DETAILED ACTION
Status of the Application
The following is a Final Office Action. In response to Examiner's communication of July 1, 2025, Applicant, on December 27, 2025, amended claims 1 & 16. Claims 1-18 are now pending in this application and have been rejected below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Amendment
Applicant's amendments are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action. Therefore, these rejections are maintained below.
Applicant's amendments render moot the prior art rejections set forth in the previous action. Therefore, new grounds for rejection under 35 USC 103 necessitated by Applicant’s amendments are set forth below.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant argues that the do not recite an abstract idea because the claims cannot fall within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, or management of behavior or relationships or interactions between people since the claims are directed to data analysis, execution of an optimization engine and an analysis engine and at least one machine learning model, and displaying visualizations of data output by the executed engines based upon the analyses, the claims relate to steps executed by a computing device and resulting in a modification to a user interface and lack any recitation of an activity occurring between humans. Examiner respectfully disagrees.
Pursuant to 2019 Revised Patent Subject Matter Eligibility Guidance, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 56.
The limitations reciting of an optimization engine, an analysis engine, and at least one machine learning model are additional elements beyond the recite abstract idea addressed below under Prong 2 of Step 2A and Step 2B, and Prong 2 of Step 2A and Step 2B, these elements are generic computer components applying the recited abstract idea.
Under Prong 1 of Step 2A, Claim 1, and similarly claims 2-18, recites “performance optimization recommendations and modifying … performance-related data … comprising: accessing … data identifying a goal associated with a project assigned to a team associated with at least one organization; accessing … data identifying a goal associated with the at least one organization; analyzing … data relating to at least one characteristic of each of a plurality of members of the team; determining … a level of contribution of each of the plurality of members of the team to the goal associated with the at least one organization; determining … a level of contribution of each of the plurality of members of the team to the goal associated with the project; determining … a likelihood of the team accomplishing the goal associated with the project, wherein determining further comprises … to provide a probability of success in accomplishing the goal; modifying, … a visualization of the determinations …; and providing, …, an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training module associated with the area of improvement.” Claims 1-18, in view of the claim limitations, recite the abstract idea of evaluating performance of team members contributing effort to goals and projects of an organization by accessing data regarding goals of a team project and goals of the organization, analyzing data regarding characteristics of members of the team, determining a level of contribution of the members to the goals of the team project and the goals of the organization, determining a likelihood and probability of completion of completing the goal of the team project, modifying a visualization of the determinations, and providing an indication of an area of improvement to increase accomplishing the goal by the team and a training module for the area of improvement.
A claim recites certain methods of organizing human activity when the claim recites fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). 84 Fed. Reg. at 52; MPEP 2106.04(a)(2). Here, each of the above limitations manage business interactions humans in a team of an organization and provide instructions or rules to follow to manage the human behavior of team members of organizations to accomplish a goal by presenting the information regarding the area of improvement and associated training for the team to accomplish the goal and the determined level of contributions of the team members and likelihood of the team completing goals based on the human behavior of the goals to be completed by the team members, characteristics of the team members, and the level of contribution to those goals by the team members, Thus, the claims recite certain methods of organizing human activity.
In addition, a claim recites mental processes when the claim recites concepts performed in the human mind (including an observation, evaluation, judgment, opinion), wherein if the claim, under its broadest reasonable interpretation, covers the claim being practically performed in the mind but for the recitation of generic computer components, then the claim is in the mental process category. 84 Fed. Reg. 52 n.14; MPEP 2106.04(a)(2). Here, as a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited accessing data regarding goals of a team project and goals of the organization, analyzing data regarding characteristics of members of the team, determining a level of contribution of the members to the goals of the team project and the goals of the organization, determining a likelihood and probability of completion of completing the goal of the team project, modifying a visualization of the determinations, and providing an indication of an area of improvement to increase accomplishing the goal by the team and a training module for the area of improvement could all be reasonably interpreted as a human making observations of information regarding the goals and team members, a human performing an evaluation and using judgment based on the observed information to determine the level of contribution of team members toward completing goals and the likelihood of completing goals, a human presenting the determined information, area of improvement, and training module manually and/or with a pen and paper. Therefore, the claims recite mental processes.
Accordingly, since the claims recite a certain method of organizing human activity and mental processes, the claims recite an abstract idea under the first prong of Step 2A.
Applicant argues that, even if the Examiner maintains that the claims recite a judicial exception, under Prong Two of the revised Step 2A, the pending claims are integrated into a practical application of the exception and are patent eligible due to the execution of the optimization engine, the analysis engine, and the at least one machine learning module that execute the steps recited in the pending claims and execution of which results in display of a modified user interface including a visualization of data output by the optimization engine, as well as at least one training module, and the pending claims recite a novel, non-obvious method that provides a meaningful limit on any abstract idea that may be present in the pending claims. Examiner respectfully disagrees.
As noted above, in view of Prong 2 of Step 2A, we determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. The steps, training module, and alleged novel and non-obvious method in the pending claims referred to by Applicant that are executed by the various generic computer components referred to by Applicant are not additional elements beyond the recited abstract idea, but rather, they are part of and directed to the recited abstract idea for the reasons set forth above under Prong 1 of Step 2A.
The alleged novelty or non-obviousness of the abstract elements does not transform the abstract idea into a practical application. The search for an inventive concept under § 101 is distinct from demonstrating novel and non-obviousness. See SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2-3 (Fed Cir. May 15, 2018) (citing Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016). Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America at 2. Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at 3. “What is needed is an inventive concept in the non-abstract application realm.” Id. at 11.
With respect to the remaining elements, under Prong 2 of Step 2A, in claim 1, and similarly claims 2-18, the only additional elements beyond the recited abstract idea set forth above in Prong 1 of Step 2A are the recitations of “[a] method … a user interface displaying at least one visualization …, the method comprising,” “by an optimization engine,” “executing, by an analysis engine of the optimization engine, at least one machine learning model,“ “a user interface displaying,” and “by the optimization engine, via the modified user interface,” and individually and when viewed as an ordered combination, pursuant to the broadest reasonable interpretation, each of the additional elements are generic computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components, which is not sufficient to integrate an abstract idea into a practical application. See MPEP 2106.05(f). Further, these elements merely generally link the abstract idea to a field of use.
Applicant argues that the claims amount to "significantly more" than an abstract idea by reciting additional limitations including, by way of example, a novel and non-obvious optimization engine including the analysis engine executing at least one machine learning model; the optimization engine executing unconventional functionality to generate data and modify user interfaces to include visualizations of the generated data provide significantly more than an abstract idea relating to behavior between humans. Examiner respectfully disagrees.
As noted above, in view of Step 2B, we determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, are significantly more than the abstract idea itself. The alleged unconventional functions in the pending claims referred to by Applicant that are executed by the various generic computer components referred to by Applicant are not additional elements beyond the recited abstract idea, but rather, they are part of and directed to the recited abstract idea for the reasons set forth above under Prong 1 of Step 2A.
The alleged novelty or non-obviousness of the abstract elements does not transform the abstract idea into "significantly more" than an abstract idea. The search for an inventive concept under § 101 is distinct from demonstrating novel and non-obviousness. See SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2-3 (Fed Cir. May 15, 2018) (citing Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016). Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America at 2. Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at 3. In Step 2B, “[w]hat is needed is an inventive concept in the non-abstract application realm.” Id. at 11.
With respect to the remaining elements, under Step 2B, in claim 1, and similarly claims 2-18, the only additional elements beyond the recited abstract idea set forth above in Prong 1 of Step 2A are the recitations of “[a] method … a user interface displaying at least one visualization …, the method comprising,” “by an optimization engine,” “executing, by an analysis engine of the optimization engine, at least one machine learning model,“ “a user interface displaying,” and “by the optimization engine, via the modified user interface,” and individually and when viewed as an ordered combination, pursuant to the broadest reasonable interpretation, each of the additional elements are generic computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components, which is not sufficient to amount to significantly more than the abstract idea itself. See MPEP 2106.05(f). Further, these elements merely generally link the abstract idea to a field of use.
Response to Arguments - Prior Art
Applicant’s arguments with respect to the prior art rejections have been fully considered, but they are now moot in view of new grounds for rejection necessitated by Applicant’s amendments.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1, and similarly claims 2-18, recites “performance optimization recommendations and modifying … performance-related data … comprising: accessing … data identifying a goal associated with a project assigned to a team associated with at least one organization; accessing … data identifying a goal associated with the at least one organization; analyzing … data relating to at least one characteristic of each of a plurality of members of the team; determining … a level of contribution of each of the plurality of members of the team to the goal associated with the at least one organization; determining … a level of contribution of each of the plurality of members of the team to the goal associated with the project; determining … a likelihood of the team accomplishing the goal associated with the project, wherein determining further comprises … to provide a probability of success in accomplishing the goal; modifying, … a visualization of the determinations …; and providing, …, an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training module associated with the area of improvement.” Claims 1-18, in view of the claim limitations, recite the abstract idea of evaluating performance of team members contributing effort to goals and projects of an organization by accessing data regarding goals of a team project and goals of the organization, analyzing data regarding characteristics of members of the team, determining a level of contribution of the members to the goals of the team project and the goals of the organization, determining a likelihood and probability of completion of completing the goal of the team project, modifying a visualization of the determinations, and providing an indication of an area of improvement to increase accomplishing the goal by the team and a training module for the area of improvement.
Each of the above limitations manage business interactions and provide instructions or rules to follow to manage the human behavior of team members of organizations to accomplish a goal being presented the information regarding the area of improvement and associated training for the team to accomplish the goal and the determined level of contributions of the team members and likelihood of completing goals based on the human behavior of the goals to be completed by the team members, characteristics of the team members, and the level of contribution to those goals by the team members; thus, the claims recite certain methods of organizing human activity. In addition, as a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited accessing data regarding goals of a team project and goals of the organization, analyzing data regarding characteristics of members of the team, determining a level of contribution of the members to the goals of the team project and the goals of the organization, determining a likelihood and probability of completion of completing the goal of the team project, modifying a visualization of the determinations, and providing an indication of an area of improvement to increase accomplishing the goal by the team and a training module for the area of improvement could all be reasonably interpreted as a human making observations of information regarding the goals and team members, a human performing an evaluation and using judgment based on the observed information to determine the level of contribution of team members toward completing goals and the likelihood of completing goals, a human presenting the determined information, area of improvement, and training module manually and/or with a pen and paper; therefore, the claims recite mental processes. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2-15, 17, & 18 recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper and recite a certain method of organizing human activity that manages business interactions and personal human behavior. Accordingly, since the claims recite a certain method of organizing human activity and mental processes, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “[a] method … a user interface displaying at least one visualization …, the method comprising,” “by an optimization engine,” “executing, by an analysis engine of the optimization engine, at least one machine learning model,“ “a user interface displaying,” and “by the optimization engine, via the modified user interface” in claim 1, “by the optimization engine, via the user interface” and “by the optimization engine, a user interface displaying” in claim 14, “by the optimization engine, a user interface to display” in claim 15, “[a] method … a user interface displaying at least one visualization …, the method comprising,” “by an optimization engine,” “executing, by an analysis engine of the optimization engine, at least one machine learning model,” “a user interface displaying,” and “by the optimization engine, via the modified user interface” in claim 16; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-15, 17, & 18 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0085]-[0087] (describing the techniques described above may be implemented in one or more computer programs executing on a programmable computer including a processor, a storage medium readable by the processor (including, for example, volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device, suitable processors include, by way of example, both general and special purpose microprocessors, and these elements will also be found in a conventional desktop or workstation computer as well as other computers suitable for executing computer programs implementing the methods described herein). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-15, 17, & 18 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-18 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1-5, 8-13, & 16-18 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Lavoie, et al. (US 20150006214 A1), hereinafter Lavoie, in view of Jain, et al. (US 20210241137 A1), hereinafter Jain.
Regarding claim 1, Lavoie discloses a method for generating performance optimization recommendations and modifying a user interface displaying at least one visualization of performance-related data, the method comprising (Abstract, [0004]-[0005]):
accessing, by an optimization engine, data identifying a goal associated with a project assigned to a team associated with at least one organization ([0175], fig. 5, method 500 includes associating, by a computing device, at least one employee with each of a plurality of goals (502) as disclosed in FIG. 3A, [0094], method 300 includes associating, by the first computing device, an employee task with the goal (304), including receiving an instruction to associate the employee task with the goal from a user, and adding an identification of the association to the data structure 232b for the goal and the data structure 232c for the task, and users may hold a meeting, assign tasks in the meeting, and then later, after the meeting has ended, view the meeting object data to identify what goal(s) the meeting was aligned with, what employee tasks were assigned);
accessing, by the optimization engine, data identifying a goal associated with the at least one organization ([0175], fig. 5, method 500 includes associating, by a computing device, at least one employee with each of a plurality of goals (502) as disclosed in FIG. 3A, [0093], fig. 3A, method 300 includes associating, by a first computing device, a goal with a corporate objective (302), including the object management module 204 receiving an identification of the objective, receiving an instruction to associate the goal with the objective, and adding an identification of the association to the data structure for 232b for the goal and the data structure 232a for the objective);
analyzing, by the optimization engine, data relating to at least one characteristic of each of a plurality of members of the team ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) as disclosed in FIG. 3A, [0094]-[0095], method 300 includes associating, by the first computing device, an employee task with the goal (304), including users may hold a meeting, assign tasks in the meeting, and then later, after the meeting has ended, view the meeting object data to identify what goal(s) the meeting was aligned with, what employee tasks were assigned, and method 300 includes receiving, by the first computing device, from a second computing device, an identification of a change to a status of the employee task (306));
determining, by the optimization engine, a level of contribution of each of the plurality of members of the team to the goal associated with the at least one organization ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal);
determining, by the optimization engine, a level of contribution of each of the plurality of members of the team to the goal associated with the project ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee);
determining, by the optimization engine, a likelihood of the team accomplishing the goal associated with the project, wherein determining further comprises executing, by an analysis engine of the optimization engine, … to provide a probability of success in accomplishing the goal ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data);
modifying, by the optimization engine, a user interface displaying a visualization of the determinations of the optimization engine ([0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data and the data visualization module 210 generates updated data visualizations, [0174], [0178]-[0179], method 500 includes determining a corporate allocation of effort for each goal of the plurality of goals, using the determined allocation of effort for the at least one employee (506), and providing a visualization of the determined corporate allocation of effort (508), the method 500 also includes attributing, to the at least one employee, a level of performance on one of the plurality of goals, evaluating the level of performance, and providing a data visualization modeling an organizational structure for improving the determined corporate allocation of effort towards one of the plurality of goals based upon the level of performance of the at least one employee, and the analysis module 208 may provide a data visualization allowing the user to view at least one of the plurality of goals and the evaluated performance of each employee associated with the at least one of the plurality of goals); and
providing, by the optimization engine, via the modified user interface, an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training [recommendation] associated with the area of improvement ([0173], the methods and systems described herein provide functionality for identifying opportunities for training and continuing education, the analysis module 208 may identify tasks in which employees fell behind or failed to complete due to insufficient training to master particular skills, and the analysis module 208 may recommend providing additional training to an employee lacking a particular skill, the analysis module 208 provides recommendations for training or continuing education opportunities based on qualitative or quantitative analyses, and in conjunction with the data visualization module 210, provides a visualization of what employees would most benefit from training and how that training will further various tasks or goals).
While Lavoie discloses all of the above, including determining, by the optimization engine, a likelihood of the team accomplishing the goal associated with the project, wherein determining further comprises executing, by an analysis engine of the optimization engine, … to provide a probability of success in accomplishing the goal; … and
providing, by the optimization engine, via the modified user interface, an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training [recommendation] associated with the area of improvement (as above), Lavoie does not expressly disclose the following remaining elements of the limitation, which however, are taught by further teachings in Jain.
Jain teaches determining, by the optimization engine, a likelihood of the team accomplishing the goal associated with the project, wherein determining further comprises executing, by an analysis engine of the optimization engine, at least one machine learning model to provide a probability of success in accomplishing the goal ([0410], [0413], [0419], process 1500 generates a group readiness measure (1504) indicating a predicted ability of the group to satisfy one or more group readiness criteria using the one or more models, e.g., machine learning models using one or more machine learning models such as, for example, the machine learning models 130, 132, and/or 134, [0423], output data provided can indicate a prediction, such as a prediction (e.g., classification or likelihood) whether the group will achieve readiness, [0205]-[0207], [0209], process 200b selects at least one model, such as the machine learning model 130, 132, and/or 134 and then scores performance readiness to a subject or a group of subjects (238) using selected at least one models includes predicting a future readiness probability (242) including a likelihood that a subject, a group of subjects, or team of subjects in a group of subjects will achieve readiness, [0008], readiness may represent the person obtaining a level of proficiency at a skill, readiness to take on a particular job role, knowledge level, skills acquired); … and
providing, by the optimization engine, via the modified user interface, an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training module associated with the area of improvement ([0420], [0243]-[0424], process 1500 includes providing output data based on the group readiness measure (1508) by altering a user interface of a device or alter interaction of a device with one or more of the subjects in the group, wherein the output can include a prediction (e.g., classification or likelihood) whether the group will achieve readiness criteria, a score indicative of the prediction for display on a user interface; an indicator of the prediction for display on a user interface, and a recommendation, determined based on the prediction, of an action to improve or accelerate acquisition of readiness of the group and/or one or more subjects in the group to satisfy one or more individual readiness criteria, [0370], the candidate actions may include changes in the training program or training plan for a subject, such as exercises, classes, practice activities, classes, and other activities for the subject to participate in)
Lavoie and Jain are analogous fields of invention because both address the problem of managing organizations achieving of goals and completing tasks. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Lavoie the ability to execute a machine learning model to provide a probability of success in accomplishing the goal and provide an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training module associated with the area of improvement, as taught by Jain, 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 combination would produce the predictable results of executing a machine learning model to provide a probability of success in accomplishing the goal and providing an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training module associated with the area of improvement, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Lavoie with the aforementioned teachings of Jain in order to produce the added benefit of improving the process of achieving readiness including allowing the subject to reach readiness faster. [0005].
Regarding claim 2, the combined teachings of Lavoie and Jain teach the method of claim 1 (as above). Further, Lavoie discloses wherein the goal associated with the project relates to a metric of success ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0069]-[0070], a status update includes a quantitative representation of a status that may be a numerical value or other metric, such as dollar value and percentage of goal, the status module 206 modifies a status of a task, a goal, or an objective).
Regarding claim 3, the combined teachings of Lavoie and Jain teach the method of claim 1 (as above). Further, Lavoie discloses wherein the goal associated with the at least one organization relates to a human resources goal ([0160], the methods and systems described herein allow users to create and align the goals of a future employee before a person is hired for the role, so that users throughout the organization understand the impact of a potential candidate on the goals of a company before the candidate is approved or hired, wherein visualizations may provide an indication of the areas where existing employees are trying to hire new resources and would allow existing employees to make hiring decisions based on the impact of the goals potential candidates would perform in the job, [0164], the individuals requesting the new hire (e.g., executives or managers) provide an identification of the incomplete tasks that the new hire would be assigned to complete; in such an embodiment, managers have an opportunity to identify and approve hiring decisions based on the specific tasks and goals that will be accomplished as opposed to vague titles or job descriptions).
Regarding claim 4, the combined teachings of Lavoie and Jain teach the method of claim 1 (as above). Further, Lavoie discloses wherein the goal associated with the at least one organization relates to a corporate governance goal ([0135], the analysis module 208 executes a method for estimating the transparency of an organization to company leadership based on an equation where Transparency=Average span of Control Levels of Visibility (T=C L/S) in the organization/size of the organization in people, and using this equation, the analysis engine 208 may determine a level of transparency and visibility that a user assigned to a high-level objective (such as a company executive) has into lower-level tasks (such as employee tasks for carrying out goals that will implement the objectives), [0092], the computing device 106 receives an identification of a company mission statement from a user, which typically includes aspirational objectives describing why the company exists, the system provide users with functionality for tracking progress towards such aspirational objectives by associating the objectives with more specific goals and even more specific, actionable tasks that specify what actions one or more members of the company need to execute in order to meet the goal, e.g., a mission statement may include an objective indicating that a company exists to pioneer new software; one goal may be to launch the software and add $150,000 in new product sales; associated tasks may include launching a beta version of the software, scheduling a meeting to discuss the final revisions to the beta version of the software, and preparing marketing materials promoting the launch).
Regarding claim 5, the combined teachings of Lavoie and Jain teach the method of claim 1 (as above). Further, Lavoie discloses further comprising determining, by the optimization engine, a likelihood of retention of at least one of the plurality of members of the team by the at least one organization ([0172], the analysis module 208 includes determining a level of employee satisfaction, by analyzing user contributions--number and quality of status updates, comments on their own tasks and goals, comments on the tasks and goals of others, and other user-generated content--the analysis module 208 can conduct sentiment analysis to determine when an employee is dissatisfied or may be a potential risk for leaving the organization).
Regarding claim 8, the combined teachings of Lavoie and Jain teach the method of claim 1 (as above). Further, Lavoie discloses further comprising identifying, by the optimization engine, at least one characteristic of the team ([0173], the analysis module 208 may identify tasks in which employees fell behind schedule or failed to complete the tasks due to insufficient training or time required to master particular skills, and the analysis module 208 may determine that similar employees may also require additional training in those skills, and in some embodiments the analysis module 208 may recommend assigning a different employee to a task) correlated with a modification to the determined likelihood of the team accomplishing the goal associated with the project ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0094], method 300 includes associating, by the first computing device, an employee task with the goal (304) [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data).
Regarding claim 9, the combined teachings of Lavoie and Jain teach the method of claim 8 (as above). Further, Lavoie discloses further comprising: generating, by the optimization engine, a survey to provide to the members of the team having the at least one characteristic, the survey presenting at least one question associated with the performance of the team ([0096], Fig. 3B, diagram depicts embodiments of user interfaces with which users may instruct the computing device 106 to request periodic status updates, wherein a user may instruct the computing device 106 to request periodic status updates using an interface 320, the user may identify an object for which no status updates have been received for a threshold period of time and instruct the computing device 106 to request a one-time status update, in addition to or instead of any other scheduled request);
sending, by the optimization engine, the survey to the members of the team according to a predetermined schedule ([0096], after the user requests periodic status updates or updates when no update has been made for a predetermined time, the computing device 106 transmits, to the computing device 102, a request for the identification of the change to the status of the employee task, [0067], [0069]-[0070], FIG. 2J depicts the user interface 228a in which the user has not yet begun typing the status update and the user interface 228b in which the user has begun typing the status update ("Hello"), wherein a status update includes a quantitative representation of a status, which may be a numerical value or other metric, and may be rolled up to show the quantitative status across similar tasks, goals, objectives, groups, and organizations, and the status module 206 modifies a status of a task, a goal, or an objective, based upon receiving an identification of a change, [0091], method 300 includes receiving from a second computing device, an identification of a change to a status of the employee task (306) and modifying, by the first computing device, a status of the goal responsive to the received identification (308));
analyzing, by the optimization engine, at least one received response to the survey ([0070], the status module 206 modifies a status of a task, a goal, or an objective, based upon receiving an identification of a change, [0091], method 300 includes modifying, by the first computing device, a status of the objective responsive to the modification of the status of the goal (310)); and
determining, by the optimization engine, a second modification to the likelihood of the team accomplishing the goal associated with the project, responsive to the analyzing of the at least one received response to the survey ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0167]-[0168], the methods and systems described herein provide functionality for evaluating performance of employees based on the employee contributions to the tasks and goals they were hired to complete, in these embodiments, the analysis module 208 examines the goal alignment, status of the goals aligned and an individual's historical success at achieving those goals and how that links to the performance "grade" people have been allocated during their performance reviews, and the analysis module 208 attempts to draw conclusions from this process such as, "`A` players are two times more likely to achieve their goals on time", [0176], [0178], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, and the method includes attributing to the at least one employee, a level of performance on one of the plurality of goals, evaluating the level of performance, and providing a data visualization modeling an organizational structure for improving the determined corporate allocation of effort towards one of the plurality of goals based upon the level of performance of the at least one employee, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data).
Regarding claim 10, the combined teachings of Lavoie and Jain teach the method of claim 8 (as above). Further, Lavoie discloses further comprising identifying, by the optimization engine, an action to assign to the members of the team having the at least one characteristic ([0173], the analysis module 208 may identify tasks in which employees fell behind schedule or failed to complete the tasks due to insufficient training or time required to master particular skills, and the analysis module 208 may determine that similar employees may also require additional training in those skills, and in some embodiments the analysis module 208 may recommend assigning a different employee to a task) to improve the determined likelihood of the team accomplishing the goal associated with the project ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data).
Regarding claim 11, the combined teachings of Lavoie and Jain teach the method of claim 1 (as above). Further, Lavoie discloses further comprising identifying, by the optimization engine, at least one characteristic of the team ([0173], the analysis module 208 may identify tasks in which employees fell behind schedule or failed to complete the tasks due to insufficient training or time required to master particular skills, and the analysis module 208 may determine that similar employees may also require additional training in those skills, and in some embodiments the analysis module 208 may recommend assigning a different employee to a task) correlated with a modification to the determined likelihood of the team accomplishing the goal associated with the organization ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0093], fig. 3A, method 300 includes associating, by a first computing device, a goal with a corporate objective (302), [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data).
Regarding claim 12, the combined teachings of Lavoie and Jain teach the method of claim 11 (as above). Further, Lavoie discloses further comprising: generating, by the optimization engine, a survey to provide to the members of the team having the at least one characteristic, the survey presenting at least one question associated with the performance of the team ([0096], Fig. 3B, diagram depicts embodiments of user interfaces with which users may instruct the computing device 106 to request periodic status updates, wherein a user may instruct the computing device 106 to request periodic status updates using an interface 320, the user may identify an object for which no status updates have been received for a threshold period of time and instruct the computing device 106 to request a one-time status update, in addition to or instead of any other scheduled request);
sending, by the optimization engine, the survey to the members of the team according to a predetermined schedule ([0096], after the user requests periodic status updates or updates when no update has been made for a predetermined time, the computing device 106 transmits, to the computing device 102, a request for the identification of the change to the status of the employee task, [0067], [0069]-[0070], FIG. 2J depicts the user interface 228a in which the user has not yet begun typing the status update and the user interface 228b in which the user has begun typing the status update ("Hello"), wherein a status update includes a quantitative representation of a status, which may be a numerical value or other metric, and may be rolled up to show the quantitative status across similar tasks, goals, objectives, groups, and organizations, and the status module 206 modifies a status of a task, a goal, or an objective, based upon receiving an identification of a change, [0091], method 300 includes receiving from a second computing device, an identification of a change to a status of the employee task (306) and modifying, by the first computing device, a status of the goal responsive to the received identification (308));
analyzing, by the optimization engine, at least one received response to the survey ([0070], the status module 206 modifies a status of a task, a goal, or an objective, based upon receiving an identification of a change, [0091], method 300 includes modifying, by the first computing device, a status of the objective responsive to the modification of the status of the goal (310)); and
determining, by the optimization engine, a second modification to the likelihood of the team accomplishing the goal associated with the organization, responsive to the analyzing of the at least one received response to the survey ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0167]-[0168], the methods and systems described herein provide functionality for evaluating performance of employees based on the employee contributions to the tasks and goals they were hired to complete, in these embodiments, the analysis module 208 examines the goal alignment, status of the goals aligned and an individual's historical success at achieving those goals and how that links to the performance "grade" people have been allocated during their performance reviews, and the analysis module 208 attempts to draw conclusions from this process such as, "`A` players are two times more likely to achieve their goals on time", [0176], [0178], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, and the method includes attributing to the at least one employee, a level of performance on one of the plurality of goals, evaluating the level of performance, and providing a data visualization modeling an organizational structure for improving the determined corporate allocation of effort towards one of the plurality of goals based upon the level of performance of the at least one employee. [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data).
Regarding claim 13, the combined teachings of Lavoie and Jain teach the method of claim 11 (as above). Further, Lavoie discloses further comprising identifying, by the optimization engine, an action to assign to the members of the team having the at least one characteristic ([0173], the analysis module 208 may identify tasks in which employees fell behind schedule or failed to complete the tasks due to insufficient training or time required to master particular skills, and the analysis module 208 may determine that similar employees may also require additional training in those skills, and in some embodiments the analysis module 208 may recommend assigning a different employee to a task) to improve the determined likelihood of the team accomplishing the goal associated with the organization ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data).
Regarding claim 16, Lavoie discloses a method for generating performance optimization recommendations and modifying a user interface displaying at least one visualization of performance-related data, the method comprising (Abstract, [0004]-[0005]):
accessing, by an optimization engine, data identifying a goal associated with a project ([0175], fig. 5, method 500 includes associating, by a computing device, at least one employee with each of a plurality of goals (502) as disclosed in FIG. 3A, [0094], method 300 includes associating, by the first computing device, an employee task with the goal (304), including receiving an instruction to associate the employee task with the goal from a user, and adding an identification of the association to the data structure 232b for the goal and the data structure 232c for the task, and users may hold a meeting, assign tasks in the meeting, and then later, after the meeting has ended, view the meeting object data to identify what goal(s) the meeting was aligned with, what employee tasks were assigned);
accessing, by the optimization engine, data identifying a goal associated with at least one organization having at least one member available for assignment to the project ([0175], fig. 5, method 500 includes associating, by a computing device, at least one employee with each of a plurality of goals (502) as disclosed in FIG. 3A, [0093], fig. 3A, method 300 includes associating, by a first computing device, a goal with a corporate objective (302), including the object management module 204 receiving an identification of the objective, receiving an instruction to associate the goal with the objective, and adding an identification of the association to the data structure for 232b for the goal and the data structure 232a for the objective);
analyzing, by the optimization engine, data relating to at least one characteristic of the at least one member ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) as disclosed in FIG. 3A, [0094]-[0095], method 300 includes associating, by the first computing device, an employee task with the goal (304), including users may hold a meeting, assign tasks in the meeting, and then later, after the meeting has ended, view the meeting object data to identify what goal(s) the meeting was aligned with, what employee tasks were assigned, and method 300 includes receiving, by the first computing device, from a second computing device, an identification of a change to a status of the employee task (306));
identifying a proposed team including a plurality of members to assign to the project ([0179], embodiments of the method 500 also include, the analysis module 208 allocates one of the plurality of goals to the at least one employee based on the evaluation, as set forth above in reference to FIGS. 4A-4E, [0159]-[0165], with regard to Fig. 4A, the methods herein allow users to create and align the goals of a future employee before a person is hired for the role, so that users understand the impact of a potential candidate on the goals before the candidate is approved or hired, wherein method 400 includes receiving an indication that the employee task is incomplete (406), which includes in some embodiments, a user evaluating whether to create a new job opportunity first identifies what incomplete tasks, projects or goals the new hire would complete, associating the goal and the employee task with a description of a job opportunity (408), including the individuals requesting the new hire provide an identification of the incomplete tasks that the new hire would be assigned to complete, and providing an indication that the goal and the employee task are incomplete and the description of the job opportunity (410), including providing the visualization to a potential candidate, allowing the candidate to better understand what tasks and goals they will be contributing to if they are hired, [0173], the analysis module 208 may identify tasks in which employees fell behind schedule or failed to complete the tasks due to insufficient training or time required to master particular skills, and in some embodiments the analysis module 208 may recommend assigning a different employee to a task, [0094], method 300 includes associating, by the first computing device, an employee task with the goal (304), including users may hold a meeting, assign tasks in the meeting, and then later, after the meeting has ended, view the meeting object data to identify what goal(s) the meeting was aligned with, what employee tasks were assigned);
determining, by the optimization engine, a level of contribution of each of the plurality of members of the proposed team to the goal associated with the at least one organization ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal);
determining, by the optimization engine, a level of contribution of each of the plurality of members of the proposed team to the goal associated with the project ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee);
determining, by the optimization engine, a likelihood of the team accomplishing the goal associated with the project, wherein determining the likelihood of accomplishing the goal further comprises executing, by an analysis engine of the optimization engine, … to provide a probability of success in accomplishing the goal ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data); and
modifying, by the optimization engine, a user interface displaying a visualization of the determinations of the optimization engine ([0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data and the data visualization module 210 generates updated data visualizations, [0174], [0178]-[0179], method 500 includes determining a corporate allocation of effort for each goal of the plurality of goals, using the determined allocation of effort for the at least one employee (506), and providing a visualization of the determined corporate allocation of effort (508), the method 500 also includes attributing, to the at least one employee, a level of performance on one of the plurality of goals, evaluating the level of performance, and providing a data visualization modeling an organizational structure for improving the determined corporate allocation of effort towards one of the plurality of goals based upon the level of performance of the at least one employee, and the analysis module 208 may provide a data visualization allowing the user to view at least one of the plurality of goals and the evaluated performance of each employee associated with the at least one of the plurality of goals); and
providing, by the optimization engine, via the modified user interface, an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training [recommendation] associated with the area of improvement ([0173], the methods and systems described herein provide functionality for identifying opportunities for training and continuing education, the analysis module 208 may identify tasks in which employees fell behind or failed to complete due to insufficient training to master particular skills, and the analysis module 208 may recommend providing additional training to an employee lacking a particular skill, the analysis module 208 provides recommendations for training or continuing education opportunities based on qualitative or quantitative analyses, and in conjunction with the data visualization module 210, provides a visualization of what employees would most benefit from training and how that training will further various tasks or goals).
While Lavoie discloses all of the above, including determining, by the optimization engine, a likelihood of the team accomplishing the goal associated with the project, wherein determining the likelihood of accomplishing the goal further comprises executing, by an analysis engine of the optimization engine, … to provide a probability of success in accomplishing the goal; … and
providing, by the optimization engine, via the modified user interface, an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training [recommendation] associated with the area of improvement (as above), Lavoie does not expressly disclose the following remaining elements of the limitation, which however, are taught by further teachings in Jain.
Jain teaches determining, by the optimization engine, a likelihood of the team accomplishing the goal associated with the project, wherein determining the likelihood of accomplishing the goal further comprises executing, by an analysis engine of the optimization engine, at least one machine learning model to provide a probability of success in accomplishing the goal ([0410], [0413], [0419], process 1500 generates a group readiness measure (1504) indicating a predicted ability of the group to satisfy one or more group readiness criteria using the one or more models, e.g., machine learning models using one or more machine learning models such as, for example, the machine learning models 130, 132, and/or 134, [0423], output data provided can indicate a prediction, such as a prediction (e.g., classification or likelihood) whether the group will achieve readiness, [0205]-[0207], [0209], process 200b selects at least one model, such as the machine learning model 130, 132, and/or 134 and then scores performance readiness to a subject or a group of subjects (238) using selected at least one models includes predicting a future readiness probability (242) including a likelihood that a subject, a group of subjects, or team of subjects in a group of subjects will achieve readiness, [0008], readiness may represent the person obtaining a level of proficiency at a skill, readiness to take on a particular job role, knowledge level, skills acquired); … and
providing, by the optimization engine, via the modified user interface, an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training module associated with the area of improvement ([0420], [0243]-[0424], process 1500 includes providing output data based on the group readiness measure (1508) by altering a user interface of a device or alter interaction of a device with one or more of the subjects in the group, wherein the output can include a prediction (e.g., classification or likelihood) whether the group will achieve readiness criteria, a score indicative of the prediction for display on a user interface; an indicator of the prediction for display on a user interface, and a recommendation, determined based on the prediction, of an action to improve or accelerate acquisition of readiness of the group and/or one or more subjects in the group to satisfy one or more individual readiness criteria, [0370], the candidate actions may include changes in the training program or training plan for a subject, such as exercises, classes, practice activities, classes, and other activities for the subject to participate in)
Lavoie and Jain are analogous fields of invention because both address the problem of managing organizations achieving of goals and completing tasks. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Lavoie the ability to execute a machine learning model to provide a probability of success in accomplishing the goal and provide an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training module associated with the area of improvement, as taught by Jain, 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 combination would produce the predictable results of executing a machine learning model to provide a probability of success in accomplishing the goal and providing an identification of an area of improvement associated with an increased likelihood of accomplishing, by the team, the goal and at least one training module associated with the area of improvement, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Lavoie with the aforementioned teachings of Jain in order to produce the added benefit of improving the process of achieving readiness including allowing the subject to reach readiness faster. [0005].
Regarding claim 17, the combined teachings of Lavoie and Jain teach the method of claim 16 (as above). Further, Lavoie discloses further comprising: determining, by the optimization engine, that a second member of the organization is available for assignment to the project and provides a higher level of contribution to the goal associated with the project; and modifying the proposed team to include the second member of the organization ([0173], the analysis module 208 may identify tasks in which employees fell behind schedule or failed to complete the tasks due to insufficient training or time required to master particular skills, the analysis module 208 may review data associated with an employee to determine whether the employee has the requisite skills for a particular task, and in some embodiments the analysis module 208 may recommend assigning a different employee to a task).
Regarding claim 18, the combined teachings of Lavoie and Jain teach the method of claim 16 (as above). Further, Lavoie discloses further comprising: determining, by the optimization engine, that a second member of the organization is available for assignment to the project and provides a higher level of contribution to the goal associated with the organization; and modifying the proposed team to include the second member of the organization ([0173], the analysis module 208 may identify tasks in which employees fell behind schedule or failed to complete the tasks due to insufficient training or time required to master particular skills, the analysis module 208 may review data associated with an employee to determine whether the employee has the requisite skills for a particular task, and in some embodiments the analysis module 208 may recommend assigning a different employee to a task, [0176], at least one task is aligned with at least one of the plurality of goals).
Claims 6, 7, 14, & 15 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Lavoie, et al. (US 20150006214 A1), hereinafter Lavoie, in view of Jain, et al. (US 20210241137 A1), hereinafter Jain, and in further view of Portnoy, et al. (US 20190066011 A1), hereinafter Portnoy.
Regarding claim 6, the combined teachings of Lavoie and Jain teach the method of claim 5 (as above). Further, while Lavoie discloses all of the above and further comprising determining, by the optimization engine, … a likelihood of retention of the at least one of the plurality of members by the at least one organization ([0172], the analysis module 208 includes determining a level of employee satisfaction, by analyzing user contributions--number and quality of status updates, comments on their own tasks and goals, comments on the tasks and goals of others, and other user-generated content--the analysis module 208 can conduct sentiment analysis to determine when an employee is dissatisfied or may be a potential risk for leaving the organization) and the determined likelihood of the team accomplishing the goal associated with the project ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data), Lavoie does not expressly disclose the following remaining elements of the limitation, which however, are taught by further teachings in Portnoy.
Portnoy teaches determining, by the optimization engine, a correlation between a likelihood of retention of the at least one of the plurality of members by the at least one organization and … ([0167], [0171], workforce management system 100 may provide analyses for the organization as a whole, or for any suborganization 206, 208, wherein among the analyses that may be computed by workforce management system 100 are: Attrition rate, and reasons for leaving (retired, left for other reason)? How has attrition been distributed by age, tenure, and occupational role? Which areas have greatest attrition?).
Lavoie and Portnoy are analogous fields of invention because both address the problem of generating scores categorizing changes in business processes. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Lavoie the ability to determine a correlation between a likelihood of retention of the at least one of the plurality of members by the at least one organization, as taught by Portnoy, 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 combination would produce the predictable results of determining a correlation between a likelihood of retention of the at least one of the plurality of members by the at least one organization and the determined likelihood of the team accomplishing the goal associated with the project, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Lavoie with the aforementioned teachings of Portnoy in order to produce the added benefit of improving organization performance. [0033].
Regarding claim 7, the combined teachings of Lavoie and Jain teach the method of claim 5 (as above). Further, while Lavoie discloses all of the above and further comprising determining, by the optimization engine, … a likelihood of retention of the at least one of the plurality of members by the at least one organization and the determined likelihood of the team accomplishing the goal associated with the at least one organization ([0172], the analysis module 208 includes determining a level of employee satisfaction, by analyzing user contributions--number and quality of status updates, comments on their own tasks and goals, comments on the tasks and goals of others, and other user-generated content--the analysis module 208 can conduct sentiment analysis to determine when an employee is dissatisfied or may be a potential risk for leaving the organization) and the determined likelihood of the team accomplishing the goal associated with the project ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data), Lavoie does not expressly disclose the following remaining elements of the limitation, which however, are taught by further teachings in Portnoy.
Portnoy teaches determining, by the optimization engine, a correlation between a likelihood of retention of the at least one of the plurality of members by the at least one organization and … ([0167], [0171], workforce management system 100 may provide analyses for the organization as a whole, or for any suborganization 206, 208, wherein among the analyses that may be computed by workforce management system 100 are: Attrition rate, and reasons for leaving (retired, left for other reason)? How has attrition been distributed by age, tenure, and occupational role? Which areas have greatest attrition?).
Lavoie and Portnoy are analogous fields of invention because both address the problem of generating scores categorizing changes in business processes. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Lavoie the ability to determine a correlation between a likelihood of retention of the at least one of the plurality of members by the at least one organization, as taught by Portnoy, 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 combination would produce the predictable results of determining a correlation between a likelihood of retention of the at least one of the plurality of members by the at least one organization and the determined likelihood of the team accomplishing the goal associated with the at least one organization, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Lavoie with the aforementioned teachings of Portnoy in order to produce the added benefit of improving organization performance. [0033].
Regarding claim 14, the combined teachings of Lavoie and Jain teach the method of claim 1 (as above). Further, while Lavoie discloses all of the above and analyzing, by the optimization engine, data relating to at least one characteristic of the second member ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) as disclosed in FIG. 3A, [0094]-[0095], method 300 includes associating, by the first computing device, an employee task with the goal (304), including users may hold a meeting, assign tasks in the meeting, and then later, after the meeting has ended, view the meeting object data to identify what goal(s) the meeting was aligned with, what employee tasks were assigned, and method 300 includes receiving, by the first computing device, from a second computing device, an identification of a change to a status of the employee task (306));
determining, by the optimization engine, a level of contribution of the second member to the goal associated with the at least one organization ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal);
determining, by the optimization engine, a level of contribution of the second member to the goal associated with the project ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee);
determining, by the optimization engine, a likelihood of the modified team accomplishing the goal associated with the project ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data); and
modifying, by the optimization engine, a user interface displaying a visualization of the determinations of the optimization engine … ([0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data and the data visualization module 210 generates updated data visualizations, [0174], [0178]-[0179], method 500 includes determining a corporate allocation of effort for each goal of the plurality of goals, using the determined allocation of effort for the at least one employee (506), and providing a visualization of the determined corporate allocation of effort (508), the method 500 also includes attributing, to the at least one employee, a level of performance on one of the plurality of goals, evaluating the level of performance, and providing a data visualization modeling an organizational structure for improving the determined corporate allocation of effort towards one of the plurality of goals based upon the level of performance of the at least one employee, and the analysis module 208 may provide a data visualization allowing the user to view at least one of the plurality of goals and the evaluated performance of each employee associated with the at least one of the plurality of goals) and generally teaches modify an identification of the members of the team to replace one member of the team with a second member of the at least one organization ([0173], the analysis module 208 may identify tasks in which employees fell behind schedule or failed to complete the tasks due to insufficient training or time required to master particular skills, and in some embodiments the analysis module 208 may recommend assigning a different employee to a task), Lavoie does not expressly disclose the following remaining elements of the limitation, which however, are taught by further teachings in Portnoy.
Portnoy teaches receiving, by the optimization engine, via the user interface, user input instructing the optimization engine to modify an identification of the members of the team to replace one member of the team with a second member of the at least one organization;
modifying, by the optimization engine, the identification responsive to the user input ([0057], workforce management system 100 may provide a user interface for editing a newly-input or existing organizational model 202, to change names, linkages among suborganizations 206, 208, reallocate employees among the suborganization blocks, [0325], there is a complete list 785 of division employees along with an “On/Off” toggle button for each employee to indicate their inclusion on the list under the current proposal, and this list provides a set of On/Off buttons and adjustable FTE values 785 to allow the user/manager to adjust and reallocate employee time allocations); …;
determining, by the optimization engine, a likelihood of the modified team accomplishing the goal associated with the project member ([0325], there is a complete list 785 of division employees along with an “On/Off” toggle button for each employee to indicate their inclusion on the list under the current proposal to allow the user/manager to adjust and reallocate employee time allocations, and after these adjustments, the user/manager may click “Show Impact Summary” to recalculate effects on the organization, for example, using the modeling equations (1)-(14), [0273], [0281], predictive modeling may find a combination of employee On/Off states which would best satisfy various boundary constraints: (8) risk of mission failure (for example, calculated in section VI) must stay below some threshold value); and
modifying, by the optimization engine, a user interface displaying a visualization of the determinations of the optimization engine when the team includes the second member instead of the first member ([0325], there is a complete list 785 of division employees along with an “On/Off” toggle button for each employee to indicate their inclusion on the list under the current proposal to allow the user/manager to adjust and reallocate employee time allocations, and after these adjustments, the user/manager may click “Show Impact Summary” to recalculate effects on the organization, for example, using the modeling equations (1)-(14), to present the function/activity's cost compared with a newly assigned cost).
Lavoie and Portnoy are analogous fields of invention because both address the problem of generating scores categorizing changes in business processes. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Lavoie the ability to receive user input instructing the optimization engine to modify an identification of the members of the team to replace one member of the team with a second member of the at least one organization, as taught by Portnoy, 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 combination would produce the predictable results of receiving user input instructing the optimization engine to modify an identification of the members of the team to replace one member of the team with a second member of the at least one organization, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Lavoie with the aforementioned teachings of Portnoy in order to produce the added benefit of improving organization performance. [0033].
Regarding claim 15, the combined teachings of Lavoie and Jain teach the method of claim 1 (as above). Further, while Lavoie discloses all of the above and further comprising: analyzing, by the optimization engine, data relating to at least one characteristic of a second member of the at least one organization ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) as disclosed in FIG. 3A, [0094]-[0095], method 300 includes associating, by the first computing device, an employee task with the goal (304), including users may hold a meeting, assign tasks in the meeting, and then later, after the meeting has ended, view the meeting object data to identify what goal(s) the meeting was aligned with, what employee tasks were assigned, and method 300 includes receiving, by the first computing device, from a second computing device, an identification of a change to a status of the employee task (306)); …;
determining, by the optimization engine, a level of contribution of the second member to the goal associated with the at least one organization ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal);
determining, by the optimization engine, a level of contribution of the second member to the goal associated with the project ([0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee);
determining, by the optimization engine, a likelihood of the modified team accomplishing the goal associated with the project ([0111], the analysis module 208 analyzes a level of likelihood that individuals associated with the goal will achieve the goal and any sub-goals or tasks by analyzing historical performance of each individual, [0176], method 500 includes determining an allocation of personal effort of the at least one employee toward each of the plurality of goals (504) by determining a percentage of effort the at least one employee has dedicated to each goal, by determining a percentage of effort the at least one employee has dedicated to at least one task aligned with at least one of the plurality of goals and associated with the at least one employee, and by analyzing a prioritization level of each of the plurality of goals and an estimated completion date for each of the plurality of goals, analyzing a level of alignment of each of the plurality of goals to at least one of an overarching goal and an overarching objective, and allocating personal effort between those goals based proportionally on their priority, [0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data); and
modifying, by the optimization engine, a user interface to display a visualization of the determinations of the optimization engine … ([0070], the status module 206 modifies a status of a task, a goal, or an objective, while the analysis module 208 identifies and updates associated data and the data visualization module 210 generates updated data visualizations, [0174], [0178]-[0179], method 500 includes determining a corporate allocation of effort for each goal of the plurality of goals, using the determined allocation of effort for the at least one employee (506), and providing a visualization of the determined corporate allocation of effort (508), the method 500 also includes attributing, to the at least one employee, a level of performance on one of the plurality of goals, evaluating the level of performance, and providing a data visualization modeling an organizational structure for improving the determined corporate allocation of effort towards one of the plurality of goals based upon the level of performance of the at least one employee, and the analysis module 208 may provide a data visualization allowing the user to view at least one of the plurality of goals and the evaluated performance of each employee associated with the at least one of the plurality of goals) and generally teaches modifying, by the optimization engine, an identification of the members of the team to replace one member of the team with the second member of the at least one organization ([0173], the analysis module 208 may identify tasks in which employees fell behind schedule or failed to complete the tasks due to insufficient training or time required to master particular skills, and in some embodiments the analysis module 208 may recommend assigning a different employee to a task), Lavoie does not expressly disclose the following remaining elements of the limitation, which however, are taught by further teachings in Portnoy.
Portnoy teaches modifying, by the optimization engine, an identification of the members of the team to replace one member of the team with the second member of the at least one organization ([0057], workforce management system 100 may provide a user interface for editing a newly-input or existing organizational model 202, to change names, linkages among suborganizations 206, 208, reallocate employees among the suborganization blocks, [0325], there is a complete list 785 of division employees along with an “On/Off” toggle button for each employee to indicate their inclusion on the list under the current proposal, and this list provides a set of On/Off buttons and adjustable FTE values 785 to allow the user/manager to adjust and reallocate employee time allocations); …;
determining, by the optimization engine, a likelihood of the modified team accomplishing the goal associated with the project ([0325], there is a complete list 785 of division employees along with an “On/Off” toggle button for each employee to indicate their inclusion on the list under the current proposal to allow the user/manager to adjust and reallocate employee time allocations, and after these adjustments, the user/manager may click “Show Impact Summary” to recalculate effects on the organization, for example, using the modeling equations (1)-(14), [0273], [0281], predictive modeling may find a combination of employee On/Off states which would best satisfy various boundary constraints: (8) risk of mission failure (for example, calculated in section VI) must stay below some threshold value); and
modifying, by the optimization engine, a user interface to display a visualization of the determinations of the optimization engine when the team includes the second member instead of the first member and to include a recommendation to modify the team to include the second member ([0325], there is a complete list 785 of division employees along with an “On/Off” toggle button for each employee to indicate their inclusion on the list under the current proposal to allow the user/manager to adjust and reallocate employee time allocations, and after these adjustments, the user/manager may click “Show Impact Summary” to recalculate effects on the organization, for example, using the modeling equations (1)-(14), to present the function/activity's cost compared with a newly assigned cost).
Lavoie and Portnoy are analogous fields of invention because both address the problem of generating scores categorizing changes in business processes. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Lavoie the ability to modify an identification of the members of the team to replace one member of the team with the second member of the at least one organization and modify a user interface to display a visualization of the determinations of the optimization engine when the team includes the second member instead of the first member and to include a recommendation to modify the team to include the second member, as taught by Portnoy, 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 combination would produce the predictable results of modifying an identification of the members of the team to replace one member of the team with the second member of the at least one organization and modifying a user interface to display a visualization of the determinations of the optimization engine when the team includes the second member instead of the first member and to include a recommendation to modify the team to include the second member, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Lavoie with the aforementioned teachings of Portnoy in order to produce the added benefit of improving organization performance. [0033].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES A GUILIANO whose telephone number is (571)272-9859. The examiner can normally be reached Mon-Fri 10:00 am - 6:00 pm.
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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623