is 2 days before the deadline.), and
an urgency level assigned to the ad-hoc task ([0093] Also as described above, tasks may have a priority specified by a priority field 316. The value of the priority is one of “Low”, “Normal” or “High” in some embodiments. In such embodiments, the one or more scheduling rules may include a priority rule which specifies that new calendar events associated with the one or more tasks are automatically populated in the calendar database so that calendar events associated with tasks having a priority are scheduled in accordance with an order of priority based on the priority associated with the one or more tasks, for example, a decreasing order of priority (e.g., from High to Low). For example, “High” priority tasks may be automatically populated first, followed by “Normal” priority tasks, followed last by “Low” priority tasks. This allows higher priority tasks to be scheduled before lower priority tasks.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Dunn and Shenk with the teachings of Bos wherein the identifying the ad-hoc tasks further comprises: identifying for each of the ad-hoc tasks: a timeframe during which the ad-hoc task is to be performed. By ensuring tasks are scheduled according to a timeframe and urgency level, it ensures tasks are scheduled with enough time for completion before a deadline, as discussed in Bos ([0092], [0093]).
12. With regard to claim 4, Dunn further teaches:
further comprising:
training the machine learning engine based upon the identifying ([0033] As a machine learning algorithm 510 may be utilized to generate an application model from the semantic representation of the app, the machine learning algorithm 510 may be trained utilizing data acquired from user/app interaction. In some examples, an application model may be generated by one or more services; in other examples, the application model may be trained with data obtained from the mobile device 102 and may be specific to the mobile device 102 and the user; [0035] An appropriate task model may then be generated if one does not already exist, or otherwise be retrieved from a storage location, such as the database 226. The automation agent 106 may process the input 620 to not only identify the task intent, but additional details or context associated with the task intent. For example, one or more natural language processing techniques may be utilized to identify the task intent 624, a type of food 628, a reservation size 632, a location 640, and/or a data/time 636. Accordingly, a task model 644 may be generated or otherwise obtained based on such information. That is, the task model 644 may include contextual information for the task intent.).
13. With regard to claim 6, Bos teaches:
wherein a graphical user interface of the first client device is configured to allow the first user to select applications to be monitored for the electronic communications ([0065] FIG. 3A shows a diagrammatic view of a task user interface 300 for a task application in accordance with one example embodiment of the present disclosure. The task user interface 300 may be used to create and store a new task or edit an existing task; [0073] The privacy field 320 is a dropdown list from which a privacy setting can be selected from a number of predetermined privacy setting options. The privacy field 320 has at least two privacy field options. In a relatively simple embodiment, the privacy field 320 has a value of “Private” or “Public”. A task having the privacy setting of “Public” is published to a public calendar (if any) maintained by the PIM server 132 and can only be moved by the user and not the event scheduler 290. Thus, tasks having the privacy setting of “Public” can be identified by the event scheduler 290 as not moveable. In contrast, tasks having the privacy setting of “Private” are not published to a public calendar (if any) and can be moved by either the event scheduler 290 or the user. Thus, tasks having the privacy setting of “Private” can be identified by the event scheduler 290 as moveable. It will be appreciated that calendar events associated with private tasks may be stored in the calendar database 294 of the user, or private tasks may be stored in other databases and marked as “Private”. The privacy setting, in addition to determining whether a calendar event associated with a task is published to a public calendar, provides a mechanism for identifying moveable calendar events; Examiner’s Note: The task user interface is a type of graphical user interface. It allows the user to edit or create tasks. One way to edit a task is to edit its privacy setting. When a task is set as public, it is unmovable. When a task is unmovable the event scheduler cannot change them, and they need to be manually modified by the user. Since the event scheduler does not interfere with public tasks, it is not monitoring them to be rescheduled or edited.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Dunn and Shenk with the teachings of Bos wherein a graphical user interface of the first client device is configured to allow the first user to select applications to be monitored for the electronic communications. By allowing the user to select which applications are being monitored for communications, it gives the users the option for privacy. As discussed in Bos, when a user does not what a task to be able to be moved by the event scheduler, the user can set the task to “Public” through the task user interface ([0073]). This ensures that only the user can edit that task if it needs to be moved. In this way, the public task is not monitored for additional updates or changes.
14. With regard to claim 7, Bos further teaches:
further comprising:
pushing a notification to an application executing with the first client device based upon the application being a platform, of the one or more platforms, for performing at least one of the ad-hoc tasks ([0033] An order of priority of the tasks and/or a task deadline may also be provided. The application will then populate the calendar with events to occupy empty time slots, for example, during the week. Users may also drag and drop the tasks into the calendar to create calendar events as desired. Users will receive a timely notification to remind them to start the task when appropriate.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Dunn and Shenk with the teachings of Bos further comprising: pushing a notification to an application executing with the first client device based upon the application being a platform, of the one or more platforms, for performing at least one of the ad-hoc tasks. In this example above, the platform is a calendar app. There is a notification pushed to the user to remind them to complete tasks, as discussed in Bos ([0033]). This ensures that the user is reminded to complete tasks that need to be addressed; therefore, ensuring that the user completes the tasks in a timely manner.
15. With regard to claim 8, Bos further teaches:
wherein the automatic reserving includes associating at least a portion of the ad-hoc tasks with the time slot (0012] In accordance with an example embodiment of one aspect of the present disclosure, there is provided a method of automatic scheduling on an electronic device, comprising: receiving one or more tasks associated with a user, wherein each task has an associated duration; determining available timeslots in a calendar database associated with the user; and automatically populating one or more of the available timeslots in the calendar database associated with the user with new calendar events associated with the one or more tasks in accordance with one or more scheduling rules.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Dunn and Shenk with the teachings of Bos wherein the automatic reserving includes associating at least a portion of the ad-hoc tasks with the time slot. Once a task is scheduled, it is allotted time in a calendar database associated with the user, as discussed in Bos ([0012]). By allocating a dedicated time for completion of a task, a user can ensure that tasks are completed on time.
16. Regarding claim 9, it is rejected under the same reasoning as claim 1 above. Therefore, it is rejected under the same rationale.
17. Regarding claim 10, it is rejected under the same reasoning as claim 2 above. Therefore, it is rejected under the same rationale.
18. Regarding claim 11, it is rejected under the same reasoning as claim 3 above. Therefore, it is rejected under the same rationale.
19. Regarding claim 12, it is rejected under the same reasoning as claim 4 above. Therefore, it is rejected under the same rationale.
20. Regarding claim 14, it is rejected under the same reasoning as claim 6 above. Therefore, it is rejected under the same rationale.
21. Regarding claim 15, it is rejected under the same reasoning as claim 7 above. Therefore, it is rejected under the same rationale.
22. Regarding claim 16, it is rejected under the same reasoning as claim 8 above. Therefore, it is rejected under the same rationale.
23. Regarding claim 17, it is rejected under the same reasoning as claim 1 above. Therefore, it is rejected under the same rationale.
24. Regarding claim 18, it is rejected under the same reasoning as claims 2 and 3 above. Therefore, it is rejected under the same rationale.
25. Regarding claim 20, it is rejected under the same reasoning as claim 7 above. Therefore, it is rejected under the same rationale.
26. Claims 5, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. US 20210303342 A1; Bos US 20170068934 A1; Shenk et al. US 20170344931 A1; and Song et al. US 20190340030 A1, as applied in claim 1, in further view of Ikkaku et al. US 20180341527 A1.
27. With regard to claim 5, Dunn, Shenk, Bos, and Song teach the method of claim 1 but fail to explicitly teach wherein the task velocity score is further determined based upon task velocity.
However, in analogous art, Ikkaku teaches:
wherein the task velocity score is further determined based upon task velocity ([0263] Next, the task deployment apparatus 100 refers to the queue state DB 900, and calculates a task execution completion time E=(K+1)/μ based on the number of tasks K+1 and the average process execution rate μ in the CPU 801 so as to calculate a task execution completion point (step S3502). The task deployment apparatus 100 finishes the task execution completion point calculation process; Examiner’s Note: The average process execution rate μ is analogous with task velocity.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Dunn, Shenk, Bos, and Song with the teachings of Ikkaku wherein the task velocity score is further determined based upon task velocity. The task velocity score can help determine when to schedule a task. By taking into how quickly a task is being performed, it ensures that tasks are completed in the most efficient was as possible. For example, in Ikkaku, a task execution time is calculated based on the number of tasks and average process execution rate ([0263]). By ensuring that factors of tasks are taken into consideration, the user is able to guarantee their tasks are scheduled for times that ensure their timely completion. For example, an execution completion point of a process executed by the calculation device is estimated based on the number of processes in the queue and a process execution rate in the calculation device. According thereto, a process can be deployed to a calculation device which appears to complete execution of the process by a predetermined time limit, as discussed in Ikkaku ([0061]).
28. Regarding claim 13, it is rejected under the same reasoning as claim 5 above. Therefore, it is rejected under the same rationale.
29. Regarding claim 19, it is rejected under the same reasoning as claim 5 above. Therefore, it is rejected under the same rationale.
Conclusion
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/AN-AN NGOC NGUYEN/Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195