DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is responsive to Applicant's amendment filed on 25 February 2026. Applicant’s amendment on 25 February 2026 amended Claims 1-3, 9, and 10. Currently Claims 1-5, 7, 9, and 10 are pending and have been examined. Claims 6 and 8 have been canceled. The Examiner notes that the 101 rejection has been maintained.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 25 February 2026 has been entered.
Response to Arguments
The Applicant argues on page 9 that “Applicant respectfully traverses all of these rejections. Nevertheless, without conceding to the merits of the Examiner’s rejections, the claims have been amended, as set forth above. Applicant respectfully submits that all of these rejections are moot”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner points out that the Applicant's sole 101 argument is that the rejection is moot in view of the claim amendments. While the claims have been amended to include additional limitations such as authentication of user terminals at different authority levels, role-based display of contribution degrees, and a graphical user interface with input and output areas, these amendments do not overcome the 101 rejection because the fundamental character of the claims remains directed to the abstract idea of estimating contribution degrees of project members based on gathered project, task, and member information a concept that, as previously explained, corresponds to a mental process that a project manager could perform manually or with pen and paper. The newly added authentication and role-based display limitations are recited at a high level of generality and amount to no more than generic computer functions that implement the abstract idea in the manner of "apply it," and do not integrate the judicial exception into a practical application under Step 2A Prong 2. Furthermore, these additional elements do not provide significantly more under Step 2B, as authentication and role-based access control are well-understood, routine, and conventional computer functions in the relevant art. Accordingly, the 101 rejection is therefore maintained.
The Applicant argues on pages 9-10 that “without conceding to the merits of the Examiner’s rejection, the claims have been amended, as set forth above… Applicant respectfully submits that the cited references, and any combination thereof, fail to teach or suggest the above features and, therefore, claim 1 is patentable for at least these reasons…”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner points out that as noted in the MPEP 714.02 the Applicant shall “distinctly and specifically points out the supposed errors in the Examiner’s action and must reply to every ground of objection and rejection in the prior Office action. The reply must present arguments pointing out the specific distinctions believed to render the claims, including any newly presented claims, patentable over any applied references. It is viewed that the Applicant has not provided arguments pointing out the specific distinctions believed to render the claims patentable, and as such the Examiner notes that upon further examination the claims are taught by the currently applied prior art. The rejection is therefore maintained.
Applicant's arguments filed 25 February 2026 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
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 1–5, 7, 9, and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite the abstract idea of collecting and analyzing project, task, and member information to estimate contribution degrees of project members, which corresponds to a mental process performable in the human mind. This judicial exception is not integrated into a practical application because the recited computer components, including processors, memory, graphical user interfaces, and authentication functions, are recited at a high level of generality and amount to no more than generic computer implementation of the abstract idea. The claims do not include additional elements sufficient to amount to significantly more than the judicial exception because the additional elements perform well-understood, routine, and conventional computer functions that merely implement the abstract idea.
STEP 1
Regarding Step 1 of the Subject Matter Eligibility Test, claims 1–5 and 7 are directed to a system (machine), claim 9 is directed to a method (process), and claim 10 is directed to a non-transitory computer-readable medium (manufacture). Therefore, all pending claims fall within the statutory categories of invention.
STEP 2 Prong One
The claims recite an abstract idea. Specifically, independent claims 1, 9, and 10 each recite the following key limitations:
Receiving project information including at least one of a type, a goal, budget, or outcomes of a project and achievement percentages of required tasks;
Receiving task information including at least one of levels of difficulties or market values of a plurality of required tasks required to carry out the project;
Receiving member information including different skills of the plurality of members and at least one of respective tasks, roles, or knowledge of the respective members;
Estimating the respective contribution degrees of each of the plurality of members using a trained model that receives the project, task, and member information as input and outputs contribution degrees; and
Concurrently presenting, for comparison, the estimated contribution degrees of the respective members.
Mental Process Grouping Analysis
These limitations, under their broadest reasonable interpretation, cover performance of the limitations in the human mind, including observation, evaluation, judgment, and opinion. Specifically, the core activity of the claimed invention gathering information about a project, its tasks, and its members and determining how much each member contributed is precisely the kind of evaluative activity that a project manager, client, or team lead could and does perform mentally, or at most with pen and paper. The step of "estimating the respective contribution degrees of each of the plurality of members" based on project information, task information, and member information encompasses a mental judgment that can be performed by a person who observes the project details, reviews the tasks and their difficulty, and evaluates each member’s skills, roles, and knowledge to arrive at a contribution assessment. The specification itself confirms this when it states that “the determination and the evaluation of contribution degrees of the respective members in such a project are generally made by a client, a manager (e.g., a project manager) or the like” and that “it is difficult, however, for the project manager to objectively and appropriately evaluate the contribution degrees of the respective members” (Specification, Background Art, page 1, lines 15–20). This express acknowledgment that the underlying activity is one that humans have historically performed confirms that the claimed process falls squarely within the mental processes grouping. The mere recitation of a trained model and a processor does not remove the limitations from the mental processes grouping, as has been established by ongoing guidance that claims containing generic processor language are still viewed as mental processes when they contain limitations that can practically be performed in the human mind. See MPEP 2106.04(a)(2), subsection III.
Furthermore, the specification describes the claimed invention as being directed to the mental activity of data gathering and data analysis to determine a contribution degree, confirming that the underlying concept is not a technical improvement but rather an administrative and managerial judgment task. The Examiner has reviewed the disclosure and determined that the underlying claimed invention is described as a concept performed in the human mind and/or with the aid of pen and paper, such that the applicant is merely claiming that concept performed on a generic computer, in a computer environment, or using a computer as a tool to perform the concept.
STEP 2A Prong Two
Identification of Additional Elements
The claims recite the following additional elements beyond the identified abstract idea:
At least one memory storing instructions;
At least one processor configured to execute the instructions;
Authentication of user terminals at different authority levels (project manager level and project member level);
A graphical user interface (GUI) on a display device including an input area and an output area;
Role-based display of contribution degrees depending on authentication level (project manager sees all members’ degrees; each project member sees only their own degree); and
A plurality of user terminals connected via a network to the contribution degree estimation apparatus.
Analysis of Additional Elements
Improvement to Technology or Technical Field (MPEP 2106.05(a)):
The claims do not recite an improvement to the functioning of a computer or to any other technology or technical field. The specification does not describe any technical problem with prior computer systems, nor does it describe any improvement to computer architecture, processing efficiency, memory utilization, or network performance. Rather, the specification frames the problem in administrative and managerial terms: that it is difficult for project managers to objectively evaluate contribution degrees of project members (Specification, page 1, lines 15–20). The claimed solution using a trained model to estimate contribution degrees and presenting them via a GUI describes using a computer as a tool to perform the abstract evaluative task more efficiently, not an improvement to computer functionality itself. The authentication and role-based display limitations similarly do not improve computer technology; they describe conventional access control applied to the display of evaluation results. The disclosure does not provide sufficient detail such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement to technology. Any assertion of improvement is presented in a conclusory manner without the detail necessary to demonstrate a technical improvement apparent to a person of ordinary skill in the art. See MPEP 2106.05(a).
Particular Machine (MPEP 2106.05(b)):
The claims do not recite use of a particular machine that imposes meaningful limits on the claim. The recited “at least one processor,” “at least one memory,” “user terminals,” “a network,” and “a display device” are generic computing components recited at a high level of generality. These components do not impose any meaningful structural or functional constraint on the abstract idea beyond requiring that it be performed on a computer. The recitation of a “trained model” similarly does not identify a particular machine, as the trained model is described generically as a machine learning model using a neural network (Specification, pages 7–9), without any specific structural constraints that would differentiate it from conventional machine learning implementations. See MPEP 2106.05(b).
Mere Instructions to Apply the Exception (MPEP 2106.05(f)):
The additional elements amount to no more than mere instructions to implement the abstract idea on a generic computer. The claims recite generic computing components (processor, memory, network, display device, user terminals) performing generic computing functions (receiving data, processing data with a model, displaying results, authenticating users). The “authenticate” steps recite a conventional computer security function at a high level of generality without specifying any particular authentication mechanism or technical innovation. The GUI with input and output areas is a conventional data entry and display mechanism. Using a computer for receiving, providing, inputting, presenting, and updating data resulting from the mental process of estimating contribution degrees merely implements the abstract idea in the manner of “apply it” and does not provide something more to make the claimed invention patent eligible. The claimed limitations of a computing device do not constrain the abstract idea to a particular technological environment and do not provide significantly more. See Alice Corp. v. CLS Bank Int’l); MPEP 2106.05(f).
Insignificant Extra-Solution Activity (MPEP 2106.05(g)):
The additional elements of receiving project, task, and member information and presenting the estimated contribution degrees on a GUI constitute insignificant extra-solution activity. The “receiving” and “authenticate” steps are mere data gathering and access control steps that are necessary precursors to the primary abstract process of estimating contribution degrees. The “presenting” and “concurrently present” steps are mere output steps that display the results of the abstract estimating step. The “update the output area” step is a routine data display refresh function. None of these additional elements impose any meaningful limit on practicing the abstract idea of estimating contribution degrees. See MPEP 2106.05(g).
STEP 2B
As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using generic computer components. The same analysis applies in Step 2B mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(f). In order for the claims to be viewed as significantly more, the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates. Here, the recited computer components (processor, memory, GUI, network, user terminals) function solely as conventional tools for implementing the abstract estimating process, rather than playing a significant part in permitting the claimed method to be performed in a new or unconventional way.
Well-Understood, Routine, Conventional Activity Analysis
The additional elements, when considered individually and in combination, are well-understood, routine, and conventional activities in the field. Specifically:
Using a processor and memory to execute instructions. The courts have recognized that the use of a generic processor and memory to execute software instructions is well-understood, routine, and conventional. See Alice Corp., 573 U.S. at 225–26; MPEP 2106.05(d)(II).
Receiving and storing data - The courts have recognized receiving and storing data as well-understood, routine, and conventional computer activity. See Elec. Power Grp., LLC v. Alstom S.A. (collecting information is an abstract idea; “receiving” and “storing” data are routine); MPEP 2106.05(d)(II).
Providing a graphical user interface with input and output areas - Displaying data on a GUI with input and output fields is a basic, well-understood computing function. See TLI Commc’ns LLC v. AV Auto. LLC, (generic computer display functions do not supply significantly more); MPEP 2106.05(d)(II).
Authenticating user terminals and providing role-based access control - User authentication and role-based access control are foundational, well-understood, and conventional computer security techniques that have been widely used for decades. See Intellectual Ventures I LLC v. Symantec Corp.; MPEP 2106.05(d)(II). The Examiner also notes that the implementing steps can be found in Del Balso, which discloses how the claims alone and in combination are viewed as well-understood, routine, and conventional based on point 3 of the Berkheimer Memo and subsequent evidence.
Using a trained machine learning model - Applying machine learning to perform data analysis and estimation is a conventional computer function in the relevant field. The specification itself describes the trained model as a neural network using supervised learning (Specification, page 7, lines 15–20), which is a generic machine learning approach. The specification does not describe any novel or unconventional configuration of the neural network itself.
Presenting data concurrently for comparison on a display - Displaying multiple data items concurrently on a screen for comparison purposes is a well-understood, routine, and conventional output function. See Electric Power Group.
Considering the additional elements individually and in combination, the claims do not include additional elements sufficient to amount to significantly more than the judicial exception. Each additional element either performs a generic computing function, implements conventional computer security, or provides routine data input/output activity. Together, these elements do not transform the abstract idea of estimating contribution degrees into a patent-eligible application. The claims are not patent eligible.
Dependent Claims Analysis
The dependent claims do not add limitations that integrate the judicial exception into a practical application or provide an inventive concept:
Claims 2 and 3: These claims add limitations directed to weighting the trained model for each required task or each piece of project information (e.g., level of difficulty, market value, importance). These limitations further define the parameters of the abstract estimation process and constitute additional data analysis steps that narrow the metes and bounds of the abstract idea but do not provide something more. Applying weights to data inputs in a machine learning model is a well-understood and routine aspect of machine learning that does not impose a meaningful limit on the abstract idea or integrate it into a practical application.
Claim 4: This claim adds receiving project information including achievement percentages of the required tasks and estimating different contribution degrees for the different achievement percentages. This further specifies the type of input data gathered for the abstract estimating process. Receiving and using percentage data as input to an estimation process is mere data gathering and data analysis that constitutes insignificant extra-solution activity and does not integrate the judicial exception into a practical application.
Claim 5: This claim adds selecting one of a plurality of stored trained models to perform the estimation. Selecting among stored models is a routine and conventional data processing step that does not meaningfully limit the abstract idea or provide significantly more.
Claim 7: This claim adds outputting reasons for estimating the respective contribution degrees. Generating and outputting explanatory text alongside estimation results is a conventional output function that constitutes insignificant post-solution activity and does not integrate the judicial exception into a practical application or provide an inventive concept.
Claims 9 and 10: These claims recite the same or similar limitations as independent claim 1 in method and computer-readable medium format, respectively. For all the reasons set forth above with respect to claim 1, these claims are likewise directed to an abstract idea without significantly more.
For the foregoing reasons, claims 1–5, 7, 9, and 10 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. The claims are directed to the abstract idea of estimating contribution degrees of project members based on project, task, and member information a mental process without significantly more. The additional elements recited in the claims, whether considered individually or in combination, amount to no more than generic computer implementation of the abstract idea, well-understood and routine computer functions, and insignificant extra-solution activity. Accordingly, the 101 rejection is maintained.
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 may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7, 9, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Del Balso et al. (U.S. Patent Publication 2018/0018610 A1) (hereafter Del Balso) in view of Kitabatake (U.S. Patent Publication 2010/0223092 A1) in further view of Brooks (U.S. Patent Publication 2008/0195464 A1), in further view of Maluf et al. (U.S. Patent 8,224,472 B1).
Referring to Claim 1, Del Balso teaches a contribution degree estimation system comprising:
a plurality of user terminals used by a plurality of members including a project manager and a plurality of project members (see; [0042]–[0043] of Del Balso teaches a supervisor client device (project manager's terminal) and user client devices (project members' terminals), all operated by different members of the project team, and col. [0063]: In various examples, the supervisor client device is used by a supervisor who is overseeing the project).
a contribution degree estimation apparatus connected via a network to the plurality of user terminals (see; par. [0042]-[0043] of Del Balso teaches a Server system 112 "provides data retrieval, project task analysis, and project monitoring" and is accessed "through a network 32 (e.g., the Internet and/or a local network) by users of client devices." This is a central estimation apparatus connected via network to all user terminals, par. [0005] which includes task completion data (i.e. contribution degree)).
at least one memory storing instructions (see; par. [0113] of Del Balso teaches essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data.).
at least one processor configured to execute the instructions to (see; par. [0042] of Del Balso teaches The server system comprises one or more processors, software components, and databases and par. [0113] of Del Bals processors suitable for the execution of a computer program include...both general and special purpose microprocessors, and any one or more processors of any kind of digital computer...a processor will receive instructions and data from a read-only memory or a random access memory).
estimate the respective contribution degrees of each of the respective plurality of members using a trained model that has performed learning so as to receive, as input, the project information, the task information, and the member information and output respective contribution degrees of each of the plurality of respective members in the project (see; Abstract of Del Balso teaches Del Balso, training a predictive model using the collected data...using the predictive model to determine weights for uncompleted tasks associated with the product. Par. [0047] the training data is used to train one or more predictive models (e.g., classifiers) in the classifier module, so that predictions can be made for new mortgages. Par. [0049]: "after the classifier module has been trained with the training data, the classifier module can be used to predict certain characteristics related to the completion of tasks in a project... The project data may be or include... information about the loan applicant or customer... loan conditions... and tasks that must be performed... The classifier module receives the project data and provides predictions related to the new loan. For example, the classifier module may predict weights associated with tasks that need to be performed." the trained model receives, as input, project information, task information, and member (customer) information, and outputs predictions for each task/member. Par. [0087] Individuals or equipment performing the tasks may also be evaluated and/or monitored by comparing their performance with expectations" the system estimates per-individual contribution degree. Par. [0005] training a predictive model using the task completion data, the predictive model configured to determine a weight for an uncompleted task, given a task type and attributes of a current customer associated with the uncompleted task" trained model that receives task and member (customer) information as input and outputs contribution weights per member).
an input area configured to receive project information including at least one of a type, a goal, budget, or outcomes of a project and achievement percentages of required tasks, task information including at least one of levels of difficulties or market values of a plurality of required tasks that are required to carry out the project (see; par. [0065] of Del Balso teaches The configuration file "describes or includes information about a borrower, a property, and loan terms for the loan project. Information about the borrower may include, for example, income, credit rating... the loan terms information may include, for example, interest rate, term, down payment and project information including budget and outcomes. Achievement percentages of required tasks. Par. [0056] the queue system tracks an indication of how much of the task has been completed, an indication of how much more time will be required to complete the task. par. [0070] if the user has not completed 25% of the tasks for a project, the bar in the user column 914 for that project may be 25% red achievement percentage display. Task information (levels of difficulties or market values): par. [0048] Training data includes "how difficult it was to complete the task (e.g., on a scale of 1 to 10, with 10 being most difficult)" (levels of difficulty) and "conditions related to the loan product (e.g., amount of the loan, term of the loan, interest rate, down payment amount, value of property being purchased) (market values), par. [0049] a weight for a task is or includes a predicted time it will take to complete the task, an estimate of how difficult it will be to complete the task, and/or a measure of a complexity of the task. Project involving members with different skills).
Del Balso does not explicitly disclose the following limitation, however,
Kitabatake teaches an output area configured to: concurrently present, for comparison, respective estimated contribution degrees of each of the plurality of respective members (see; Figure 55 of Kitabatake teaches show concurrent display of contribution degrees for multiple workers on one page, enabling side-by-side visual comparison of individual contribution degrees across all project members).
The Examiner notes that Del Balso teaches similar to the instant application teaches optimizing parallel task completion. Specifically, Del Balso discloses the performing task associated with projects including collecting data related to tasks, training, and using predictive models to determine weights for tasks and users it is therefore viewed as analogous art in the same field of endeavor. Additionally, Kitabatake teaches information processing program to be used to store and process information such that related data including work and task data are analyzed and as it is comparable in certain respects to Del Balso which optimizing parallel task completion as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Del Balso discloses the performing task associated with projects including collecting data related to tasks, training, and using predictive models to determine weights for tasks and users. However, Del Balso fails to disclose an output area configured to: concurrently present, for comparison, respective estimated contribution degrees of each of the plurality of respective members.
Kitabatake discloses an output area configured to: concurrently present, for comparison, respective estimated contribution degrees of each of the plurality of respective members.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Del Balso an output area configured to: concurrently present, for comparison, respective estimated contribution degrees of each of the plurality of respective members as taught by Kitabatake since the claimed invention is merely a combination of old elements, and in the combination each element merely 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. Additionally, Del Balso, and Kitabatake teach the collecting and analysis of data in order to maximize the utilization of workers using associated tasks during program management processes and they do not contradict or diminish the other alone or when combined.
Del Balso in view of Kitabatake does not explicitly disclose the following limitation, however,
Brooks teaches update the output area to present the estimated contribution degrees in response to the received project, task, and member information (see; par. [0010] of Brooks teaches the utilization of collecting data with respect to the relative contribution to the task and par. [0096] using that information to estimate future project impacts).
The Examiner notes that Del Balso teaches similar to the instant application teaches optimizing parallel task completion. Specifically, Del Balso discloses the performing task associated with projects including collecting data related to tasks, training, and using predictive models to determine weights for tasks and users it is therefore viewed as analogous art in the same field of endeavor. Additionally, Kitabatake teaches information processing program to be used to store and process information such that related data including work and task data are analyzed and as it is comparable in certain respects to Del Balso which optimizing parallel task completion as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Brooks teaches collecting, calculating, and reporting quantifiable peer feedback on relative contribution of team members and as it is comparable in certain respects to Del Balso and Kitabatake which optimizing parallel task completion as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Del Balso and Kitabatake discloses the performing task associated with projects including collecting data related to tasks, training, and using predictive models to determine weights for tasks and users. However, Del Balso and Kitabatake fails to disclose update the output area to present the estimated contribution degrees in response to the received project, task, and member information.
Kitabatake discloses update the output area to present the estimated contribution degrees in response to the received project, task, and member information.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Del Balso and Kitabatake update the output area to present the estimated contribution degrees in response to the received project, task, and member information as taught by Brooks since the claimed invention is merely a combination of old elements, and in the combination each element merely 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. Additionally, Del Balso, Kitabatake, and Brooks teach the collecting and analysis of data in order to maximize the utilization of workers using associated tasks during program management processes and they do not contradict or diminish the other alone or when combined.
Maluf teaches authenticate a user terminal used by the project manager according to an authority level of the project manager (see; col. 6, lines (30-49) of Maluf teaches the system requires the client to perform a "USER LOGIN" step; the system then "VALIDATE[S]" the sequence. If validation fails, access is declined; if validated, the system provides the "MAIN MENU." This is the authentication gate keyed to user identity. col. 12, lines (31-62) and The XDB server optionally includes an authentication module that authenticates the information provider and/or the information requester, using a password, a biometric indicium, a subscriber list or another means to distinguish authenticated users from non-authenticated entities. Direct, explicit teaching of a server-side authentication module. col. 2, lines (24-35). The invention allows a PMT administrator to establish permissions (read, write, edit, delete) for a user for each report role-based, per-user permission levels constitute authentication according to an authority level. The PMT administrator's higher-level permissions are established differently from ordinary workers', directly teaching authentication according to authority level), and
authenticate user terminals used by the plurality of project members according to a respective authority level of each of the plurality of project members (see; col. 12, line (31-62 of Maluf teaches the authentication module, col. 12, lines (48–58) and the PMT administrator permission framework, col. 2, lines (24-44) operate on a per-user basis "a user for each report" establishes that each individual user's permissions (authority level) are set separately, teaching authentication according to the respective authority level of each project member and col. 2, lines 24–44: "Where a user who does not have at least review-access to a report explicitly requests that report, the system optionally informs this user of the lack of review-access" confirms that individual users have individual (respective) access levels, i.e., respective authority levels, which determine what they may view upon authentication, and
receive member information from each of the user terminals that have been authenticated (see; col.17, lines (12-45) of Maluf teaches when the user's identity is authenticated, to receive from the user a specification of information the user seeks" explicitly teaches receiving information from a user after authentication, (i.e., from an authenticated terminal), col. 6, lines 30-49: After the login is validated, the user proceeds to the MAIN MENU and submits Monthly Reports, Task Plan Reports, Budget Reports, etc. the system receives member-specific information (including worker identity, skills, task assignments, and performance data) from each authenticated terminal), and
provide a graphical user interface on a display device, the graphical user interface including (see; Fig. 17A and 17 B of Maluf teaches General an interface that provides Project/Task Information" interactive input screen includes fields for Title, Task Lead, NASA Relevance, Objectives, and "Brief Description of Project/Task" project type, goal, and outcomes. Abstract - system provides monthly reports, budget reports, schedule reports tracking project budget and outcomes), and
the project being a plan involving a plurality of members with different skills, and member information including different skills of the plurality of members and at least one of respective tasks, roles, or knowledge of the plurality of members (see; Fig. 17A and 17 B and Maluf, col. 5, line (46) – col. 6, line (29) "The human model optionally includes...relevant worker skills (tools and equipment used, techniques used and processes known); worker experience (roles played in past and present assignments)." col. 8, lines (6-49) "The invention includes a searchable skill set module that lists a name of each worker...and a list of skills possessed by each such worker" member information including different skills. Member information (tasks, roles, knowledge) Fig. 18: "Human Resources" table with Name, Organization, Role, % Time member information including respective roles. col. 5, lines (10-19) The human model includes "worker experience (roles played in past and present assignments) ... presently assigned tasks; and present workload" member information including respective tasks and knowledge. GUI presentation), and
cause a display device of each of the authenticated user terminals to present the contribution degrees in different aspects depending on an authentication level via the authenticated user terminal, such that the authenticated user terminal of the project manager presents the contribution degrees of the plurality of project members, and the authenticated user terminals of the plurality of project members presents only one of the contribution degrees of the respective one of the plurality of project members (see; Fig. 5A of Maluf teaches Fig. 5A shows the individual worker view the screen is labeled "All Monthly Reports View/Submit your monthly reports" followed by a table listing that individual's task files and Task Names with Contribution and POC columns. Each authenticated project member sees and submits only their own monthly reports this is the "project members present only one contribution aspect of the display. Fig. 8 shows the project manager/reviewer view the screen header reads "Aggregated Monthly Reports" with the subheading "Reviewers can view all monthly reports on one page." The table below displays rows for every worker each with Technical, Schedule, Budget, and Management status columns the authenticated project manager sees all project members' report data simultaneously on one page. col. 2, lines (24-35) "Where a user who does not have at least review-access to a report explicitly requests that report, the system optionally informs this user of the lack of review-access" directly confirms that different authentication levels produce different display content. Workers without review-access cannot see others' data; reviewers/managers can. This is the "different aspects depending on an authentication level" teaching. Maluf, col. 7, lines (4-13): "In a Monthly Report, a project manager reports the status of each of a specified set of tasks...optionally including the number of FTE operational workers presently working on each task" the project manager's view encompasses all workers' task status, while each operational worker reports only their own status. col. 12, lines (63) - col., 13, line (5) The access control module controls access to the XDB data store module by XDB server modules, such as the query module. This access may require provision of a password or other authentication mechanism the authenticated user identity, controlled by the access control module, determines what data is accessible and therefore displayable to each user terminal. Together, Fig. 5A (individual worker view = "present only one contribution degree, the respective member's own") and Fig. 8 (aggregated reviewer view = "project manager presents the contribution degrees of all project members") constitute direct, unambiguous teaching of the complete pivotal two-tier role-differentiated display limitation).
The Examiner notes that Del Balso teaches similar to the instant application teaches optimizing parallel task completion. Specifically, Del Balso discloses the performing task associated with projects including collecting data related to tasks, training, and using predictive models to determine weights for tasks and users it is therefore viewed as analogous art in the same field of endeavor. The Examiner notes that Del Balso teaches similar to the instant application teaches optimizing parallel task completion. Specifically, Del Balso discloses the performing task associated with projects including collecting data related to tasks, training, and using predictive models to determine weights for tasks and users it is therefore viewed as analogous art in the same field of endeavor. Additionally, Kitabatake teaches information processing program to be used to store and process information such that related data including work and task data are analyzed and as it is comparable in certain respects to Del Balso which optimizing parallel task completion as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Brooks teaches collecting, calculating, and reporting quantifiable peer feedback on relative contribution of team members and as it is comparable in certain respects to Del Balso and Kitabatake which optimizing parallel task completion as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Maluf teaches enhanced project management including multiple tasks and plurality of workers and as it is comparable in certain respects to Del Balso, Kitabatake, and Brooks which optimizing parallel task completion as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Del Balso, Kitabatake, and Brooks discloses the performing task associated with projects including collecting data related to tasks, training, and using predictive models to determine weights for tasks and users. However, Del Balso, Kitabatake, and Brooks fails to disclose authenticate a user terminal used by the project manager according to an authority level of the project manager, authenticate user terminals used by the plurality of project members according to a respective authority level of each of the plurality of project members, receive member information from each of the user terminals that have been authenticated, provide a graphical user interface on a display device, the graphical user interface including, the project being a plan involving a plurality of members with different skills, and member information including different skills of the plurality of members and at least one of respective tasks, roles, or knowledge of the plurality of members and cause a display device of each of the authenticated user terminals to present the contribution degrees in different aspects depending on an authentication level via the authenticated user terminal, such that the authenticated user terminal of the project manager presents the contribution degrees of the plurality of project members, and the authenticated user terminals of the plurality of project members presents only one of the contribution degrees of the respective one of the plurality of project members.
Maluf discloses authenticate a user terminal used by the project manager according to an authority level of the project manager, authenticate user terminals used by the plurality of project members according to a respective authority level of each of the plurality of project members, receive member information from each of the user terminals that have been authenticated, provide a graphical user interface on a display device, the graphical user interface including, the project being a plan involving a plurality of members with different skills, and member information including different skills of the plurality of members and at least one of respective tasks, roles, or knowledge of the plurality of members and cause a display device of each of the authenticated user terminals to present the contribution degrees in different aspects depending on an authentication level via the authenticated user terminal, such that the authenticated user terminal of the project manager presents the contribution degrees of the plurality of project members, and the authenticated user terminals of the plurality of project members presents only one of the contribution degrees of the respective one of the plurality of project members.
.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Del Balso, Kitabatake, and Brooks authenticate a user terminal used by the project manager according to an authority level of the project manager, authenticate user terminals used by the plurality of project members according to a respective authority level of each of the plurality of project members, receive member information from each of the user terminals that have been authenticated, provide a graphical user interface on a display device, the graphical user interface including, the project being a plan involving a plurality of members with different skills, and member information including different skills of the plurality of members and at least one of respective tasks, roles, or knowledge of the plurality of members and cause a display device of each of the authenticated user terminals to present the contribution degrees in different aspects depending on an authentication level via the authenticated user terminal, such that the authenticated user terminal of the project manager presents the contribution degrees of the plurality of project members, and the authenticated user terminals of the plurality of project members presents only one of the contribution degrees of the respective one of the plurality of project members as taught by Maluf since the claimed invention is merely a combination of old elements, and in the combination each element merely 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. Additionally, Del Balso, Kitabatake, Brooks and Maluf teach the collecting and analysis of data in order to maximize the utilization of workers using associated tasks during program management processes and they do not contradict or diminish the other alone or when combined.
Referring to Claim 2, see discussion of claim 1 above, while Del Balso in view of Kitabatake in further view of Brooks in further view of Maluf teaches the system above, Del Balso further discloses a system having the limitations of:
the trained model is weighted for each of the required tasks in the project, (see; Abstract and par. [0005] of Del Balso teaches assigning respective weights to a plurality of uncompleted tasks associated with the first product, the weight for each uncompleted task determined by the predictive model" the trained (predictive) model assigns a weight to each required task individually par. [0049] the classifier module may predict weights associated with tasks that need to be performed to close the new loan. In certain instances, a weight for a task is or includes a predicted time it will take to complete the task, an estimate of how difficult it will be to complete the task, and/or a measure of a complexity of the task and the model is weighted per required task).
the at least one processor is configured to execute the instructions to estimate the respective contribution degrees of each of the plurality of members by associating the required tasks with the tasks assigned to the respective one of the plurality of member (see; par. [0060] of Del Balso teaches tasks may be assigned according to a role associated with one or more users or machines. For example, a user or a machine may have a particular role in a project, and tasks associated with the role may be assigned to that particular user or machine. teaches associating required tasks with the tasks assigned to respective members (by role). Par. [0058] tasks may be assigned based on capabilities associated with performing tasks in a queue... a queue associated with a more experienced person and/or a more capable machine... may be assigned tasks having greater complexity and/or requiring specific skills, estimation of contribution/capability is done by associating required tasks (based on complexity) with tasks assigned to respective members (based on their capabilities).).
Referring to Claim 3, see discussion of claim 1 above, while Del Balso in view of Kitabatake in further view of Brooks in further view of Maluf teaches the system above, Del Balso further discloses a system having the limitations of:
the trained model is weighted for each piece of the project information including at least one of a level of difficulty, a market value, and importance of the required task (see; par. [0055] of Del Balso teaches a machine learning model that is used to predict based on factors including responsibilities (i.e. importance of tasks), par. [0014] where weighting for each task of a project).
the at least one processor is configured to execute the instructions to estimate different contribution degrees in accordance with the trained model for each piece of the project information (see; par. [0010] of Del Balso teaches estimating the degree of collaboration (i.e. contribution degree) on a job which entails, par. [0045] estimating collaboration positions, tasks, or responsibilities).
Referring to Claim 4, see discussion of claim 1 above, while Del Balso in view of Kitabatake in further view of Brooks in further view of Maluf teaches the system above, Del Balso further discloses a system having the limitations of:
The contribution degree estimation system according to wherein the at least one processor configured to execute the instructions to receive the project information including achievement percentages of the respective required tasks (see; par. [0048] of Del Balso teaches the monitoring of changes in responsibilities and tasks (i.e. percentages), where par. [0010] estimating the degree of collaboration (i.e. contribution degree) on a job includes par. [0045] estimating collaboration positions, tasks, or responsibilities).
estimate different contribution degrees for the different achievement percentages (see; par. [0105] of Del Balso teaches measuring the influence or contribution using weighting (i.e. degree)).
Referring to Claim 5, see discussion of claim 1 above, while Del Balso in view of Kitabatake in further view of Brooks in further view of Maluf teaches the system above, Del Balso further discloses a system having the limitations of:
the contribution degree estimation system according to wherein the at least one processor configured to execute the instructions to select one of a plurality of trained models that are stored and estimates the contribution degrees (see; par. [0017] of Del Balso teaches using trained models, par. [0047] to retrieve and process utilizing skill and task information to determine an estimate of the degree of collaboration).
Referring to Claim 7, see discussion of claim 1 above, while Del Balso in view of Kitabatake in further view of Brooks in further view of Maluf teaches the system above, Del Balso further discloses a system having the limitations of,
contribution degree estimation system according to wherein the at least one processor configured to execute the instructions to output reasons for estimating the respective contribution degrees (see; par. [0010] of Del Balso teaches an example of estimates where predictions are generated (i.e. output) providing classifications that are used for the scoring, par. [0043] where the estimate takes into account project management tasks, par. [0014] where the assigning of weights to tasks and utilize a predictive model to report on the uncompleted tasks, par. [0010] in order to estimate the degree of collaboration on a job (i.e. whom performs what and to what degree)).
Referring to Claim 9, Del Balso in view of Kitabatake in further view of Brooks in further view of Maluf teaches a contribution degree estimation method. Claim 9 recites the same or similar limitations as those addressed above in claim 1, Claim 9 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 10, Del Balso in view of Kitabatake in further view of Brooks in further view of Maluf teaches a non-transitory computer readable medium. Claim 10 recites the same or similar limitations as those addressed above in claim 1, Claim 10 is therefore rejected for the same reasons as set forth above in claim 1.
Conclusion
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/S.S.S/Examiner, Art Unit 3625
/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625