DETAILED ACTION
This communication is a Non-Final Office Action rejection on the merits. Claims 21-40 are currently pending and have been addressed below.
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 .
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 06/15/2025 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,205,146 B2 and claim 21-40 of Application No. 17/542,670. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of ‘146 and claims 21-40 of ‘670 are narrower as they further describe an overlay graphical object having the length that is determined in accordance with the predicted range.
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.
Claims 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 21 is directed to a method which is a statutory category.
Step 2A, Prong One - Claim 21 recites: A method comprising: causing display a first work item, the first work item depicting: a scheduled start date of the first work item; a scheduled end date for the first work item; a start date adjustment; and an end date adjustment, wherein in response to an input at the end date adjustment: a new scheduled end date is generated based on the input; and the new scheduled end date is transmitted; generating after the input is received, forecast data based on the first work item and the new scheduled end date; determining, based on the forecast data: a forecast range based at least in part on a start date of a second work item associated with the first work item; and a forecast confidence interval for the forecast range; and causing display of a forecast indicating the forecast confidence interval and the forecast range. These claim elements are considered to be abstract ideas because they are directed to a method of organizing human activity which includes managing personal behavior. Providing a forecast range based on historical data is a form of managing personal behavior because it allows the program manager to track the schedule of a project and determine whether the forecast end data is past the project completion date. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 21 includes additional elements: a computer; a user computer; a user interface; a project management server application hosted by a server; a scheduling interface; a first work item user interface element; stored by a data storage system; an adjustment control; and a forecasting server; and a forecast user interface element.
The computer and the project management server are merely used to implement embodiments and/or features of the present disclosure (Paragraph 0059 & Paragraph 0075). The server is merely used to host a project management server application (Paragraph 0059). The scheduling interface is merely used to display all the work item user interface elements (Paragraph 0093). The work item user interface element is merely used to display the start day and the end day of a work item and to change the start day and the end day of a work item (Paragraph 0102 & Paragraph 0106). The data storage system is merely used to save changes to a scheduled start or end date (Paragraph 0123). The adjustment control is merely used to adjust the scheduled start and end of the work item (Paragraph 0122). The forecasting server is merely used to generate the forecasting models (Paragraph 0261). The forecast user interface element is merely used to display a forecast start date range and a forecast end date range (Paragraphs 0213-0215). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). The computer components are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Also, the interface elements (scheduling interface, work item user interface, adjustment control, and forecast user interface element) are considered “field of use” since they’re just used to receive updated dates and display information, but does not improve the interface (MPEP 2106.05h). Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of calculating and displaying a forecast range in a user interface. The specification shows that the computer and the project management server are merely used to implement embodiments and/or features of the present disclosure (Paragraph 0059 & Paragraph 0075). The scheduling interface is merely used to display all the work item user interface elements (Paragraph 0093). The work item user interface element is merely used to display the start day and the end day of a work item and to change the start day and the end day of a work item (Paragraph 0102 & Paragraph 0106). The data storage system is merely used to save changes to a scheduled start or end date (Paragraph 0123). The adjustment control is merely used to adjust the scheduled start and end of the work item (Paragraph 0122). The forecasting server is merely used to generate the forecasting models (Paragraph 0261). The forecast user interface element is merely used to display a forecast start date range and a forecast end date range (Paragraphs 0213-0215). Also, the interface elements (scheduling interface, work item user interface, adjustment control, and forecast user interface element) are considered a conventional computer function of “mere data gathering” and “performing repetitive calculations” since they’re just used to receive updated scheduling information for calculating an updated forecast/risk analysis (MPEP 2106.05d). Thus, nothing in the claim adds significantly more to an abstract idea. The claim is ineligible.
Independent claim 28 is directed to a system at step 1, which is a statutory category. Claim 28 recites similar limitations as claim 21 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 28 further recites: a processor; and a memory – which are treated as just an explicit “processor/computer” for executing the operations and are treated under MPEP 2106.05f in the same manner as claim 21. Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, the claim is ineligible.
Independent claim 35 is directed to a method at step 1, which is a statutory category. Claim 35 recites similar limitations as claim 21 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is ineligible.
Dependent claims 22, 24, 26, 31-32, 36, and 39 are not directed to any additional claim elements. Rather, these claims offer further descriptive functions of elements found in the independent claims and addressed above - such as: wherein the forecast range is further based on a set of additional work items having dependency relationships with the first work item; wherein the first work item and the second work item have a dependency relationship; and in response to a break dependency relationship input or scheduling information update associated with the first work item and the second work item, the method includes determining an updated forecast range; wherein the particular forecast start data is associated with a confidence interval; and the forecast user interface element further indicates a planned end date of the second work item and a forecast end date range generated based on the set of forecast start dates. In this case, the main functions are merely used to: collect data (e.g., receive scheduling dependency information); analyze the data (e.g., calculate an updated forecast range); and display certain results of the collection and analysis (e.g., display the updated forecast range). Those are functions that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 25-27, 33-34, and 37-38 are directed to additional elements such as: a forecast model; and a static error model. The forecast model is merely used to calculate forecast data for each work item (Paragraph 0247). The static error model is merely used to use the static error value (or values) is/are used to calculate forecast start/end dates (or ranges) for the work item (Paragraph 0265). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 23, 30, and 40 are directed to additional functions of the forecast user interface. The forecast user interface is further used to display an alert indicating to the user that the changes have resulted in a scheduled-broken dependency (Paragraph 0176); and display of the set of forecast start date ranges and end date ranges in response to receiving a user hover input with respect to the forecast user interface element (Paragraph 0226). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21-24, 28-29, 32, 35-36, and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Ponce de Leon (US 2008/0195452 A1), in view of Newpol et al. (US 2011/0302090 A1).
Regarding claim 21 (New), Ponce de Leon discloses a computer-implemented method comprising (Paragraph 0020, In accordance with a preferred embodiment hereof, this invention provides a planning method, relating to at least one project; Paragraph 0132, FIG. 12 shows an overview schematic representation of the computer hardware and Internet environment utilized by the Interactive Graphics-Based Planning System according to a preferred embodiment of the present invention):
causing display, on a user interface communicably coupled to a project management server application hosted by a server, of a scheduling interface including a first work item user interface element corresponding to a first work item stored by a data storage system of the project management server application, the first work item user interface element depicting (Figure 10, Activity 1; Paragraph 0031, and storing at least one project plan, wherein such at least one project plan comprises at least at least two of such activities relating to at least one project, such position of such at least one symbol representing such at least one activity on such selected at least on time-scaled calendar, such at least one initial estimated duration relating to such at least one activity; Paragraph 0181, Application Server; Paragraph 0166, Referring to FIG. 10, which shows a simplified sample project schedule created through the use of the interactive planning and scheduling method before re-positioning one or more activities provided by the Interactive Graphics-Based Planning System 100 according to a preferred embodiment of the present invention):
a scheduled start date of the first work item (Figure 10, Activity 1 Start Date, 12/30; Activity 1 End Date, 1/19);
a scheduled end date for the first work item (Figure 10, Activity 1 Start Date, 12/30; Activity 1 End Date, 1/19);
a start date adjustment control (Figure 2A and related text in Paragraph 0141, In principle, predecessor Activity 201 has three possible connect points, that is: Start Node (“I” node 209); Embedded Node 211; and Finish Node (“J” node 210); Paragraph 0154, Interactive Graphics-Based Planning System 100 enables users to make their own choices as to which project activities to move to earlier or later time frames, “crash” or extend to improve manpower loading and optimize time-dependent costs; Table 2, Activity 201 Attributes Activity, I-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node);
and an end date adjustment control, wherein in response to an input at the end date adjustment control (Figure 2A and related text in Paragraph 0141, In principle, predecessor Activity 201 has three possible connect points, that is: Start Node (“I” node 209); Embedded Node 211; and Finish Node (“J” node 210); Paragraph 0154, Interactive Graphics-Based Planning System 100 enables users to make their own choices as to which project activities to move to earlier or later time frames, “crash” or extend to improve manpower loading and optimize time-dependent costs; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node):
a new scheduled end date is generated based on the input (Figure 2A and related text in Paragraph 0141, In principle, predecessor Activity 201 has three possible connect points, that is: Start Node (“I” node 209); Embedded Node 211; and Finish Node (“J” node 210); Paragraph 0154, Interactive Graphics-Based Planning System 100 enables users to make their own choices as to which project activities to move to earlier or later time frames, “crash” or extend to improve manpower loading and optimize time-dependent costs; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node);
and the new scheduled end date is transmitted to a forecasting server in communication with the project management server application and the user interface (Figure 10, Activity 1; Paragraph 0031, and storing at least one project plan, wherein such at least one project plan comprises at least at least two of such activities relating to at least one project, such position of such at least one symbol representing such at least one activity on such selected at least on time-scaled calendar, such at least one initial estimated duration relating to such at least one activity; Paragraph 0181, Application Server; Paragraph 0166, Referring to FIG. 10, which shows a simplified sample project schedule created through the use of the interactive planning and scheduling method before re-positioning one or more activities provided by the Interactive Graphics-Based Planning System 100 according to a preferred embodiment of the present invention; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node);
generating, at the forecasting server after the input is received, forecast data based on the first work item and the new scheduled end date (Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node; Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200; Examiner notes that probability density functions are re-calculated in response to changes in project task/activities. Therefore, new forecast data is generated every time that there’s a change in the schedule);
determining, based on the forecast data: a forecast range based at least in part on a start date of a second work item associated with the first work item stored by the data storage system (Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node; Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200; Examiner interprets the “duration probability distribution for each task/activity” as the “forecast range”); …;
and causing display, on the user interface, of a forecast … on the scheduling interface indicating … the forecast range (Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200. Through Monte Carlo simulation techniques, iterating many times through the plan, Interactive Graphics-Based Planning System 100, preferably calculates the corresponding probability distribution function for the plan overall completion, as well as to what extent the activities turned out as having zero float (commonly referred to as activity criticality indexes). Using this highly preferred feature of Interactive Graphics-Based Planning System 100 allows the evolving plan to be risked at any time, further allowing the project to be "reworked" to attain a better risk profile. The above-described cost optimization feature of Interactive Graphics-Based Planning System 100 at least embodies herein; associating duration probability distribution functions, by such at least one project planner using such at least one computational device, with such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-positioning, by such at least one project planner using such at least one computational device, such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-calculating, by such at least one computational device, such duration probability distribution functions, relating to at least one such activity repositioning, to provide at least one project risk profile; and displaying, essentially continuously, by such at least one computational device, such at least one project risk profile on such at least one time-scaled calendar on such at least one graphical user interface; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node; Examiner interprets the “duration probability distribution for each task/activity” as the “forecast range”).
Although Ponce de Leon discloses all the limitations above, calculating a forecast range for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177), and causing a display of the forecasted range on a graphical user interface (see Paragraph 0177, display at least one project risk profile), Ponce de Leon does not specifically disclose a forecast user interface element on the scheduling interface indicating the forecast confidence interval and the forecast range.
However, Newpol et al. discloses and a forecast confidence interval for the forecast range; and causing display, on the user interface, of a forecast user interface element on the scheduling interface indicating the forecast confidence interval and the forecast range (Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task).
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It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted range for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the calculated forecasted range is displayed on a forecast user interface element of the invention of Newpol et al. because doing so would allow the method to provide a visual representation of uncertainty that can allow planners to quickly estimate expected completion dates and see where project dependencies have inordinately large uncertainties, all in a quick glance (See Newpol et al., Paragraph 0074). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 22 (New), which is dependent of claim 21, the combination of Ponce de Leon and Newpol et al. discloses all the limitations in claim 21. Ponce de Leon further discloses wherein: the forecast range is further based on a set of additional work items having dependency relationships with the first work item; the set of additional work items are each associated with a respective set of forecast ranges (Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200. Through Monte Carlo simulation techniques, iterating many times through the plan, Interactive Graphics-Based Planning System 100, preferably calculates the corresponding probability distribution function for the plan overall completion, as well as to what extent the activities turned out as having zero float (commonly referred to as activity criticality indexes). Using this highly preferred feature of Interactive Graphics-Based Planning System 100 allows the evolving plan to be risked at any time, further allowing the project to be "reworked" to attain a better risk profile. The above-described cost optimization feature of Interactive Graphics-Based Planning System 100 at least embodies herein; associating duration probability distribution functions, by such at least one project planner using such at least one computational device, with such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-positioning, by such at least one project planner using such at least one computational device, such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-calculating, by such at least one computational device, such duration probability distribution functions, relating to at least one such activity repositioning, to provide at least one project risk profile; and displaying, essentially continuously, by such at least one computational device, such at least one project risk profile on such at least one time-scaled calendar on such at least one graphical user interface; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node); ...
Although Ponce de Leon discloses all the limitations above, calculating a forecast range for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177), and causing a display of the forecasted range on a graphical user interface (see Paragraph 0177, display at least one project risk profile), Ponce de Leon does not specifically disclose determining a forecast confidence interval includes determining a set of alternative confidence intervals based on the set of additional work items and the respective sets of forecast ranges.
However, Newpol et al. discloses and determining the forecast confidence interval includes determining a set of alternative confidence intervals based on the set of additional work items and the respective sets of forecast ranges (Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted range for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate a forecast confidence interval of the invention of Newpol et al. because doing so would allow the method to know the degree of confidence the project adheres (see Newpol et al., Paragraph 0006). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 23 (New), which is dependent of claim 22, the combination of the combination of Ponce de Leon and Newpol et al. discloses all the limitations in claim 22. Ponce de Leon further discloses wherein: determining the forecast range includes determining … a forecast end date of the second work item based on the new scheduled end date of the first work item (Paragraph 0017, It is a further object and feature of the present invention that, in substantial and real time, as activities, relationship (logic) ties and milestone deadlines are changed, repositioned or as new activities, relationship (logic) ties and milestone deadlines are added to the plan, if probability distribution functions are associated with the activities, to provide such a system that calculates completion risk profiles and activity criticality indexes the simplest possible way; Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200. Through Monte Carlo simulation techniques, iterating many times through the plan, Interactive Graphics-Based Planning System 100, preferably calculates the corresponding probability distribution function for the plan overall completion, as well as to what extent the activities turned out as having zero float (commonly referred to as activity criticality indexes). Using this highly preferred feature of Interactive Graphics-Based Planning System 100 allows the evolving plan to be risked at any time, further allowing the project to be "reworked" to attain a better risk profile. The above-described cost optimization feature of Interactive Graphics-Based Planning System 100 at least embodies herein; associating duration probability distribution functions, by such at least one project planner using such at least one computational device, with such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-positioning, by such at least one project planner using such at least one computational device, such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-calculating, by such at least one computational device, such duration probability distribution functions, relating to at least one such activity repositioning, to provide at least one project risk profile; and displaying, essentially continuously, by such at least one computational device, such at least one project risk profile on such at least one time-scaled calendar on such at least one graphical user interface; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node; Examiner interprets the “probability distribution for the completion of each activity” as the “forecast end date”); ...
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Although Ponce de Leon discloses determining the forecast range includes determining a forecast end date of the second work item based on the new scheduled end date of the first work item (see Paragraph 0177, display at least one project risk profile such as completion risk profile), Ponce de Leon does not specifically disclose causing display of the forecast user interface element includes causing display of an information pane including an alert that the forecast end date of the second work item is after a scheduled end date of the second work item.
However, Newpol et al. further discloses wherein: determining the forecast range includes determining a forecast start date and a forecast end date of the second work item based on the new scheduled end date of the first work item (Paragraph 0012, What is needed is a way to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates, yet still retain the ease, familiarity, and timeline context of a Gantt-style representation; Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task; Paragraph 0066, However, in traditional project planning the start date of a task is also of interest. Since statistical distributions do not provide a fixed start date, and each task has only a duration estimate, it takes additional analysis to derive individual task start dates with the same overall specified confidence. For any given task, this is done by identifying the tasks that must be completed before the given task can begin. These tasks may be either `predecessor` tasks, whose output is used in the given task, or independent tasks assigned to the same person (assuming that multiple tasks assigned to the same person are performed in some planned order); Paragraph 0067, Once these pre-requisite tasks are identified, the statistical Completion modeling described above is used to compute the completion duration for the overall set of pre-requisites (i.e. the `pre-requisites end date`). Taking the specified confidence point of that function produces the start date of the given dependent task, at the specified confidence);
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and causing display of the forecast user interface element includes causing display of an information pane including an alert that the forecast end date of the second work item is after a scheduled end date of the second work item (Figure 6, Tracepoint; Paragraph 0108, The tracepoint display algorithm will obtain the combined CC curve for the associated tasks, and evaluate it at its completion target date. This value can be called the Target Probability (TP). Next, it applies specified thresholds to determine what state the tracepoint is in. For example, a tracepoint could be given upper and lower probability thresholds of 75% and 50%, to determine the result; Paragraph 0109, If TP is below the lower threshold, the tracepoint might show RED; if the TP is between the two thresholds, the tracepoint might show YELLOW, and if the TP is above the upper threshold, the tracepoint might show GREEN. Note that the choice of visual indication is arbitrary; various fill patterns could be used instead for monochromatic displays or print output. It can be noted that in this case, if the baseline duration is using the mean of the probability density function and the target value is set at .5, then any probability of completion that is less than 50% will result in a forecast end date that is past the baseline/scheduled end date. Therefore, a probability of completion less than 50% indicates that the work item (e.g. completion/end date) is forecast-broken, in which tracepoint might show red).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted completion/end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate an alert indicating that the forecast end date of the second work item is after a scheduled end date of the second work item of the invention of Newpol et al. because doing so would allow the user interface to include a tracepoint which can represent the completion probability of a task or set of tasks, against some targeted completion date (see Newpol et al., Paragraph 0107). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 24 (New), which is dependent of claim 23, the combination of Ponce the Leon and Newpol et al. discloses all the limitations in claim 23. Ponce the Leon further discloses wherein: the first work item and the second work item have a dependency relationship (Paragraph 0051, Arrow Diagramming Method (ADM). Activity-based network technique introduced with CPM in the late-50s. ADM uses arrows to represent activities, each arrow uniquely identified by the activity start node (also referred to as the I node or iNode) and the finish node (also referred to the J node or jNode). ADM restricts relationships from the predecessor finish jNode to the successor start iNode; thereby not permitting dependent activities to overlap, i.e., the start of the successor activity cannot be any earlier than the finish of the predecessor activity. On the other hand, if a number of activities (for example 2) share common successors (for example 3), all such 6 relationships interconnect efficiently through a single node: their common jNode); ....
Although Ponce de Leon discloses wherein: the first work item and the second work item have a dependency relationship (see Ponce de Leon, Paragraph 0051), Ponce de Leon and does not specifically disclose in response to a break dependency relationship input associated with the first work item and the second work item, the method includes determining an updated forecast range based on the broken dependency relationship.
However, However, Newpol et al. further discloses wherein: the first work item and the second work item have a dependency relationship; and in response to a break dependency relationship input associated with the first work item and the second work item, the method includes determining an updated forecast range based on the broken dependency relationship (Paragraph 0018, When managing Engineering development projects, it is common for project managers to want to identify a sequence of tasks in which the last task in the sequence is also the last task to be completed in the project. This sequence of tasks is called the "critical path` and because of the direct (day-for-day) effect on the project completion, usually represents the tasks of most interest to project managers. Other paths are said to have a "lag" of some amount, which represents amount of time a path completes ahead of the critical path. As projects progress and unexpected events occur, the critical path may change as some tasks slip out and become part of a new critical path driving overall project completion; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Figure 6, Tracepoint; Paragraph 0108, The tracepoint display algorithm will obtain the combined CC curve for the associated tasks, and evaluate it at its completion target date. This value can be called the Target Probability (TP). Next, it applies specified thresholds to determine what state the tracepoint is in. For example, a tracepoint could be given upper and lower probability thresholds of 75% and 50%, to determine the result; Paragraph 0109, If TP is below the lower threshold, the tracepoint might show RED; if the TP is between the two thresholds, the tracepoint might show YELLOW, and if the TP is above the upper threshold, the tracepoint might show GREEN. Note that the choice of visual indication is arbitrary; various fill patterns could be used instead for monochromatic displays or print output. It can be noted that in this case, if the baseline duration is using the mean of the probability density function and the target value is set at .5, then any probability of completion that is less than 50% will result in a forecast end date that is past the baseline/scheduled end date. Therefore, a probability of completion less than 50% indicates that the work item (e.g. completion/end date) is forecast-broken, in which tracepoint might show red).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted completion/end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon of the invention of Ponce the Leon to further incorporate an alert indicating that the forecast end date of the second work item is after a scheduled end date of the second work item of the invention of Newpol et al. because doing so would allow the user interface to include a tracepoint which can represent the completion probability of a task or set of tasks, against some targeted completion date (see Newpol et al., Paragraph 0107). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 28 (New), Ponce de Leon discloses a project management system comprising: a processor; and a memory cooperating with the processor to (Figure 12, Processor & Database; Paragraph 0020, In accordance with a preferred embodiment hereof, this invention provides a planning method, relating to at least one project; Paragraph 0132, FIG. 12 shows an overview schematic representation of the computer hardware and Internet environment utilized by the Interactive Graphics-Based Planning System according to a preferred embodiment of the present invention):
cause display, on a user interface communicably coupled to the project management system, of a scheduling interface including a first work item user interface element corresponding to a first work item stored by a data storage system of the project management system, the first work item user interface element depicting (Figure 10, Activity 1; Paragraph 0031, and storing at least one project plan, wherein such at least one project plan comprises at least at least two of such activities relating to at least one project, such position of such at least one symbol representing such at least one activity on such selected at least on time-scaled calendar, such at least one initial estimated duration relating to such at least one activity; Paragraph 0181, Application Server; Paragraph 0166, Referring to FIG. 10, which shows a simplified sample project schedule created through the use of the interactive planning and scheduling method before re-positioning one or more activities provided by the Interactive Graphics-Based Planning System 100 according to a preferred embodiment of the present invention):
schedule information for the first work item including a scheduled duration of the first work item (Figure 3, Activity Properties, Display Activity Start/End Dates, Display Activity Duration);
an end date adjustment control (Figure 2A and related text in Paragraph 0141, In principle, predecessor Activity 201 has three possible connect points, that is: Start Node (“I” node 209); Embedded Node 211; and Finish Node (“J” node 210); Paragraph 0154, Interactive Graphics-Based Planning System 100 enables users to make their own choices as to which project activities to move to earlier or later time frames, “crash” or extend to improve manpower loading and optimize time-dependent costs; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node);
and a start date adjustment control (Figure 2A and related text in Paragraph 0141, In principle, predecessor Activity 201 has three possible connect points, that is: Start Node (“I” node 209); Embedded Node 211; and Finish Node (“J” node 210); Paragraph 0154, Interactive Graphics-Based Planning System 100 enables users to make their own choices as to which project activities to move to earlier or later time frames, “crash” or extend to improve manpower loading and optimize time-dependent costs; Table 2, Activity 201 Attributes Activity, I-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node);
in response to an input at the start date adjustment control or the end date adjustment control: generate a new scheduled end date based on the input and the scheduled duration (Figure 2A and related text in Paragraph 0141, In principle, predecessor Activity 201 has three possible connect points, that is: Start Node (“I” node 209); Embedded Node 211; and Finish Node (“J” node 210); Paragraph 0154, Interactive Graphics-Based Planning System 100 enables users to make their own choices as to which project activities to move to earlier or later time frames, “crash” or extend to improve manpower loading and optimize time-dependent costs; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node);
identify a second work item having a dependency relationship to the first work item (Paragraph 0026, Further, it provides such a planning method, wherein such at least one time-dependent relationship comprises "from such at least one first activity embedded node to such at least one second activity start node" relationship. Even further, it provides such a planning method, wherein such at least one time-dependent relationship comprises "from such at least one first activity finish node to such at least one second activity embedded node" relationship);
generate forecast data based on the new scheduled end date and a start date of the second work item (Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node; Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200; Examiner notes that probability density functions are re-calculated in response to changes in project task/activities. Therefore, new forecast data is generated every time that there’s a change in the schedule);
and determine, based on the forecast data: a set of forecast start dates for the second work item (Paragraph 0052, Backward Planning--A protocol based on the predicate that the completion date of the project is known from which the start date for the project is to be calculated. In the Present invention-provided Backward Planning mode, the system determines the latest overall schedule start date and the minimum project duration from the given completion date; Paragraph 0067, Early/Late Dates. Earliest/latest possible start and finish times for the activities based on activity durations, logic ties and constraints; Paragraph 0092, Monte Carlo Schedule Analysis. A technique that computes or iterates the plan many times using input values selected at random from probability distribution functions of possible durations, to calculate the distribution of possible completion dates for the plurality of activities comprising the plan at the time such analysis is performed); …;
cause display, on the user interface, of a forecast … on the scheduling interface indicating a particular forecast [completion/end] date of the set of forecast [completion/end] dates (Paragraph 0017, It is a further object and feature of the present invention that, in substantial and real time, as activities, relationship (logic) ties and milestone deadlines are changed, repositioned or as new activities, relationship (logic) ties and milestone deadlines are added to the plan, if probability distribution functions are associated with the activities, to provide such a system that calculates completion risk profiles and activity criticality indexes the simplest possible way; Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200. Through Monte Carlo simulation techniques, iterating many times through the plan, Interactive Graphics-Based Planning System 100, preferably calculates the corresponding probability distribution function for the plan overall completion, as well as to what extent the activities turned out as having zero float (commonly referred to as activity criticality indexes). Using this highly preferred feature of Interactive Graphics-Based Planning System 100 allows the evolving plan to be risked at any time, further allowing the project to be "reworked" to attain a better risk profile. The above-described cost optimization feature of Interactive Graphics-Based Planning System 100 at least embodies herein; associating duration probability distribution functions, by such at least one project planner using such at least one computational device, with such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-positioning, by such at least one project planner using such at least one computational device, such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-calculating, by such at least one computational device, such duration probability distribution functions, relating to at least one such activity repositioning, to provide at least one project risk profile; and displaying, essentially continuously, by such at least one computational device, such at least one project risk profile on such at least one time-scaled calendar on such at least one graphical user interface; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node; Examiner interprets the “probability distribution for the completion of each activity” as the “forecast end date”).
Although Ponce de Leon discloses all the limitations above, calculating a forecast for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177, duration probability distribution), and causing a display of the forecasted end/completion date on a graphical user interface (see Paragraph 0177, display at least one project risk profile such as completion risk profile), Ponce de Leon does not specifically disclose a forecast user interface element on the scheduling interface indicating a particular forecast start date of the set of forecast start dates.
However, Newpol et al. discloses a set of confidence intervals, each confidence interval of the set of confidence intervals associated with a respective forecast start date of the set of forecast start dates; cause display, on the user interface, of a forecast user interface element on the scheduling interface indicating a particular forecast start date of the set of forecast start dates (Paragraph 0012, What is needed is a way to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates, yet still retain the ease, familiarity, and timeline context of a Gantt-style representation; Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task; Paragraph 0066, However, in traditional project planning the start date of a task is also of interest. Since statistical distributions do not provide a fixed start date, and each task has only a duration estimate, it takes additional analysis to derive individual task start dates with the same overall specified confidence. For any given task, this is done by identifying the tasks that must be completed before the given task can begin. These tasks may be either `predecessor` tasks, whose output is used in the given task, or independent tasks assigned to the same person (assuming that multiple tasks assigned to the same person are performed in some planned order); Paragraph 0067, Once these pre-requisite tasks are identified, the statistical Completion modeling described above is used to compute the completion duration for the overall set of pre-requisites (i.e. the `pre-requisites end date`). Taking the specified confidence point of that function produces the start date of the given dependent task, at the specified confidence).
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It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted start date and end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the forecasted data is displayed on a forecast user interface element of the invention of Newpol et al. because doing so would allow the method to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates (see Newpol et al., Paragraph 0012). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 29 (New), which is dependent of claim 28, the combination of Ponce de Leon and Newpol et al. discloses all the limitations in claim 28. Although Ponce de Leon discloses all the limitations above, calculating a forecast for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177, duration probability distribution), and causing a display of the forecasted end/completion date on a graphical user interface (see Paragraph 0177, display at least one project risk profile such as completion risk profile), Ponce de Leon does not specifically disclose a forecast user interface element on the scheduling interface indicating a particular forecast start date of the set of forecast start dates.
However, Newpol et al. discloses wherein: the particular forecast start date is associated with a highest confidence interval of the set of confidence intervals; and the forecast user interface element further indicates a planned end date of the second work item and a forecast end date range generated based on the set of forecast start dates (Paragraph 0012, What is needed is a way to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates, yet still retain the ease, familiarity, and timeline context of a Gantt-style representation; Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task; Paragraph 0066, However, in traditional project planning the start date of a task is also of interest. Since statistical distributions do not provide a fixed start date, and each task has only a duration estimate, it takes additional analysis to derive individual task start dates with the same overall specified confidence. For any given task, this is done by identifying the tasks that must be completed before the given task can begin. These tasks may be either `predecessor` tasks, whose output is used in the given task, or independent tasks assigned to the same person (assuming that multiple tasks assigned to the same person are performed in some planned order); Paragraph 0067, Once these pre-requisite tasks are identified, the statistical Completion modeling described above is used to compute the completion duration for the overall set of pre-requisites (i.e. the `pre-requisites end date`). Taking the specified confidence point of that function produces the start date of the given dependent task, at the specified confidence).
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It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted start date and end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the forecasted data is displayed on a forecast user interface element of the invention of Newpol et al. because doing so would allow the method to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates (see Newpol et al., Paragraph 0012). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 32 (New), which is dependent of claim 28, the combination of Ponce de Leon and Newpol et al. discloses all the limitations in claim 28. Although Ponce de Leon discloses determining the forecast range includes determining a forecast end date of the second work item based on the new scheduled end date of the first work item (see Paragraph 0177, display at least one project risk profile such as completion risk profile), Ponce de Leon does not specifically disclose causing display of the forecast user interface element includes displaying an alert that indicates the dependency relationship is forecast to be broken.
However, Newpol et al. further discloses wherein: subsequent to determining the set of confidence intervals (Paragraph 0012, What is needed is a way to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates, yet still retain the ease, familiarity, and timeline context of a Gantt-style representation; Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task; Paragraph 0066, However, in traditional project planning the start date of a task is also of interest. Since statistical distributions do not provide a fixed start date, and each task has only a duration estimate, it takes additional analysis to derive individual task start dates with the same overall specified confidence. For any given task, this is done by identifying the tasks that must be completed before the given task can begin. These tasks may be either `predecessor` tasks, whose output is used in the given task, or independent tasks assigned to the same person (assuming that multiple tasks assigned to the same person are performed in some planned order); Paragraph 0067, Once these pre-requisite tasks are identified, the statistical Completion modeling described above is used to compute the completion duration for the overall set of pre-requisites (i.e. the `pre-requisites end date`). Taking the specified confidence point of that function produces the start date of the given dependent task, at the specified confidence), a determination is made that a third work item having a dependency relationship to the second work item has a scheduled start date before a forecast end date within the set of forecast end date ranges; and causing display of the forecast user interface element includes displaying an alert that indicates the dependency relationship is forecast to be broken (Figure 6, Tracepoint; Paragraph 0108, The tracepoint display algorithm will obtain the combined CC curve for the associated tasks, and evaluate it at its completion target date. This value can be called the Target Probability (TP). Next, it applies specified thresholds to determine what state the tracepoint is in. For example, a tracepoint could be given upper and lower probability thresholds of 75% and 50%, to determine the result; Paragraph 0109, If TP is below the lower threshold, the tracepoint might show RED; if the TP is between the two thresholds, the tracepoint might show YELLOW, and if the TP is above the upper threshold, the tracepoint might show GREEN. Note that the choice of visual indication is arbitrary; various fill patterns could be used instead for monochromatic displays or print output. It can be noted that in this case, if the baseline duration is using the mean of the probability density function and the target value is set at .5, then any probability of completion that is less than 50% will result in a forecast end date that is past the baseline/scheduled end date. Therefore, a probability of completion less than 50% indicates that the work item (e.g. completion/end date) is forecast-broken, in which tracepoint might show red).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted start date and end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the forecasted data is displayed on a forecast user interface element of the invention of Newpol et al. because doing so would allow the method to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates (see Newpol et al., Paragraph 0012). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 35 (New), Ponce de Leon discloses a computer-implemented method comprising (Paragraph 0020, In accordance with a preferred embodiment hereof, this invention provides a planning method, relating to at least one project; Paragraph 0132, FIG. 12 shows an overview schematic representation of the computer hardware and Internet environment utilized by the Interactive Graphics-Based Planning System according to a preferred embodiment of the present invention):
causing display, on a user interface communicably coupled to a project management server application hosted by a server, of a scheduling interface including a first work item user interface element corresponding to a first work item stored by a data storage system of the project management server application, the first work item user interface element depicting (Figure 10, Activity 1; Paragraph 0031, and storing at least one project plan, wherein such at least one project plan comprises at least at least two of such activities relating to at least one project, such position of such at least one symbol representing such at least one activity on such selected at least on time-scaled calendar, such at least one initial estimated duration relating to such at least one activity; Paragraph 0181, Application Server; Paragraph 0166, Referring to FIG. 10, which shows a simplified sample project schedule created through the use of the interactive planning and scheduling method before re-positioning one or more activities provided by the Interactive Graphics-Based Planning System 100 according to a preferred embodiment of the present invention):
scheduling information extracted from the first work item (Figure 3, Activity Properties, Display Activity Start/End Dates, Display Activity Duration);
a scheduled date adjustment control (Figure 2A and related text in Paragraph 0141, In principle, predecessor Activity 201 has three possible connect points, that is: Start Node (“I” node 209); Embedded Node 211; and Finish Node (“J” node 210); Paragraph 0154, Interactive Graphics-Based Planning System 100 enables users to make their own choices as to which project activities to move to earlier or later time frames, “crash” or extend to improve manpower loading and optimize time-dependent costs; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node);
and a create dependency control, wherein in response to an input at the create dependency control (Paragraph 0026, Further, it provides such a planning method, wherein such at least one time-dependent relationship comprises "from such at least one first activity embedded node to such at least one second activity start node" relationship. Even further, it provides such a planning method, wherein such at least one time-dependent relationship comprises "from such at least one first activity finish node to such at least one second activity embedded node" relationship):
a dependency relationship between the first work item and a second work item is created and stored at the data storage system (Paragraph 0026, Further, it provides such a planning method, wherein such at least one time-dependent relationship comprises "from such at least one first activity embedded node to such at least one second activity start node" relationship. Even further, it provides such a planning method, wherein such at least one time-dependent relationship comprises "from such at least one first activity finish node to such at least one second activity embedded node" relationship; Paragraph 0031, Storing at least one project plan, wherein such at least one project plan comprises at least at least two of such activities relating to at least one project);
and forecast data associated with the second work item is generated based on the scheduling information extracted from the first work item and the dependency relationship (Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node; Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200; Examiner notes that probability density functions are re-calculated in response to changes in project task/activities. Therefore, new forecast data is generated every time that there’s a change in the schedule);
determining, based on the forecast data: a forecast start date of the second work item (Paragraph 0052, Backward Planning--A protocol based on the predicate that the completion date of the project is known from which the start date for the project is to be calculated. In the Present invention-provided Backward Planning mode, the system determines the latest overall schedule start date and the minimum project duration from the given completion date; Paragraph 0067, Early/Late Dates. Earliest/latest possible start and finish times for the activities based on activity durations, logic ties and constraints; Paragraph 0092, Monte Carlo Schedule Analysis. A technique that computes or iterates the plan many times using input values selected at random from probability distribution functions of possible durations, to calculate the distribution of possible completion dates for the plurality of activities comprising the plan at the time such analysis is performed); …;
and causing display, on the user interface, of a forecast … on the scheduling interface indicating … the forecast [completion/end] date (Paragraph 0017, It is a further object and feature of the present invention that, in substantial and real time, as activities, relationship (logic) ties and milestone deadlines are changed, repositioned or as new activities, relationship (logic) ties and milestone deadlines are added to the plan, if probability distribution functions are associated with the activities, to provide such a system that calculates completion risk profiles and activity criticality indexes the simplest possible way; Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200. Through Monte Carlo simulation techniques, iterating many times through the plan, Interactive Graphics-Based Planning System 100, preferably calculates the corresponding probability distribution function for the plan overall completion, as well as to what extent the activities turned out as having zero float (commonly referred to as activity criticality indexes). Using this highly preferred feature of Interactive Graphics-Based Planning System 100 allows the evolving plan to be risked at any time, further allowing the project to be "reworked" to attain a better risk profile. The above-described cost optimization feature of Interactive Graphics-Based Planning System 100 at least embodies herein; associating duration probability distribution functions, by such at least one project planner using such at least one computational device, with such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-positioning, by such at least one project planner using such at least one computational device, such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-calculating, by such at least one computational device, such duration probability distribution functions, relating to at least one such activity repositioning, to provide at least one project risk profile; and displaying, essentially continuously, by such at least one computational device, such at least one project risk profile on such at least one time-scaled calendar on such at least one graphical user interface; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node; Examiner interprets the “probability distribution for the completion of each activity” as the “forecast end date”).
Although Ponce de Leon discloses all the limitations above, calculating a forecast for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177, duration probability distribution), and causing a display of the forecasted end/completion date on a graphical user interface (see Paragraph 0177, display at least one project risk profile such as completion risk profile), Ponce de Leon does not specifically disclose a forecast user interface element on the scheduling interface indicating the forecast confidence interval and the forecast start date.
However, Newpol et al. discloses a forecast confidence interval for the forecast start date; and causing display, on the user interface, of a forecast user interface element on the scheduling interface indicating the forecast confidence interval and the forecast start date (Paragraph 0012, What is needed is a way to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates, yet still retain the ease, familiarity, and timeline context of a Gantt-style representation; Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task; Paragraph 0066, However, in traditional project planning the start date of a task is also of interest. Since statistical distributions do not provide a fixed start date, and each task has only a duration estimate, it takes additional analysis to derive individual task start dates with the same overall specified confidence. For any given task, this is done by identifying the tasks that must be completed before the given task can begin. These tasks may be either `predecessor` tasks, whose output is used in the given task, or independent tasks assigned to the same person (assuming that multiple tasks assigned to the same person are performed in some planned order); Paragraph 0067, Once these pre-requisite tasks are identified, the statistical Completion modeling described above is used to compute the completion duration for the overall set of pre-requisites (i.e. the `pre-requisites end date`). Taking the specified confidence point of that function produces the start date of the given dependent task, at the specified confidence).
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It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted start date and end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the forecasted data is displayed on a forecast user interface element of the invention of Newpol et al. because doing so would allow the method to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates (see Newpol et al., Paragraph 0012). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 36 (New), which is dependent of claim 35, the combination of Ponce de Leon and Newpol et al. discloses all the limitations in claim 35. Ponce de Leon further discloses wherein the method further comprises: subsequent to creating the dependency relationship, receiving an input at the scheduled date adjustment control; updating the scheduling information of the first work item based on the input at the scheduled date adjustment control (Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node; Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200; Examiner notes that probability density functions are re-calculated in response to changes in project task/activities. Therefore, new forecast data is generated every time that there’s a change in the schedule); and generating and causing display of updated forecast data in the … user interface element based on the updated forecast data (Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200. Through Monte Carlo simulation techniques, iterating many times through the plan, Interactive Graphics-Based Planning System 100, preferably calculates the corresponding probability distribution function for the plan overall completion, as well as to what extent the activities turned out as having zero float (commonly referred to as activity criticality indexes). Using this highly preferred feature of Interactive Graphics-Based Planning System 100 allows the evolving plan to be risked at any time, further allowing the project to be "reworked" to attain a better risk profile. The above-described cost optimization feature of Interactive Graphics-Based Planning System 100 at least embodies herein; associating duration probability distribution functions, by such at least one project planner using such at least one computational device, with such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-positioning, by such at least one project planner using such at least one computational device, such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-calculating, by such at least one computational device, such duration probability distribution functions, relating to at least one such activity repositioning, to provide at least one project risk profile; and displaying, essentially continuously, by such at least one computational device, such at least one project risk profile on such at least one time-scaled calendar on such at least one graphical user interface; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node).
Although Ponce de Leon discloses all the limitations above, calculating a forecast for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177, duration probability distribution), and causing a display of the forecasted end/completion date on a graphical user interface (see Paragraph 0177, display at least one project risk profile such as completion risk profile), Ponce de Leon does not specifically disclose a forecast user interface element.
However, Newpol et al. discloses generating and causing display of updated forecast data in the forecast user interface element based on the updated forecast data (Paragraph 0012, What is needed is a way to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates, yet still retain the ease, familiarity, and timeline context of a Gantt-style representation; Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task; Paragraph 0066, However, in traditional project planning the start date of a task is also of interest. Since statistical distributions do not provide a fixed start date, and each task has only a duration estimate, it takes additional analysis to derive individual task start dates with the same overall specified confidence. For any given task, this is done by identifying the tasks that must be completed before the given task can begin. These tasks may be either `predecessor` tasks, whose output is used in the given task, or independent tasks assigned to the same person (assuming that multiple tasks assigned to the same person are performed in some planned order); Paragraph 0067, Once these pre-requisite tasks are identified, the statistical Completion modeling described above is used to compute the completion duration for the overall set of pre-requisites (i.e. the `pre-requisites end date`). Taking the specified confidence point of that function produces the start date of the given dependent task, at the specified confidence).
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It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted start date and end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the forecasted data is displayed on a forecast user interface element of the invention of Newpol et al. because doing so would allow the method to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates (see Newpol et al., Paragraph 0012). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 39 (New), which is dependent of claim 35, the combination of Ponce de Leon and Newpol et al. discloses all the limitations in claim 35. Ponce de Leon further discloses wherein: generating the forecast data is further based on forecast end dates for a set of additional work items having dependency relationships with the first work item (Paragraph 0177, In managing Schedule Risk, provided duration probability distribution functions are, in a similar manner, preferably associated with project task/activities, as activities are added, deleted and/or repositioned on Time-Scaled Calendar 200. Through Monte Carlo simulation techniques, iterating many times through the plan, Interactive Graphics-Based Planning System 100, preferably calculates the corresponding probability distribution function for the plan overall completion, as well as to what extent the activities turned out as having zero float (commonly referred to as activity criticality indexes). Using this highly preferred feature of Interactive Graphics-Based Planning System 100 allows the evolving plan to be risked at any time, further allowing the project to be "reworked" to attain a better risk profile. The above-described cost optimization feature of Interactive Graphics-Based Planning System 100 at least embodies herein; associating duration probability distribution functions, by such at least one project planner using such at least one computational device, with such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-positioning, by such at least one project planner using such at least one computational device, such at least one symbol representing each such activity of such at least two activities of such plurality of activities on such at least one time-scaled calendar on such at least one graphical user interface; re-calculating, by such at least one computational device, such duration probability distribution functions, relating to at least one such activity repositioning, to provide at least one project risk profile; and displaying, essentially continuously, by such at least one computational device, such at least one project risk profile on such at least one time-scaled calendar on such at least one graphical user interface; Table 2, Activity 201 Attributes Activity, J-Node (Logic Diagram Method & Arrow Diagramming Method), Automatic by clicking on LDM icon, Display date as user clicks/drags node); …
Although Ponce de Leon discloses all the limitations above, calculating a forecast range for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177), and causing a display of the forecasted range on a graphical user interface (see Paragraph 0177, display at least one project risk profile), Ponce de Leon does not specifically disclose wherein the forecast end dates are each associated with a respective confidence interval.
However, Newpol et al. discloses wherein: generating the forecast data is further based on forecast end dates for a set of additional work items having dependency relationships with the first work item; the forecast end dates are each associated with a respective confidence interval; and determining the respective confidence interval for the forecast start date includes evaluating the respective confidence intervals for forecast end dates of the set of additional work items (Paragraph 0012, What is needed is a way to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates, yet still retain the ease, familiarity, and timeline context of a Gantt-style representation; Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task; Paragraph 0066, However, in traditional project planning the start date of a task is also of interest. Since statistical distributions do not provide a fixed start date, and each task has only a duration estimate, it takes additional analysis to derive individual task start dates with the same overall specified confidence. For any given task, this is done by identifying the tasks that must be completed before the given task can begin. These tasks may be either `predecessor` tasks, whose output is used in the given task, or independent tasks assigned to the same person (assuming that multiple tasks assigned to the same person are performed in some planned order); Paragraph 0067, Once these pre-requisite tasks are identified, the statistical Completion modeling described above is used to compute the completion duration for the overall set of pre-requisites (i.e. the `pre-requisites end date`). Taking the specified confidence point of that function produces the start date of the given dependent task, at the specified confidence).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted start date and end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the forecasted data is displayed on a forecast user interface element of the invention of Newpol et al. because doing so would allow the method to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates (see Newpol et al., Paragraph 0012). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 25-27, 30-31, 33, 37-38, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Ponce de Leon (US 2008/0195452 A1), in view of Newpol et al. (US 2011/0302090 A1), in further view of Sen et al. (US 2016/0171406 A1).
Regarding claims 25 and 33 (New), which are dependent of claims 21 and 28, the combination of Ponce de Leon and Newpol et al. discloses all the limitations in claims 21 and 28. Ponce de Leon further discloses wherein: the forecast data is generated using a forecast model of a set of forecast models; … (Paragraph 0023, Even further, it provides such a planning method, further comprising the step of revising, by such at least one project planner using such at least one computational device, such at least one initial estimated duration for such at least one activity having such at least one time-dependent relationship on such at least one graphical user interface).
Although Ponce de Leon discloses estimating the duration of the activity (Paragraph 0023), the combination of Ponce de Leon and Newpol et al. does not specifically disclose wherein the estimate/forecast is based on historical data.
However, Sen et al. discloses wherein: the forecast data is generated using a forecast model of a set of forecast models; the forecast model is generated based on historical data associated with previous work items that were previously completed (Paragraph 0021, Systems, methods, and other embodiments for facilitating the forecasting of activity and project time durations and completion dates of a project plan associated with a computer application are disclosed. Example embodiments are discussed herein with respect to computerized project scheduling, where activities are defined and are to be performed over scheduled time periods. Some activities are related by one or more of timing with respect each other, common category (similar type of activities), or common resources used to complete the activities. In one embodiment, a project scheduling tool is disclosed that is configured to intelligently forecast an accurate completion date for an on-going project and for the on-going tasks and activities making up the project plan of the project).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the method used for generating forecast data for the plurality of activities and/or for the overall project of the invention of Ponce the Leon and Newpol et al. to further incorporate wherein the forecast data is generated based on historical data associated with previous work items that were previously completed of the invention of Sen et al. because doing so would allow the method to intelligently forecast an accurate completion date based on similar type of activities (see Sen et al., Paragraph 0021). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 26 (New), which is dependent of claim 25, the combination of Ponce de Leon, Newpol et al., and Sen et al. discloses all the limitations in claim 25. Although Ponce de Leon discloses estimating the duration of the activity (Paragraph 0023), the combination of Ponce de Leon and Newpol et al. does not specifically disclose wherein the estimate/forecast is based on historical data.
However, Sen et al. discloses wherein: the previous work items are associated with a project; and the first work item and the second work item are associated with the project (Paragraph 0096, Systems, methods, and other embodiments for providing scheduling of activities of a project plan associated with a computer application have been described herein. In one embodiment, a project scheduling tool is configured to handle projects composed of activities that are largely similar in nature as well as more complex projects composed of activities that are widely dissimilar. The project scheduling tool takes into account project units (e.g., units of work) logged over elapsed periods of time of the project and facilitates user selection of past time periods likely to reflect future progress of the project. This provides easier and more accurate estimation of completion dates and time durations for individual tasks of a project and for the entire project).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the method used for generating forecast data for the plurality of activities and/or for the overall project of the invention of Ponce the Leon and Newpol et al. to further incorporate wherein the forecast data is generated based on historical data associated with previous work items that were previously completed of the invention of Sen et al. because doing so would allow the method to intelligently forecast an accurate completion date based on similar type of activities (see Sen et al., Paragraph 0021). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 27 (New), which is dependent of claim 26, the combination of Ponce de Leon, Newpol et al., and Sen et al. discloses all the limitations in claim 26. Although Ponce de Leon discloses estimating the duration of the activity (Paragraph 0023), the combination of Ponce de Leon and Newpol et al. does not specifically disclose wherein the estimate/forecast is based on historical data.
However, Sen et al. discloses wherein: a subset of the previous work items do not have at least one of a recorded actual start date or a recorded actual end date; the forecast data is generated in part by determining workflow transition dates of the subset of the previous work items; and each workflow transition date of the workflow transition dates is associated with a respective transition to an in-progress workflow state or a done workflow state for a respective previous work item of the subset of previous work items (Paragraph 0036, The schedule forecasting logic 115 is configured to generate the planned progress data structure having planned progress data characterizing a planned progress of the project (or activity). The schedule forecasting logic 115 is also configured to generate the actual progress data structure having actual progress data characterizing an actual progress of the project (or activity). The schedule forecasting logic 115 is further configured to generate a slope value corresponding to a slope of a linear path between the trend start indicium and the trend finish indicium on the graph. The slope value represents a pace or velocity of actual progress of the project (or activity) between the two indicia; Paragraph 0037, The schedule forecasting logic 115 is configured to extrapolate the actual progress data within the actual progress data structure (i.e., transform the actual progress data structure), based on the slope value, from a current time to a future time. The future time corresponds to a second (updated or forecasted) completion date for a project (or activity). In this manner, a more accurate or realistic project (or activity) completion date may be forecasted based on a pace or velocity of actual progress data. Examples of such extrapolations are illustrated and discussed herein with respect to FIGS. 2-8).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the method used for generating forecast data for the plurality of activities and/or for the overall project of the invention of Ponce the Leon and Newpol et al. to further incorporate wherein the forecast data is generated based on historical data associated with previous work items that were previously completed of the invention of Sen et al. because doing so would allow the method to intelligently forecast an accurate completion date based on similar type of activities (see Sen et al., Paragraph 0021). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 30 (New), which is dependent of claim 29, the combination of Ponce de Leon and Newpol et al. discloses all the limitations in claim 29. Although Ponce de Leon discloses all the limitations above, calculating a forecast for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177, duration probability distribution), and causing a display of the forecasted end/completion date on a graphical user interface (see Paragraph 0177, display at least one project risk profile such as completion risk profile), Ponce de Leon does not specifically disclose wherein the forecast is based in part on the set of confidence intervals
However, Newpol et al. discloses wherein causing display of the forecast end date range includes: determining a set of forecast end date ranges based in part on the set of confidence intervals; and causing display of the set of forecast end date ranges … (Paragraph 0012, What is needed is a way to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates, yet still retain the ease, familiarity, and timeline context of a Gantt-style representation; Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task; Paragraph 0066, However, in traditional project planning the start date of a task is also of interest. Since statistical distributions do not provide a fixed start date, and each task has only a duration estimate, it takes additional analysis to derive individual task start dates with the same overall specified confidence. For any given task, this is done by identifying the tasks that must be completed before the given task can begin. These tasks may be either `predecessor` tasks, whose output is used in the given task, or independent tasks assigned to the same person (assuming that multiple tasks assigned to the same person are performed in some planned order); Paragraph 0067, Once these pre-requisite tasks are identified, the statistical Completion modeling described above is used to compute the completion duration for the overall set of pre-requisites (i.e. the `pre-requisites end date`). Taking the specified confidence point of that function produces the start date of the given dependent task, at the specified confidence).
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It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted start date and end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the forecasted data is displayed on a forecast user interface element of the invention of Newpol et al. because doing so would allow the method to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates (see Newpol et al., Paragraph 0012). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although the combination of Ponce de Leon and Newpol et al. discloses causing display of the set of forecast end date ranges based in part on the set of confidence intervals (see Paragraphs 0061), the combination of Ponce de Leon and Newpol et al. does not specifically disclose wherein the set of forecast end date ranges is displayed in response to receiving a user hover input with respect to the forecast user interface element.
However, Sen et al. discloses and causing display of [information] in response to receiving a user hover input with respect to the … user interface element (Paragraph 0053, The project scheduling tool 110 is configured to keep track of the dates on which such impactful changes have been recorded and highlights the corresponding check points 301-305 on the line of actual progress 310 (as shown in FIG. 3). Before attempting to forecast the project, as discussed previously herein, a project manager may view the check point details (e.g., comments 320) by hovering on the respective check point indicia and, therefore, make a better informed forecast (e.g., select a better representative trend region). As discussed above with respect to FIG. 1, in one embodiment, the visual user interface logic 125 is configured to facilitate user interaction with the graphical user interface to manipulate check point information).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein a set of forecast end date ranges is displayed on a forecast user interface element of the invention of invention of Ponce the Leon and Newpol et al. to further specify wherein the set of forecast end date ranges is displayed in response to receiving a user hover input with respect to the user interface element of the invention of Sen et al. because doing so would allow a program manager to view additional project details by hovering on the respective check point indicia (see Sen et al., Paragraph 0053). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 31 (New), which is dependent of claim 30, the combination of Ponce de Leon, Newpol et al., and Sen et al. discloses all the limitations in claim 30. Although Ponce de Leon discloses all the limitations above, calculating a forecast for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177, duration probability distribution), and causing a display of the forecasted end/completion date on a graphical user interface (see Paragraph 0177, display at least one project risk profile such as completion risk profile), Ponce de Leon does not specifically disclose wherein the forecast is based in part on the set of confidence intervals
However, Newpol et al. discloses wherein: generating the forecast data is further based on estimated durations for a set of additional work items having dependency relationships with the first work item; the estimated durations are each associated with a respective confidence interval; and determining the set of confidence intervals includes evaluating the respective confidence intervals for the scheduled durations for the set of additional work items ((Paragraph 0012, What is needed is a way to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates, yet still retain the ease, familiarity, and timeline context of a Gantt-style representation; Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task; Paragraph 0066, However, in traditional project planning the start date of a task is also of interest. Since statistical distributions do not provide a fixed start date, and each task has only a duration estimate, it takes additional analysis to derive individual task start dates with the same overall specified confidence. For any given task, this is done by identifying the tasks that must be completed before the given task can begin. These tasks may be either `predecessor` tasks, whose output is used in the given task, or independent tasks assigned to the same person (assuming that multiple tasks assigned to the same person are performed in some planned order); Paragraph 0067, Once these pre-requisite tasks are identified, the statistical Completion modeling described above is used to compute the completion duration for the overall set of pre-requisites (i.e. the `pre-requisites end date`). Taking the specified confidence point of that function produces the start date of the given dependent task, at the specified confidence).
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It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted completion/end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the calculated forecasted completion/end date is displayed on a forecast user interface element of the invention of Newpol et al. because doing so would allow the method to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained (see Newpol et al., Paragraph 0061). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 37 (New), which is dependent of claim 35, the combination of Ponce de Leon and Newpol et al. discloses all the limitations in claim 35. Although Ponce de Leon discloses estimating the duration of the activity (Paragraph 0023), the combination of Ponce de Leon and Newpol et al. does not specifically disclose wherein the estimate/forecast is based on historical data.
However, Sen et al. discloses wherein: the forecast data is generated based in part on a set of actual work item durations calculated based on historical work item data associated with a project (Paragraph 0021, Systems, methods, and other embodiments for facilitating the forecasting of activity and project time durations and completion dates of a project plan associated with a computer application are disclosed. Example embodiments are discussed herein with respect to computerized project scheduling, where activities are defined and are to be performed over scheduled time periods. Some activities are related by one or more of timing with respect each other, common category (similar type of activities), or common resources used to complete the activities. In one embodiment, a project scheduling tool is disclosed that is configured to intelligently forecast an accurate completion date for an on-going project and for the on-going tasks and activities making up the project plan of the project);
and the first work item and the second work item are associated with the project (Paragraph 0096, Systems, methods, and other embodiments for providing scheduling of activities of a project plan associated with a computer application have been described herein. In one embodiment, a project scheduling tool is configured to handle projects composed of activities that are largely similar in nature as well as more complex projects composed of activities that are widely dissimilar. The project scheduling tool takes into account project units (e.g., units of work) logged over elapsed periods of time of the project and facilitates user selection of past time periods likely to reflect future progress of the project. This provides easier and more accurate estimation of completion dates and time durations for individual tasks of a project and for the entire project).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the method used for generating forecast data for the plurality of activities and/or for the overall project of the invention of Ponce the Leon and Newpol et al. to further incorporate wherein the forecast data is generated based on historical data associated with previous work items that were previously completed of the invention of Sen et al. because doing so would allow the method to intelligently forecast an accurate completion date based on similar type of activities (see Sen et al., Paragraph 0021). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 38 (New), which is dependent of claim 37, the combination of Ponce de Leon, Newpol et al., and Sen et al. discloses all the limitations in claim 37. Although Ponce de Leon discloses estimating the duration of the activity (Paragraph 0023), the combination of Ponce de Leon and Newpol et al. does not specifically disclose wherein the estimate/forecast is based on historical data.
However, Sen et al. discloses wherein: a forecast model is generated using the historical work item data; and the forecast data is generated using the forecast model (Paragraph 0021, Systems, methods, and other embodiments for facilitating the forecasting of activity and project time durations and completion dates of a project plan associated with a computer application are disclosed. Example embodiments are discussed herein with respect to computerized project scheduling, where activities are defined and are to be performed over scheduled time periods. Some activities are related by one or more of timing with respect each other, common category (similar type of activities), or common resources used to complete the activities. In one embodiment, a project scheduling tool is disclosed that is configured to intelligently forecast an accurate completion date for an on-going project and for the on-going tasks and activities making up the project plan of the project).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the method used for generating forecast data for the plurality of activities and/or for the overall project of the invention of Ponce the Leon and Newpol et al. to further incorporate wherein the forecast data is generated based on historical data associated with previous work items that were previously completed of the invention of Sen et al. because doing so would allow the method to intelligently forecast an accurate completion date based on similar type of activities (see Sen et al., Paragraph 0021). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 40 (New), which is dependent of claim 35, the combination of Ponce de Leon and Newpol et al. discloses all the limitations in claim 35. Although Ponce de Leon discloses all the limitations above, calculating a forecast for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177, duration probability distribution), and causing a display of the forecasted end/completion date on a graphical user interface (see Paragraph 0177, display at least one project risk profile such as completion risk profile), Ponce de Leon does not specifically disclose wherein the forecast is based in part on the set of confidence intervals
However, Newpol et al. discloses wherein: causing display of the forecast start date includes causing display of a set of forecast start date ranges … (Paragraph 0012, What is needed is a way to visually represent a task without losing the significant statistical attributes related to the uncertainty of task start and end dates, yet still retain the ease, familiarity, and timeline context of a Gantt-style representation; Paragraph 0022, Knowing that the uncertainty of a project is decreasing as the project nears completion doesn't provide much usable information with respect to managing the project. This `gut feel` can be difficult to quantify in real terms of uncertainty or confidence, and with traditional project management, even more difficult to continually assess the impact of ongoing deviations in individual subtasks. What is needed is a way to quantify the `remaining` uncertainty as a project progresses toward completion, to give planners the ability to adjust expectations or take action to compensate for unexpected changes in project confidence; Paragraph 0041, The processing unit 12 may reset the range of the end date for the task when at least a portion of the task is completed. The processing unit 12 may reset the range of the project end date after resetting the range of the end date for the task. The processing unit 12 may report a likelihood of achieving a completion date in the range of the end date; Paragraph 0061, With Statistical Distributions for project tasks available, any progress point in a project (including the overall project completion) may be examined to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained. This allows rapid analysis of `what-if` scenarios, such as the effect of resource changes, overall impact of specific task estimates or pre-requisites, and various re-loading scenarios. As tasks are completed or estimates updated, the changes in such values may be tracked to give planners and managers an immediate evaluation of the changing risk associated with the project; Paragraph 0064, Given that a project has been described by one or more subtasks, and completion duration estimates are available for each subtask. It is possible to use statistical techniques to produce completion distributions for any subtask, or for the overall project. The key to this technique is that given a known risk (or confidence) factor, each distribution can be evaluated at the specified risk point to obtain a single fixed duration for any task; Paragraph 0066, However, in traditional project planning the start date of a task is also of interest. Since statistical distributions do not provide a fixed start date, and each task has only a duration estimate, it takes additional analysis to derive individual task start dates with the same overall specified confidence. For any given task, this is done by identifying the tasks that must be completed before the given task can begin. These tasks may be either `predecessor` tasks, whose output is used in the given task, or independent tasks assigned to the same person (assuming that multiple tasks assigned to the same person are performed in some planned order); Paragraph 0067, Once these pre-requisite tasks are identified, the statistical Completion modeling described above is used to compute the completion duration for the overall set of pre-requisites (i.e. the `pre-requisites end date`). Taking the specified confidence point of that function produces the start date of the given dependent task, at the specified confidence).
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It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted completion/end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the calculated forecasted completion/end date is displayed on a forecast user interface element of the invention of Newpol et al. because doing so would allow the method to determine the end date, given an assumed likelihood or confidence value, or alternatively, the chance of completing the tasks by a specific date can be easily obtained (see Newpol et al., Paragraph 0061). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although the combination of Ponce de Leon and Newpol et al. discloses causing display of the set of forecast end date ranges based in part on the set of confidence intervals (see Paragraphs 0061), the combination of Ponce de Leon and Newpol et al. does not specifically disclose wherein the set of forecast end date ranges is displayed in response to receiving a user hover input with respect to the forecast user interface element.
However, Sen et al. discloses wherein: causing display of [information] includes causing display of [information] in response to receiving a user hover input with respect to the … user interface element (Paragraph 0053, The project scheduling tool 110 is configured to keep track of the dates on which such impactful changes have been recorded and highlights the corresponding check points 301-305 on the line of actual progress 310 (as shown in FIG. 3). Before attempting to forecast the project, as discussed previously herein, a project manager may view the check point details (e.g., comments 320) by hovering on the respective check point indicia and, therefore, make a better informed forecast (e.g., select a better representative trend region). As discussed above with respect to FIG. 1, in one embodiment, the visual user interface logic 125 is configured to facilitate user interaction with the graphical user interface to manipulate check point information).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein a set of forecast end date ranges is displayed on a forecast user interface element of the invention of invention of Ponce the Leon and Newpol et al. to further specify wherein the set of forecast end date ranges is displayed in response to receiving a user hover input with respect to the user interface element of the invention of Sen et al. because doing so would allow a program manager to view additional project details by hovering on the respective check point indicia (see Sen et al., Paragraph 0053). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over Ponce de Leon (US 2008/0195452 A1), in view of Newpol et al. (US 2011/0302090 A1), in further view of Johnson et al. (US 2016/0063192 A1).
Regarding claim 34 (New), which is dependent of claim 28, the combination of Ponce de Leon, Newpol et al., and Sen et al. discloses all the limitations in claim 28. Although Ponce de Leon discloses calculating a forecast for the plurality of activities and/or for the overall project using a Monte Carlo simulation (see Paragraphs 0092 & 0177, duration probability distribution), the combination of Ponce de Leon, Newpol et al., and Sen et al. does not specifically disclose wherein the forecast includes a static error model.
However, Johnson et al. further discloses wherein the set of forecast models includes a static error model; and the static error model is generated in part using a user-defined static error value (Paragraph 0062, Referring to FIG. 2, an example Duration Estimator module is shown. Duration estimation is accomplished depending upon the information initially available, for example a history of procedures 24, and then a learning loop 23 is implemented that reduces the forecast error by incorporating additional information such as accurate case times and well measured descriptive attributes that serve as leading indicators. In an embodiment of duration estimation as shown in FIG. 2, a procedure is scheduled with average known time and variance at step 21 for a procedure, e.g. in the operating room (OR) at step 22. Additionally, the history 24, like attributes 25, and variation explained at steps 26 and 27 are then incorporated into forecasting and scheduling).
It would have been obvious to one ordinary skill in the art at the time the invention was filed to modify the user interface communicably coupled to a project management server application hosted by a server, wherein the server application calculates a forecasted completion/end date for the plurality of activities and/or for the overall project of the invention of Ponce the Leon to further incorporate wherein the forecast includes a static error model of the invention of Johnson et al. because doing so would allow the model to include a learning loop that reduces the forecast error by incorporating additional information such as accurate case times and well measured descriptive attributes that serve as leading indicators (see Johnson et al., Paragraph 0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Henry et al. (US 2009/0055237 A1) – FIG. 9 depicts the interface of FIG. 7 illustrating the effect of an edit to the work remaining range of a work item that could potentially cause the work item to miss its promise date. As illustrated, the work remaining range of work item 420g has been edited. The facility has re-estimated at least the earliest expected finish date, expected finish date, latest expected finish date and latest finish date for work item 420g, and generated a revised u-bar 710g to reflect the changed estimates. As illustrated, the work item 420g has a later latest finish date, which extends the corresponding u-bar 710g rightwards (i.e., forward in time). U-bar 710g now overlaps marker 715, which represents the promise date for work item 420g. The facility can thus visually inform a user that there is a significant likelihood or probability that work item 420g will not be completed by its promise date (see at least Paragraph 0060).
Dromgold (AU 2005206586 A) – discloses a computer based method and system is provided for facilitating the management of a project. The method includes receiving task data, associated resource data, associated timing data and associated task-related dependency data. This data is typically arranged to be viewed in a task-centric manner through a task-centric display interface where for each task or event all corresponding resources and a series of attributes associated with the tasks or events including the timing of the tasks or events, the human resources allocated to the tasks or events and task related dependency links. The task, timing and task-related dependency data is then grouped for each resource and the entries may be stored in a data store. The grouped data is then graphically represented on a resource-centric display interface from a resource-centric perspective as a compilation of a project management plan so that for each resource, the task, timing and task-related dependency data is collectively displayed relative to said resource in a one-to-many relationship (see at least Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJORIE PUJOLS-CRUZ whose telephone number is (571)272-4668. The examiner can normally be reached Mon-Thru 7:30 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia H Munson can be reached at (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARJORIE PUJOLS-CRUZ/Examiner, Art Unit 3624
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