DETAILED ACTION
Notice to Applicant
1. The following is a FINAL Office action upon examination of application number 18/422,645 filed on 01/25/2024. Claims 1-7 and 11-17 are pending in this application and have been examined on the merits discussed below.
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
3. Application 18/422,645 filed 01/25/2024 is a Continuation of Application 17/335,830, filed 06/01/2021, now U.S. Patent # 11,928,636.
Response to Amendment
4. In the response filed July 25, 2025, Applicant amended claims 1, 3, 11, and 13, and canceled claims 8-10 and 18-20. No new claims were presented for examination.
5. Applicant's cancelled claims 8 and 18. Accordingly, the previously issued claim rejection under 35 U.S.C. 112(a) has been withdrawn.
6. Applicant's amendments to claim 1 are hereby acknowledged. Accordingly, the previously claim interpretation under 35 U.S.C. 112(f) has been removed.
7. Applicant's amendments to claims 1, 3, 11, and 13, are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C.
101; accordingly, this rejection has been maintained.
Response to Arguments
8. Applicant's arguments filed July 25, 2025, have been fully considered.
9. Applicant submits “It is submitted that the claims are patent eligible under at least Prong Two of Step 2A because the claims integrate the judicial exception identified by the Office Action into a practical application.” [Applicant’s Remarks, 07/25/2025, page 9]
The Examiner respectfully disagrees. Under Step 2A, Prong Two of the eligibility inquiry, Applicant argues that “the claims are patent eligible under at least Prong Two of Step 2A because the claims integrate the judicial exception identified by the Office Action into a practical application.” The additional elements in exemplary claim 1 are: generating a user interface on a computing device using a computing application, a skill activity detector of a server that is remote from the computing device, automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation, automatically generating a link to the digitally stored training media content item, generating a graphical element using another user interface, the graphical element including the link and being selectable, and a media playback device , which merely serve to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has provided no facts/evidence, cited any portion of the Specification, nor provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
The Examiner further notes that the knowledge risk determination is directed towards the abstract idea, where the algorithm used to make that determination is merely being used on a general-purpose computer (as disclosed in paragraphs 0051 of Applicant’s Specification) to obtain the result, where the same result would be found if the same algorithm was applied via pen and paper. The computer is merely being used as a tool to implement the abstract idea which does not integrate the abstract idea into a practical application or amount to significantly more (See MPEP 2106.05).
Lastly, the additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, this argument is found unpersuasive.
In response to Applicant’s argument that “the present patent application is directed to improvements in identifying knowledge gaps in workforce skills and, particularly, computer-based workforce skills, assigning risk levels to the knowledge gaps, and implementing measures, such as upskilling, to mitigate those risks. The risk level of a detected knowledge gap can be based on the type of workforce skill activity the complexity of the workforce skill in question. Once one or more of these factors are determined, relevant risk mitigation is automatically implemented, for example by automatically updating a calendar to include an invitation to an upskilling session or meeting that is topically relevant to the identified workforce skill, and/or by automatically generating a link for playing back media content that is topically relevant to the workforce skill.” [Remarks at page 9], the Examiner notes that the claimed limitations relate to organization and presentation of human resource information and the automation of training-related administrative tasks. While the system may provide business-related benefits, the claim does not recite any improvement to the functioning of the computer itself or to any other technology. There is no indication that the clamed invention provide a technological solution to a technical problem, nor does it improve the performance or capabilities of the underlying computing system. Accordingly, this argument is found unpersuasive.
10. Applicant submits “As recited in the claim, a specialized computing tool — a skill activity detector — performs technical operations remotely that could not be done with the human mind with respect to a user interface and a computing application to determine a type of skill activity. For example, a human could not perform the function of the skill activity detector remotely from the computing device that is operating the computing application. Thus, the technical solution recited in the claim is necessarily rooted in computer technology. There are also technological advantages to performing this type of detection remotely, such that, for example, the employee whose skill type is being detected can be unaware of the detection, which can improve the reliability of the detection. The skill activity type, the risk category, and the risk level, are then used to perform a number of automated upskilling operations, including updating a calendar to include an invitation to a training session, identifying a digitally stored training media content time, and generating a link to the media content item, which, upon selection, plays the automatically selected media content item on media playback device. These recitations are technical improvements in workforce knowledge maintenance, both from the perspective of identifying workforce knowledge risk in a way that could not be done via traditional methods and technology in this field, and from the perspective of automating specific types of appropriate upskilling measures, which could not be done via traditional methods and technology in this field. Thus, these limitations embrace the practical applications described above. Though of distinct scope, claim 11 is amended similarly to claim 1. For at least these reasons, claims 1 and 11, and claims depending therefrom, recite eligible subject matter. Reconsideration is requested.” [Applicant’s Remarks, 07/25/2025, page 11]
The Examiner respectfully disagrees. While the Applicant asserts that the claimed invention is “rooted in computer technology” and that certain operations “could not be done with the human mind,” the claims as drafted do not recite a specific improvement to computer functionality or technology. Instead, the claims recite a series of abstract operation related to detecting a type of workforce skill activity based on interaction with a computing application, assigning a risk category and risk level, selecting upskilling measures. These operations represent the automation of organization processes associated with workforce management and training. Although performed by a computer, they do not recite any technological innovation in how those systems operate, nor do they improve the functioning of the computer itself. The use of “a skill activity detector,” even when executed remotely, is described functionally and generically, without reciting any specific technical implementation that would constitute a technological improvement. Furthermore, the alleged benefit of remote operation (i.e. “the employee whose skill type is being detected can be unaware of the detection”) is a business-related outcome, not a technical solution. As such, the claims remain directed to an abstract idea, and the additional elements, individually and in combination, do not amount to significantly more than the abstract idea. Accordingly, this argument is found unpersuasive.
11. Applicant submits “none of the cited art teaches or suggests at least the following recitations in amended claim 1: automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation; automatically identifying, based on the upskilling recommendation, a digitally stored training media content item; and automatically generating a link to the digitally stored training media content item; generating a graphical element using another user interface, the graphical element including the link and being selectable to play the digitally stored training media content item; receiving a selection of the graphical element; and in response to receiving the selection, playing, with a media playback device, the digitally stored training media content item. Though of distinct scope, claim 11 is amended similarly to claim 1. For at least these reasons, claims 1 and 11, and claims depending therefrom, are allowable over the art of record. Reconsideration is requested.” [Applicant’s Remarks, 07/25/2025, page 12]
In response to Applicant's argument that “none of the cited art teaches or suggests at least the following recitations in amended claim 1: automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation; automatically identifying, based on the upskilling recommendation, a digitally stored training media content item; and automatically generating a link to the digitally stored training media content item; generating a graphical element using another user interface, the graphical element including the link and being selectable to play the digitally stored training media content item; receiving a selection of the graphical element; and in response to receiving the selection, playing, with a media playback device, the digitally stored training media content item,” it is noted that the argument is primarily raised in support of the amendments to independent claim 1, and therefore is believed to be fully addressed via the new ground of rejection under §103 set forth below, which incorporates new references, to teach the new limitations of claim 1. Accordingly, the amendment and supporting arguments are believed to be fully addressed via the new ground of rejection set forth under §103 below.
12. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Rejections - 35 USC § 101
13. 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.
14. Claims 1-7 and 11-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
15. Claims 1-7 and 11-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-7) and system (claims 11-17) is directed to at least one potentially eligible category of subject matter (i.e., process and machine, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-7 and 11-17 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for monitoring knowledge distribution across a workforce of a business enterprise, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion), and therefore fall under the “Mental Processes” abstract idea grouping. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: generating a user interface on a computing device using a computing application; determining, using a skill activity detector of a server that is remote from the computing device, a workforce skill activity type of a plurality of workforce skill activity types associated with the computing application to provide a determined workforce skill activity type, wherein providing the determined workforce skill activity type includes detecting, with the skill activity detector, that a feature of a plurality of features of the user interface generated by the computing application is being used via the computing device, the feature being associated with the determined workforce skill activity type, and another feature of the plurality of features being associated with another workforce skill activity type of the plurality of workforce skill activity types; categorizing, based on the determined workforce skill activity type, a knowledge risk in a risk category selected from a set of predefined knowledge risk categories; assigning a risk level to the knowledge risk based on the risk category; automatically selecting and implementing, based on the risk category, the risk level, and the determined workforce skill activity type, an upskilling recommendation for reducing the knowledge risk, including: automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation; automatically identifying, based on the upskilling recommendation, a digitally stored training media content item; and automatically generating a link to the digitally stored training media content item; generating a graphical element using another user interface, the graphical element including the link and being selectable to play the digitally stored training media content item; receiving a selection of the graphical element; and in response to receiving the selection, playing, with a media playback device, the digitally stored training media content item.
Considered together, these steps set forth an abstract idea of monitoring knowledge distribution across a workforce of a business enterprise [See Specification at paragraph 0004 describing “Embodiments of the present disclosure are directed to automate monitoring of knowledge distribution across a workforce or portion of a workforce of a business enterprise.”], which falls under the under the “Certain methods of organizing human activity” abstract idea grouping set forth in MPEP 2106. Independent claim 11 recites similar limitations as the above-noted limitations recited in claim 1 and is therefore found to recite the same abstract idea.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” and “Mental Processes” abstract idea groupings described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to independent claims 1/11, the additional elements are: generating a user interface on a computing device using a computing application, a skill activity detector of a server that is remote from the computing device, automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation, automatically generating a link to the digitally stored training media content item, generating a graphical element using another user interface, the graphical element including the link and being selectable, and a media playback device (claim 1), one or more processors, non-transitory computer-readable storage storing instructions, the system, generate a user interface on a computing device using a computing application, a skill activity detector of a server that is remote from the computing device, automatically update a calendar to include an invitation to a training session associated with the upskilling recommendation; automatically generate a link to the digitally stored training media content item, generate a graphical element using another user interface, the graphical element including the link and being selectable, and a media playback device (claim 11). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to independent claims 1/11, the additional elements are: generating a user interface on a computing device using a computing application, a skill activity detector of a server that is remote from the computing device, automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation, automatically generating a link to the digitally stored training media content item, generating a graphical element using another user interface, the graphical element including the link and being selectable, and a media playback device (claim 1), one or more processors, non-transitory computer-readable storage storing instructions, the system, generate a user interface on a computing device using a computing application, a skill activity detector of a server that is remote from the computing device, automatically update a calendar to include an invitation to a training session associated with the upskilling recommendation; automatically generate a link to the digitally stored training media content item, generate a graphical element using another user interface, the graphical element including the link and being selectable, and a media playback device (claim 11). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification describes generic computing devices that may be used to implement the invention, which cover virtually any computing device under the sun (See, e.g., Spec. at paragraph 0051: “The user device 14 is a computing device, such as a laptop computer, a desktop computer, a tablet computer, a smartphone, etc.”). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-7 and 12-17 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-7 recite “receiving a workforce knowledge risk query; and including a graphical representation of the workforce skill activity type and the knowledge risk, “includes first graphical elements indicating: a set of skills of a workforce; and another knowledge risk for each skill in the set of skills,” “includes second graphical elements indicating, for each skill in the set of skills, a knowledge level of each member of a team of members,” “includes third graphical elements indicating, for each skill in the set of skills, whether at least minimum predefined skill knowledge data for the team of members is known,” “includes fourth graphical elements indicating, for each skill in the set of skills, a knowledge risk level based on a combination of: a modification frequency of the skill; and a percentage of members of the team of members that have at least a minimum skill level,” “includes fourth graphical elements indicating, for each skill in the set of skills, a knowledge risk level based on a concentration of knowledge within a subset of members of the team of members,” however these limitations are part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” and “Mental Processes” abstract idea groupings. The other dependent claims have been evaluated as well, however, similar to claims 2-7 these claims also recite steps/details that are part of the abstract idea itself when analyzed under Step 2A Prong One of the eligibility inquiry and thus fall within the scope of the same “Certain methods of organizing human activity” and “Mental Processes.” Dependent claims recite additional elements of generating a graphical user interface and generating another graphical user interface in claims 2 and 12. However, when evaluated under Step 2A Prong Two and Step 2B, these additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
16. 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.
17. 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 of this title, 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.
18. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
19. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
20. Claims 1-6, 11-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hancock et al., Pub. No.: US 2019/0066029 A1, [hereinafter Hancock], in view of Gupta et al., Pub. No.: US 2019/0102741 A1, [hereinafter Gupta], in view of Regan, Pub. No.: US 2016/0180248 A1, [hereinafter Regan], in further view of Carpenter et al., Pub. No.: US 2021/0367970 A1, [hereinafter Carpenter].
As per claim 1, Hancock teaches a computer-implemented method (paragraph 0031: “a method and system are contemplated herein which seek to increase project efficiency as well as optimize skill usage within a company by matching particular projects having a plurality of required competencies associated therewith with particular workers or employees having demonstrated proficiencies relating to the required competency requirements so as to ensure proper assignment to the best available worker or employee.”), comprising:
generating a user interface on a computing device using a computing application (paragraph 0035, discussing that the true extent of a worker's skillsets or proficiencies with certain features of a tool can be readily ascertainable. For example, in a self-reported scenario two workers can claim to know how to use and use well Microsoft's Excel software. Whereas in practice worker 1 can successfully utilize the various regression tools, while worker 2 is proficient with creating and generating pivot tables. This example illustrates that for a given tool, and especially one that has hundreds if not thousands of functions, workers can be proficient at certain tasks utilizing particular features of the tool, while not being able to do other tasks; paragraph 0066, discussing that in some instances, a software program, such as an add-on, or a background module provided in the one or more programs, or on the worker's particular computer, can be utilized to track worker behavior and the worker skill database can then be automatically modified based on worker behavior. For example, a design program can be provided with an add-on which tracks functions, features, commands, keystrokes, hotkeys used, or any other potential parameter within a program. For purposes of illustration, if a particular worker regularly uses a strength analysis function in a design program, the use of that function can be tracked, and through repeated use it can then be assumed that the particular worker is proficient in using that particular command or function. As such, the use of that command or function can then be recognized by the program, add-on, and reported for inclusion in the worker skill database and associated with the particular worker's skillset; paragraphs 0038, 0052);
determining, using a skill activity detector of a server that is remote from the computing device, a workforce skill activity type of a plurality of workforce skill activity types associated with the computing application to provide a determined workforce skill activity type, wherein providing the determined workforce skill activity type includes detecting, with the skill activity detector, that a feature of a plurality of features of the user interface generated by the computing application is being used via the computing device, the feature being associated with the determined workforce skill activity type, and another feature of the plurality of features being associated with another workforce skill activity type of the plurality of workforce skill activity types (paragraph 0035, discussing that the true extent of a worker's skillsets or proficiencies with certain features of a tool can be readily ascertainable. For example, in a self-reported scenario two workers can claim to know how to use and use well Microsoft's Excel software. Whereas in practice worker 1 can successfully utilize the various regression tools, while worker 2 is proficient with creating and generating pivot tables. This example illustrates that for a given tool, and especially one that has hundreds if not thousands of functions, workers can be proficient at certain tasks utilizing particular features of the tool, while not being able to do other tasks; paragraph 0038, discussing that it has been recognized that if the system can be provided with necessary tool information, or otherwise determine the tool requirements for project completion, and then track worker proficiency with the features within the various available tools, thus allowing the system to use the tracked proficiency data of a given user or worker with regard to those tool's features in order to make a recommendation or provide a probabilistic match score between a given project and workers or users within the organization, candidates, etc., so as to ensure a given project receives the support necessary for a timely and quality completion; paragraph 0039, discussing that the method and system are configured to track various competencies of various workers within a given organization, associate those metrics to particular skillsets or competencies; paragraph 0049, discussing that each assessment can be associated with a particular tool and features thereof wherein the system can then update each particular worker's or candidate's profile and particular worker or candidate skill competency or proficiency information based on the worker's or candidate's tracked assessments associated with each tool or competency; paragraph 0052, discussing that the system can be configured to track these actions remotely, however, in most cases a particular worker or candidate can then be provided with a personal computer or work center which can include a local processor, a local non-transitory computer-readable medium, and associated computer instructions instructing the processor to utilize system input mechanisms to track user or candidate actions and transmit these actions back for recording in the worker or candidate profile database and thus be associated with the worker's profile information. In some such cases, these actions can include tool usage, tool feature usage, as detected through worker input while working on various current or previous projects, actions utilized while taking assessments or performing various tasks in training modules; paragraph 0053, discussing that it will be understood that various keystrokes, hotkey usage, cursor movements, sensor input data received from one or more sensor input devices, etc. can be associated with various skills, sequences, motions, etc. which can then be correlated to particular proficiencies with using associate tool features; paragraph 0064, discussing automatically tracked information with regards to utilizing a set of features of that tool related to a particular task; paragraph 0066, discussing that each of the plurality of worker profiles contained within the worker skill database can vary between each worker, wherein each worker has an associated worker skill proficiency for a plurality of features for various tools, i.e. software types, brands, functions within that software, etc....It is of particular advantage to update the worker skill database on a continual and iterative basis using processing circuitry and activity tracking. It will be appreciated that in some instances, a software program, such as an add-on, or a background module provided in the one or more programs, or on the worker's particular computer, can be utilized to track worker behavior and the worker skill database can then be automatically modified based on worker behavior. For example, a design program can be provided with an add-on which tracks functions, features, commands, keystrokes, hotkeys used, or any other potential parameter within a program. For purposes of illustration, if a particular worker regularly uses a strength analysis function in a design program, the use of that function can be tracked, and through repeated use it can then be assumed that the particular worker is proficient in using that particular command or function. As such, the use of that command or function can then be recognized by the program, add-on, and reported for inclusion in the worker skill database and associated with the particular worker's skillset; paragraph 0088, discussing that each user's computer can be loaded with an additional program which can keep track of time spent in various particular programs, i.e. AutoCad™ or SolidWorks™, and can also track user keystrokes when utilizing a specific program. Keystrokes can then be compared to a command list or hotkey list and a determination of used functions and frequency of use can be determined with regard to each particular program. It can then be assumed that repeated use of certain functions relates to a proficiency with respect to that particular function, and the user skill database can be updated accordingly; paragraphs 0045, 0051, 0069, 0073);
categorizing, based on the determined workforce skill activity type, a knowledge risk in a risk category selected from a set of predefined knowledge risk categories (paragraph 0006, discussing determining one or more deficiencies of one or more workers between associated worker skill proficiencies and the one or more necessary competencies; paragraph 0033, discussing that in some situations a deficiency can be detected between the competencies of the various workers within an organization and the needs of a particular project; paragraph 0070, discussing a method for determining competency deficiencies and matching between particular projects and available workers or candidates; paragraph 0080).
Hancock does not explicitly teach categorizing, based on the determined workforce skill activity type, a knowledge risk in a risk category selected from a set of predefined knowledge risk categories; assigning a risk level to the knowledge risk based on the risk category; automatically selecting and implementing, based on the risk category, the risk level, and the determined workforce skill activity type, an upskilling recommendation for reducing the knowledge risk, including: automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation; automatically identifying, based on the upskilling recommendation, a digitally stored training media content item; and automatically generating a link to the digitally stored training media content item; generating a graphical element using another user interface, the graphical element including the link and being selectable to play the digitally stored training media content item; receiving a selection of the graphical element; and in response to receiving the selection, playing, with a media playback device, the digitally stored training media content item. Gupta in the analogous art of workforce managements systems teaches:
categorizing, based on the determined workforce skill activity type, a knowledge risk in a risk category selected from a set of predefined knowledge risk categories (paragraph 0056, discussing an example user interface depicting enterprise risk due to proficiency gaps. The gaps may be identified, and the user interface may be used by users to proactively see and remedy the gaps…For example, notification 405 can provide a high-level notification summarizing the issues, which may be highlighted depending on the severity of the issue (e.g., severe (over a threshold) may be red, medium issues may be yellow, and low or no issues may be green). In this example, the summary notification 405 indicates that there is an overall 13% risk for factories within the enterprise over the next 30 days. In some embodiments, the time frame may be configurable for the user. Other configurations may include, for example, narrowing the information to a geographic region and/or department within the enterprise. Notifications 410, 415, and 420 may also be highlighted and provide more detailed information that is summarized by notification 405. For example, notification 410 indicates that there is a 23% risk of a shortfall of welders in Portland, Oreg. This information, as summarized, is over the next 30 days. Notification 415 indicates that there is an 11% risk of a shortfall of machinists in Chandler, Ariz.. Notification 420 indicates that there is an 18% risk of a surplus of fitters in Detroit, Mich. over the next 30 days. Note that surplus information may be generated as a gap as well; paragraph 0065, discussing that in user interface 700, tag clouds 710 and 715 may provide additional information to the user. For example, tag cloud 710 indicates the availability of the proficiency based on employee information in the enterprise. Tag cloud 710 indicates, for example, that the machine learning proficiency is highly available as is the Java proficiency. The deep learning proficiency, as indicated in the tag cloud 710, is relatively less available than, for example, the machine learning proficiency. Tag cloud 715 may indicate, for example, the percentage gap for proficiencies. For example, in tag cloud 715, the machine learning proficiency is relatively large font, and green in color, indicating that there is a gap in the availability versus the need. However, the green color may indicate that the there is an over-availability of employees having the machine learning proficiency. The overage is supported by viewing the machine learning proficiency in tag cloud 710 (showing the availability), which is larger font than the machine learning proficiency in tag cloud 705 (showing the need). Tag cloud 715, as shown, indicates that the statistics proficiency % gap is relatively large and negative (due to red font); paragraph 0067, discussing that each other proficiency of interest may be modeled, analyzed for gaps, and recommendations made);
assigning a risk level to the knowledge risk based on the risk category (paragraph 0004, discussing that in addition to identifying gaps, recommendations regarding retraining, hiring, and the like can be made based on further analysis to help users remedy the deficiencies; paragraph 0057, discussing that Table 425 may provide further information about the notifications 405, 410, 415, and 420. For example, the first-row entry in table 425 indicates that the project impacted is the Aluminum structures for fab project. The first-row entry further indicates that the 23% risk is due to attrition and may result in a shutdown of the project. This risk may be considered high [i.e., risk level] and accordingly the row entry in the table may be highlighted red like the notification 410; paragraph 0062, discussing that FIG. 6 illustrates an example user interface 600 depicting hiring trend gaps...Table 605 may provide, separated by proficiency, information about hiring and a recommendation for each proficiency. For example, the first-row entry of table 605 indicates that for the Java proficiency, the headcount needed is 50. The hiring rate is 8 (e.g., 8 hires per year), the attrition rate is 3, and the forecasted hiring gap is −5.5 (e.g., the enterprise will be short by 5-6 people with the Java proficiency over the next year). Based on the values and others for the java proficiency, the data processing system may provide a recommendation...The recommendation for the Java proficiency shown may be, for example, “HIRE.”…Table 605 may provide the hiring trends for a specific area of interest (e.g., by department, by factory, and the like) and, the hiring trend information shown in table 605 may be ranked such that, for example, the proficiencies facing the largest gaps are highlighted at the top of the table 605; paragraph 0065, discussing that in user interface 700, tag clouds 710 and 715 may provide additional information to the user. For example, tag cloud 710 indicates the availability of the proficiency based on employee information in the enterprise. Tag cloud 710 indicates, for example, that the machine learning proficiency is highly available as is the Java proficiency. The deep learning proficiency, as indicated in the tag cloud 710, is relatively less available than, for example, the machine learning proficiency. Tag cloud 715 may indicate, for example, the percentage gap for proficiencies. For example, in tag cloud 715, the machine learning proficiency is relatively large font, and green in color, indicating that there is a gap in the availability versus the need. However, the green color may indicate that the there is an over-availability of employees having the machine learning proficiency. The overage is supported by viewing the machine learning proficiency in tag cloud 710 (showing the availability), which is larger font than the machine learning proficiency in tag cloud 705 (showing the need). Tag cloud 715, as shown, indicates that the statistics proficiency % gap is relatively large and negative (due to red font); paragraph 0067, discussing that each other proficiency of interest may be modeled, analyzed for gaps, and recommendations made; paragraph 0066); and
automatically selecting and implementing, based on the risk category, the risk level, and the determined workforce skill activity type, an upskilling recommendation for reducing the knowledge risk (paragraph 0004, discussing that in addition to identifying gaps, recommendations regarding retraining, hiring, and the like can be made based on further analysis to help users remedy the deficiencies; paragraph 0065, discussing that in user interface 700, tag clouds 710 and 715 may provide additional information to the user. For example, tag cloud 710 indicates the availability of the proficiency based on employee information in the enterprise. Tag cloud 710 indicates, for example, that the machine learning proficiency is highly available as is the Java proficiency. The deep learning proficiency, as indicated in the tag cloud 710, is relatively less available than, for example, the machine learning proficiency. Tag cloud 715 may indicate, for example, the percentage gap for proficiencies. For example, in tag cloud 715, the machine learning proficiency is relatively large font, and green in color, indicating that there is a gap in the availability versus the need. However, the green color may indicate that the there is an over-availability of employees having the machine learning proficiency. The overage is supported by viewing the machine learning proficiency in tag cloud 710 (showing the availability), which is larger font than the machine learning proficiency in tag cloud 705 (showing the need). Tag cloud 715, as shown, indicates that the statistics proficiency % gap is relatively large and negative (due to red font); paragraph 0066, discussing that user interface 700 may further include, for example, graph 720, which may indicate the number of employees to be hired or retrained by proficiency. For example, each bar in the bar graph 720 may be for a given proficiency. The height of the bar may indicate the number of employees to be hired or retrained. For example, the fifth bar in the bar graph may indicate, for example, that 54 employees should be retrained with the Java proficiency [i.e., This shows selecting an upskilling recommendation for reducing the knowledge risk]; paragraph 0067, discussing that each other proficiency of interest may be modeled, analyzed for gaps, and recommendations made; paragraphs 0005, 0037, 0038).
Hancock is directed to a system and method for determining competency deficiencies and matching between particular projects and available workers. Gupta is directed to techniques for extraction and valuation of proficiencies for gap detection and remediation. Therefore, they are deemed to be analogous as they both are directed towards workforce management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hancock with Gupta because the references are analogous art because they are both directed to solutions for workforce management, which falls within applicant's field of endeavor (workforce knowledge risk mitigation), and because modifying Hancock to include Gupta’s features for categorizing, based on the determined workforce skill activity type, a knowledge risk in a risk category selected from a set of predefined knowledge risk categories; assigning a risk level to the knowledge risk based on the risk category, and assigning a risk level to the knowledge risk based on the risk category; and automatically selecting and implementing, based on the risk category, the risk level, and the determined workforce skill activity type, an upskilling recommendation for reducing the knowledge risk, in the manner claimed, would serve the motivation of accurate determination of proficiency gaps (Gupta at paragraph 0027); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Hancock-Gupta combination does not explicitly teach including: automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation; automatically identifying, based on the upskilling recommendation, a digitally stored training media content item; and automatically generating a link to the digitally stored training media content item; generating a graphical element using another user interface, the graphical element including the link and being selectable to play the digitally stored training media content item; receiving a selection of the graphical element; and in response to receiving the selection, playing, with a media playback device, the digitally stored training media content item. Regan in the analogous art of employee training systems teaches:
including: automatically identifying, based on the upskilling recommendation, a digitally stored training media content item (paragraph 0006, discussing an adaptive learning system that tracks learner interactions with educational content over multiple dimensions of learning and uses multiple statistical models and data analysis techniques to create personalized curricula for each learner and continuously evaluate and adjust curricula on a near-real-time basis; paragraph 0040: “a system provides an artificial intelligence (AI) based recommendation engine (hereinafter referred to as the “Brain”) which advises a learner on learning activities, resources & communities”; paragraph 0072, discussing that the method may select appropriate learning content based on data stored about the contact in a contact program (e.g., contact program indicates the contact's location and interests) and/or data stored about the event in the calendar program (e.g., meeting to discuss a particular topic), and or data stored about the user (e.g., user's profession, interests and affiliations). If the method is able to identify a contact the user is about to meet with, the method selects learning content appropriate for the contact ; paragraph 0078, discussing that the Brain can consider requirements when deciding what content to recommend to a user. A requirement can be mandatory and take the form of a requirement to complete a specific activity (e.g., provide learning on particular content) or to complete a single or set of activities that meets a criteria (e.g., provide learning on a certain topic which has a certain required level of mastery; paragraph 0080, discussing that the system is configured to maintain for each user a list of goals. Examples of these goals includes: meeting companywide requirements, meeting requirements of a particular role, meeting a manager's goals set or another's individual's goals set, and meeting the goal requirements that the learner has established for themselves. The Brain considers these goals and the skills required for each goal role when recommending content to the user. A goal may also be time sensitive and user defined. For example, the system is configured to enable a user to create a new goal and set up time constraints on that goal. The system can then optimize the learning schedule and curriculum of the user so they can achieve their goal in the required time. For example, if several different types of learning content are available that assist users in advancing to meet a goal, the system could suggest the one that fits within the user's time constraints... Another example of a user-defined goal is to gain mastery in a given competency (e.g., to become an expert in a given skill); paragraph 0158); and
automatically generating a link to the digitally stored training media content item (paragraph 0070, discussing that if the system determines from the user's calendar and contact programs that the user's next appointment is a meeting on a particular topic, the system can provide learning to the user on that specific topic; paragraph 0076, discussing that when the Brain returns a content recommendation set, instead of returning the actual content types and modalities, it can return metadata tags, which are then mapped to the available content pool; paragraph 0146, discussing that for the simulation modality, the tracking mechanism can track time/date sim was launched, location from which user launched sim, result of sim, path taken, time spent in sim, invitations, times parties arrived to sim, communications with Avatars, etc.; paragraph 0156, discussing that the Brain is configured to generate training content based on a dynamic model of a combination of different but orthogonal goals. For example, the goal of the company could be to keep cost below a threshold while the goal of the individual could be to increase their skill in a given skill to an expert level. When both goals are considered, it could be determined that the only training content that is economically feasible is training that is designed to increase the level of the employee to a competent level. Thus, rather than consider a single goal in determining content to recommend, the brain can consider multiple goals; paragraph 0169, discussing that the data stores may store the required/elective curriculum, the manager/instructor requirements/recommendations, peer recommendations, personal goals, all tracked data, user history, user proficiencies, learning plan, enrollments, user assessments,…, all media (e.g., sound, video, and text files),…, skills, categories,…, proficiency ratings, assessment scores, object interaction preferences, modality preferences, human interaction preferences, augmented reality score, e-books, immersive classrooms/learning labs, interactive videos, micro-applications, online classrooms, webcasts, single player simulations, immersive single/multi player simulations, SCORM media, hybrid single/multi player simulations/immersives/immersive-simulations, serious games, virtual courses, webinars, live events, onsite event applications, podcasts, notes, bookmarks, notifications, search results, log of chat messages, message board, study cards, shared data, scoreboard, simulations authored, etc. The data of the data stores may be accessible via the remote user devices);
generating a graphical element using another user interface, the graphical element including the link and being selectable to play the digitally stored training media content item (paragraph 0084, discussing that the system performs a Bayesian analysis of behavior within a modality (e.g. the movement in a learner's scores when the learner completes multiple E-Books sequentially) and movement between modalities (e.g. the movement in the learner's scores when the learner completes an E-Books and an interactive video sequentially), and then offers a recipe whereby each learner makes their next learning activity selection based on an updated analysis of previous outcomes, especially the learner's successes and failures within the last modality. For example, if a learner's scores are consistently positively affected by completing an E-Book and then a Simulation, the recipe will suggest Simulation content whenever the learner completes an E-Book; paragraph 0146, discussing that for the simulation modality, the tracking mechanism can track time/date sim was launched, location from which user launched sim, result of sim, path taken, time spent in sim, invitations, times parties arrived to sim, communications with Avatars, etc.; paragraph 0164, discussing that the dashboard tool may provide access to various users, including a manager, a learner, an instructor, and a peer, with dashboards. The users operating one of the remote devices may access the dashboard tool remotely. Interventions by the users through their respective dashboards act as inputs into the data stores. The manager may be a role assigned to an individual or a group of people who in a business context supervises learners. The manager can author, recommend, and require content, and evaluate learners; paragraph 0169, discussing that the data stores may store the required/elective curriculum, the manager/instructor requirements/recommendations, peer recommendations, personal goals, all tracked data, user history, user proficiencies, learning plan, enrollments, user assessments,…, all media (e.g., sound, video, and text files),…, skills,…, e-books, immersive classrooms/learning labs, interactive videos, micro-applications, online classrooms, webcasts, single player simulations, immersive single/multi player simulations, SCORM media, hybrid single/multi player simulations/immersives/immersive-simulations, serious games, virtual courses, webinars, live events, onsite event applications, podcasts, notes, bookmarks, notifications, search results, log of chat messages, message board, study cards, shared data, scoreboard, simulations authored, etc. The data of the data stores may be accessible via the remote user devices; paragraph 0179, discussing that the advisor may provide access (e.g., to a user of the remote device) to the prioritized set of content or modalities advised for a user, which could include at least one of a podcast, an e-book, an immersive learning lab/classroom, a serious game, a webinar, a webcast, compliance media, an onsite event application augmented reality, micro-application, virtual course, online classroom, interactive video, SCORM media, onsite event, interactive parable, single player simulation, immersive single/multi player simulation, collaborative challenge, hybrid single/multi player immersive/non-immersive simulations, etc. The catalog, as shown in FIG. 16, may provide access (e.g., to a user of the remote device) to the program of study, the curriculum program, quick links content, quick links skills, which could include at least one of the above-described content or modalities; paragraph 0183);
receiving a selection of the graphical element (paragraph 0183, discussing that a learner can use the learner dashboard to initiate an advisor session. The learner dashboard can be launched on a remote device of the user. The Advisor displays a list of requirements and activities that the user can choose to fulfill. The user has the ability to filter and modify Advisor suggestions (excluding required training) to create a more targeted list. The Advisor, in real time, updates the displayed list of recommended activities based on new criteria specified by the user and sends the list to the recipe tool, which becomes the added suggestions. The user then launches the activity in a chosen modality on the user device; paragraph 0132, discussing that the tracking mechanism may also track the launch of each activity by the user and a detailed activity stream of interaction with the activity including such events as moving from page to page in an e-book, listening to a podcast, completing a level of a serious game, attending a webinar, etc.; paragraph 0141, discussing that for the interactive video modality, the tracking mechanism can track time/day modality was launched, location from which user launched modality, time spent on modality, pauses, plays, and seeks performed, following of a link, viewing of embedded/specific data, etc.); and
in response to receiving the selection, playing, with a media playback device, the digitally stored training media content item (paragraph 0183, discussing that a learner can use the learner dashboard to initiate an advisor session. The learner dashboard can be launched on a remote device of the user. The Advisor displays a list of requirements and activities that the user can choose to fulfill. The user has the ability to filter and modify Advisor suggestions (excluding required training) to create a more targeted list. The Advisor, in real time, updates the displayed list of recommended activities based on new criteria specified by the user and sends the list to the recipe tool, which becomes the added suggestions. The user then launches the activity in a chosen modality on the user device; paragraph 0128, discussing that the modalities may include a Webcast or a Webinar. A Webcast is a media presentation distributed over the Internet using streaming media technology to distribute a single content source to many simultaneous listeners/viewers. A webcast may either be distributed live or on demand. A Webinar is an interactive learning activity that takes place online in a synchronous mode that involves one or more instructors and multiple learners; paragraph 0132, discussing that the tracking mechanism may also track the launch of each activity by the user and a detailed activity stream of interaction with the activity including such events as moving from page to page in an e-book, listening to a podcast, completing a level of a serious game, attending a webinar, etc.; paragraphs 0125, 0137, 0141, 0162).
The Hancock-Gupta combination describes features related to workforce management. Regan is directed to an employee learning system. Therefore, they are deemed to be analogous as they both are directed towards workforce management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Hancock-Gupta combination with Regan because the references are analogous art because they are both directed to solutions for workforce management, which falls within applicant's field of endeavor (workforce knowledge risk mitigation), and because modifying the Hancock-Gupta combination to include Regan’s features for including automatically identifying, based on the upskilling recommendation, a digitally stored training media content item; and automatically generating a link to the digitally stored training media content item; generating a graphical element using another user interface, the graphical element including the link and being selectable to play the digitally stored training media content item; receiving a selection of the graphical element; and in response to receiving the selection, playing, with a media playback device, the digitally stored training media content item, in the manner claimed, would serve the motivation of improving the user's skills (Regan at paragraph 0097); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Hancock-Gupta-Regan combination does not explicitly teach including: automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation. However, Carpenter in the analogous art of training systems teaches this concept. Carpenter teaches:
including: automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation (paragraph 0015, discussing that the method includes determining a risk score for the user based at least on the detection of the interaction with the electronic calendar invitation by the user; paragraph 0016, discussing that the method includes communicating electronic training to the user based at least on the detection of the interaction with the electronic calendar invitation by the user; paragraph 0093, discussing that whenever an organization wishes to provide security awareness training to users of the organization to help mitigate risks associated with calendar-based threats, the organization may implement security awareness training system…Security awareness training system may communicate with email/calendar host server using one or more defined APIs to access electronic calendars and/or mailboxes of one or more users of the organization; paragraph 0094, discussing that the permissions may allow security awareness training system to insert calendar events for a single user or for multiple users within a group ; paragraph 0107, discussing that the simulated attack generator may add a link (such as to training materials via a landing page hosted by the security awareness training system) and/or an attachment (such as training materials) to the electronic calendar invitation. In some examples, the generated electronic calendar invitation may appear as if it has been accepted by one or more users that the electronic calendar invitation was sent to...In an implementation, the simulated attack generator may embed or insert benign content in the electronic calendar invitation…In an example, the benign content may facilitate the delivery of the security awareness training to the user in response to the user interaction with the electronic calendar invitation; paragraph 0114, discussing that the electronic training may be delivered via a link that is clicked by the user or via an attachment accessed/opened by the user. In an example, the electronic training may be delivered in various forms, for example, via a landing page link (such as a webpage link) or via a file attachment (such as an embedded video, a Word document, or Portable Document Format (PDF) file) to the electronic calendar invitation. In some examples, the means by which the electronic training is delivered (for example, via a landing page link or via a file attachment) may also vary according to a specific type of exploit that was used in the electronic calendar invitation; paragraph 0135, discussing communicating electronic training to the user based at least on the detection. In an implementation, the security awareness training system may communicate the electronic training to the user if the user interacts with the electronic calendar invitation. In an example, the electronic training may be delivered in various forms, for example, via a landing page link (such as a webpage link) or via a file attachment (such as an embedded video, a Word document, or Portable Document Format (PDF) file) to the electronic calendar invitation).
Examiner notes that Carpenter, in addition to Regan, as cited above, also teaches generating a graphical element using another user interface, the graphical element including the link and being selectable to play the digitally stored training media content item (paragraph 0114, discussing that the electronic training may be delivered via a link that is clicked by the user or via an attachment accessed/opened by the user. In an example, the electronic training may be delivered in various forms, for example, via a landing page link (such as a webpage link) or via a file attachment (such as an embedded video, a Word document, or Portable Document Format (PDF) file) to the electronic calendar invitation. In some examples, the means by which the electronic training is delivered (for example, via a landing page link or via a file attachment) may also vary according to a specific type of exploit that was used in the electronic calendar invitation; paragraph 0135, discussing communicating electronic training to the user based at least on the detection. In an implementation, the security awareness training system may communicate the electronic training to the user if the user interacts with the electronic calendar invitation. In an example, the electronic training may be delivered in various forms, for example, via a landing page link (such as a webpage link) or via a file attachment (such as an embedded video, a Word document, or Portable Document Format (PDF) file) to the electronic calendar invitation).
The Hancock-Gupta-Regan combination describes features related to workforce analysis. Carpenter is directed to an employee training system. Therefore, they are deemed to be analogous as they both are directed towards workforce management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Hancock-Gupta-Regan combination with Carpenter because the references are analogous art because they are both directed to solutions for workforce management, which falls within applicant's field of endeavor (workforce knowledge risk mitigation), and because modifying the Hancock-Gupta-Regan combination to include Carpenter’s feature for including automatically updating a calendar to include an invitation to a training session associated with the upskilling recommendation, in the manner claimed, would serve the motivation of facilitating user training (Carpenter at paragraph 0069); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, the Hancock-Gupta-Regan-Carpenter combination teaches the computer-implemented method of claim 1. Hancock further teaches further comprising: generating a graphical user interface (paragraph 0035, discussing that the true extent of a worker's skillsets or proficiencies with certain features of a tool can be readily ascertainable. For example, in a self-reported scenario two workers can claim to know how to use and use well Microsoft's Excel software. Whereas in practice worker 1 can successfully utilize the various regression tools, while worker 2 is proficient with creating and generating pivot tables. This example illustrates that for a given tool, and especially one that has hundreds if not thousands of functions, workers can be proficient at certain tasks utilizing particular features of the tool, while not being able to do other tasks; paragraph 0041, discussing that the input interface interacts through a wireless or network connection in order to receive input…; paragraph 0066, discussing that in some instances, a software program, such as an add-on, or a background module provided in the one or more programs, or on the worker's particular computer, can be utilized to track worker behavior and the worker skill database can then be automatically modified based on worker behavior. For example, a design program can be provided with an add-on which tracks functions, features, commands, keystrokes, hotkeys used, or any other potential parameter within a program. For purposes of illustration, if a particular worker regularly uses a strength analysis function in a design program, the use of that function can be tracked, and through repeated use it can then be assumed that the particular worker is proficient in using that particular command or function. As such, the use of that command or function can then be recognized by the program, add-on, and reported for inclusion in the worker skill database and associated with the particular worker's skillset); and
receiving, via the graphical user interface, a workforce knowledge risk query (paragraph 0049, discussing instructions to retrieve, present, and track worker or candidate performance on one or more assessments. In such cases the assessment can have various actions associated therewith which correlate to worker proficiency upon performing actions correctly, in a particular sequence etc. As such, completion can result in an assessment score and an associated proficiency with regard to that particular required competency from which a best fit can be determined thereby. Further, each assessment can be associated with a particular tool and features thereof wherein the system can then update each particular worker's or candidate's profile and particular worker or candidate skill competency or proficiency information based on the worker's or candidate's tracked assessments associated with each tool or competency); and
generating another graphical user interface in response to the query, the another graphical user interface including a graphical representation of the workforce skill activity type (paragraph 0049, discussing that each assessment can be associated with a particular tool and features thereof wherein the system can then update each particular worker's or candidate's profile and particular worker or candidate skill competency or proficiency information based on the worker's or candidate's tracked assessments associated with each tool or competency; paragraph 0052, discussing associated computer instructions instructing the processor to utilize system input mechanisms to track user or candidate actions and transmit these actions back for recording in the worker or candidate profile database and thus be associated with the worker's profile information. In some such cases, these actions can include tool usage, tool feature usage, as detected through worker input while working on various current or previous projects, actions utilized while taking assessments or performing various tasks in training modules; paragraph 0088, discussing that each user's computer can be loaded with an additional program which can keep track of time spent in various particular programs, i.e. AutoCad™ or SolidWorks™, and can also track user keystrokes when utilizing a specific program. Keystrokes can then be compared to a command list or hotkey list and a determination of used functions and frequency of use can be determined with regard to each particular program. It can then be assumed that repeated use of certain functions relates to a proficiency with respect to that particular function, and the user skill database can be updated accordingly).
Hancock does not explicitly teach the another graphical user interface including a graphical representation of the knowledge risk. However, Gupta in the analogous art of workforce management systems teaches this concept. Gupta teaches:
the another graphical user interface including a graphical representation of the knowledge risk (paragraph 0004, discussing that in addition to identifying gaps, recommendations regarding retraining, hiring, and the like can be made based on further analysis to help users remedy the deficiencies; paragraph 0062, discussing that FIG. 6 illustrates an example user interface 600 depicting hiring trend gaps...Table 605 may provide, separated by proficiency, information about hiring and a recommendation for each proficiency. For example, the first-row entry of table 605 indicates that for the Java proficiency, the headcount needed is 50. The hiring rate is 8 (e.g., 8 hires per year), the attrition rate is 3, and the forecasted hiring gap is −5.5 (e.g., the enterprise will be short by 5-6 people with the Java proficiency over the next year). Based on the values and others for the java proficiency, the data processing system may provide a recommendation...The recommendation for the Java proficiency shown may be, for example, “HIRE.”…Table 605 may provide the hiring trends for a specific area of interest (e.g., by department, by factory, and the like) and, the hiring trend information shown in table 605 may be ranked such that, for example, the proficiencies facing the largest gaps are highlighted at the top of the table 605; paragraph 0065, discussing that in user interface 700, tag clouds 710 and 715 may provide additional information to the user. For example, tag cloud 710 indicates the availability of the proficiency based on employee information in the enterprise. Tag cloud 710 indicates, for example, that the machine learning proficiency is highly available as is the Java proficiency. The deep learning proficiency, as indicated in the tag cloud 710, is relatively less available than, for example, the machine learning proficiency. Tag cloud 715 may indicate, for example, the percentage gap for proficiencies. For example, in tag cloud 715, the machine learning proficiency is relatively large font, and green in color, indicating that there is a gap in the availability versus the need. However, the green color may indicate that the there is an over-availability of employees having the machine learning proficiency. The overage is supported by viewing the machine learning proficiency in tag cloud 710 (showing the availability), which is larger font than the machine learning proficiency in tag cloud 705 (showing the need). Tag cloud 715, as shown, indicates that the statistics proficiency % gap is relatively large and negative (due to red font); paragraph 0067, discussing that each other proficiency of interest may be modeled, analyzed for gaps, and recommendations made; paragraph 0073, discussing that a client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via this interface; paragraph 0024).
Hancock is directed to a system and method for determining competency deficiencies and matching between particular projects and available workers. Gupta is directed to techniques for extraction and valuation of proficiencies for gap detection and remediation. Therefore, they are deemed to be analogous as they both are directed towards workforce management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hancock with Gupta because the references are analogous art because they are both directed to solutions for workforce management, which falls within applicant's field of endeavor (workforce knowledge risk mitigation), and because modifying Hancock to include Gupta’s feature for including the another graphical user interface including a graphical representation of the knowledge risk, in the manner claimed, would serve the motivation of accurate determination of proficiency gaps (Gupta at paragraph 0027); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 3, the Hancock-Gupta-Regan-Carpenter combination teaches the computer-implemented method of claim 2. Although not explicitly taught by Hancock, Gupta in the analogous art of workforce management systems teaches wherein the another graphical user interface includes first graphical elements indicating: a set of skills of a workforce; and another knowledge risk for each skill in the set of skills (paragraph 0004, discussing that using a multi-level model for each proficiency that accounts for enterprise needs as well as hiring, retraining, and the like, a relationship between proficiencies, projects, and employees over time may be generated as a multi-dimensional temporal model…In addition to identifying gaps, recommendations regarding retraining, hiring, and the like can be made based on further analysis to help users remedy the deficiencies; paragraph 0024, discussing that proficiency gap results generated by the data processing system may be transmitted to a user system and may be output to a user via a graphical user interface (GUI) displayed by the user system; paragraph 0027, discussing that the output of the modeling sub-system may be, for example, a multi-level model that, when simulated, defines the need for each proficiency at each level of the enterprise, tracks the gaps…; paragraph 0036, discussing that the gap identification subsystem is configured to use the models from modelling subsystem and the proficiency clusters from clustering subsystem 130 to identify proficiency gaps. For example, the time-varying, multi-dimensional temporal causal models may be executed or simulated to forecast gaps in proficiencies due to reasons including but not limited to the extent, quality or lack thereof in talent sourcing, training/certification, availability of individuals, risk of talent flight (deep learning models for attrition), financial costs, and risk to revenue; paragraph 0041, discussing that notification subsystem 145 is configured to generate notifications based on the severity analysis subsystem 140 outputs. For example, gap identification subsystem 135 may output proficiency gaps above a threshold that may be sufficient to generate a notification…In some embodiments, the notifications may appear to the user on their graphical user interface...For example, notifications may appear within, for example, user interfaces 400, 500, 600 and/or 700 when a user logs into a user interface; paragraph 0054, discussing that user interface 600 of FIG. 6 provides risk information in addition to recommendations; paragraph 0056, discussing that 0056, discussing that FIG. 4 illustrates an example user interface 400 depicting enterprise risk due to proficiency gaps. The gaps may be identified, and the user interface 400 may be used by users to proactively see and remedy the gaps. For example, notification 405 can provide a high-level notification summarizing the issues, which may be highlighted depending on the severity of the issue (e.g., severe (over a threshold) may be red, medium issues may be yellow, and low or no issues may be green; paragraph 0065, discussing that in user interface 700, tag clouds 710 and 715 may provide additional information to the user. For example, tag cloud 710 indicates the availability of the proficiency based on employee information in the enterprise. Tag cloud 710 indicates, for example, that the machine learning proficiency is highly available as is the Java proficiency. The deep learning proficiency, as indicated in the tag cloud 710, is relatively less available than, for example, the machine learning proficiency. Tag cloud 715 may indicate, for example, the percentage gap for proficiencies. For example, in tag cloud 715, the machine learning proficiency is relatively large font, and green in color, indicating that there is a gap in the availability versus the need. However, the green color may indicate that the there is an over-availability of employees having the machine learning proficiency. The overage is supported by viewing the machine learning proficiency in tag cloud 710 (showing the availability), which is larger font than the machine learning proficiency in tag cloud 705 (showing the need). Tag cloud 715, as shown, indicates that the statistics proficiency % gap is relatively large and negative (due to red font)).
Hancock is directed to a system and method for determining competency deficiencies and matching between particular projects and available workers. Gupta is directed to techniques for extraction and valuation of proficiencies for gap detection and remediation. Therefore, they are deemed to be analogous as they both are directed towards workforce management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hancock with Gupta because the references are analogous art because they are both directed to solutions for workforce management, which falls within applicant's field of endeavor (workforce knowledge risk mitigation), and because modifying Hancock to include Gupta’s feature for including wherein the another graphical user interface includes first graphical elements indicating: a set of skills of a workforce; and a knowledge risk for each skill in the set of skills, in the manner claimed, would serve the motivation of accurate determination of proficiency gaps (Gupta at paragraph 0027); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 4, the Hancock-Gupta-Regan-Carpenter combination teaches the computer-implemented method of claim 3. Hancock further teaches wherein the another graphical user interface includes second graphical elements indicating, for each skill in the set of skills, a knowledge level of each member of a team of members (paragraph 0010, discussing that updating each worker's profile and an associated specific tracked worker skill proficiency data based on the associated worker's associated training history with each associated available tool; paragraph 0035, discussing that the true extent of a worker's skillsets or proficiencies with certain features of a tool can be readily ascertainable. For example, in a self-reported scenario two workers can claim to know how to use and use well Microsoft's Excel software. Whereas in practice worker 1 can successfully utilize the various regression tools, while worker 2 is proficient with creating and generating pivot tables. This example illustrates that for a given tool, and especially one that has hundreds if not thousands of functions, workers can be proficient at certain tasks utilizing particular features of the tool, while not being able to do other tasks).
As per claim 5, the Hancock-Gupta-Regan-Carpenter combination teaches the computer-implemented method of claim 4. Hancock further teaches wherein the another graphical user interface includes third graphical elements indicating, for each skill in the set of skills, whether at least minimum predefined skill knowledge data for the team of members is known (paragraph 0016, discussing determining one or more devoid candidate proficiencies for each candidate profile representing a differential between one or more skills required for project completion and the candidate skill proficiency data; paragraph 0047, discussing that in particular when weighted worker match scores are below a desired level or threshold, or even in situations where weighted candidate match scores are below a desired level or threshold, the system can then determine missing or deficient competencies which would bring the worker or the candidate above the threshold or otherwise improve their weighted match score. the system can then analyze the training module database associated with the deficient competencies and generate a training recommendation to increase the weighted worker match score or the weighted candidate match score; paragraph 0066, discussing that it will be understood that each of the plurality of worker profiles contained within the worker skill database can vary between each worker, wherein each worker has an associated worker skill proficiency for a plurality of features for various tools, i.e. software types, brands, functions within that software, etc. In some instances, each worker profile can be manually input by each worker upon profile creation, and then be updated periodically manually. However, it is of particular advantage to update the worker skill database on a continual and iterative basis using processing circuitry and activity tracking. It will be appreciated that in some instances, a software program, such as an add-on, or a background module provided in the one or more programs, or on the worker's particular computer 70, can be utilized to track worker behavior and the worker skill database can then be automatically modified based on worker behavior. For example, a design program can be provided with an add-on which tracks functions, features, commands, keystrokes, hotkeys used, or any other potential parameter within a program; paragraphs 0035, 0042).
As per claim 7, the Hancock-Gupta-Regan-Carpenter combination teaches the computer-implemented method of claim 5. Although not explicitly taught by Hancock, Gupta teaches wherein the another graphical user interface includes fourth graphical elements indicating, for each skill in the set of skills, a knowledge risk level based on a concentration of knowledge within a subset of members of the team of members (paragraph 0065, discussing that in user interface 700, tag clouds 710 and 715 may provide additional information to the user. For example, tag cloud 710 indicates the availability of the proficiency based on employee information in the enterprise. Tag cloud 710 indicates, for example, that the machine learning proficiency is highly available (as the font is quite large) as is the Java proficiency. The deep learning proficiency, as indicated in the tag cloud 710, is relatively less available than, for example, the machine learning proficiency. Tag cloud 715 may indicate, for example, the percentage gap for proficiencies. For example, in tag cloud 715, the machine learning proficiency is relatively large font, and green in color, indicating that there is a gap in the availability versus the need. However, the green color may indicate that the there is an over-availability of employees having the machine learning proficiency. The overage is supported by viewing the machine learning proficiency in tag cloud 710 (showing the availability), which is larger font than the machine learning proficiency in tag cloud 705 (showing the need). Tag cloud 715, as shown, indicates that the statistics proficiency % gap is relatively large and negative (due to red font)).
Hancock is directed to a system and method for determining competency deficiencies and matching between particular projects and available workers. Gupta is directed to techniques for extraction and valuation of proficiencies for gap detection and remediation. Therefore, they are deemed to be analogous as they both are directed towards workforce management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hancock with Gupta because the references are analogous art because they are both directed to solutions for workforce management, which falls within applicant's field of endeavor (workforce knowledge risk mitigation), and because modifying Hancock to include Gupta’s feature for including wherein the another graphical user interface includes fourth graphical elements indicating, for each skill in the set of skills, a knowledge risk level based on a concentration of knowledge within a subset of members of the team of members, in the manner claimed, would serve the motivation of accurate determination of proficiency gaps (Gupta at paragraph 0027); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 11 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 11 the Hancock-Gupta-Regan-Carpenter combination teaches a system, comprising: one or more processors; and non-transitory computer-readable storage storing instructions (Hancock, paragraph 0005, 0006, 0008).
Claim 12 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 2, as discussed above.
Claim 13 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 3, as discussed above.
Claim 14 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 4, as discussed above.
Claim 15 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 5, as discussed above.
Claim 17 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 7, as discussed above.
Claim 18 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 8, as discussed above.
Claim 19 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 9, as discussed above.
Claim 20 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 10, as discussed above.
21. Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Hancock in view of Gupta, in view of Regan, in view of Carpenter, in further view of Hey et al., Pub. No.: US 2014/0137074 A1, [hereinafter Hey].
As per claim 6, the Hancock-Gupta-Regan-Carpenter combination teaches the computer-implemented method of claim 5. Although not explicitly taught by Hancock, Gupta in the analogous art of workforce management systems teaches wherein the another graphical user interface includes fourth graphical elements indicating, for each skill in the set of skills, a knowledge risk level based on a combination of: a modification frequency of the skill; and a percentage of members of the team of members that have at least a minimum skill level (paragraph 0035, discussing that the proficiencies within a proficiency cluster may be weighted and scored to identify those proficiencies that are more valuable within the cluster. The weighting and scoring may be based on internal and/or external data regarding compensation for the job category associated with the cluster, project costs for projects needing the type of employee that has the proficiencies within the job category associated with the cluster. A linear regression model may be used, for example, to weight the proficiencies in any given cluster based on, for example, frequency of the proficiency appearing within analyzed resumes, the rarity of a proficiency internally or externally, the demand for the proficiency internally or externally, and the like. Further, the weight of a proficiency in one cluster may be different from the weight applied to the proficiency in a different cluster. For example, the weight of efficient in the software developer cluster may be different than the weight of efficient in the product manager cluster; paragraph 0038, discussing that the recommendation subsystem is configured to use the identified gaps from gap identification subsystem 135 and the models from modelling subsystem 125 to generate recommendations for remedying the identified gaps. The remedies are sought with certain boundary conditions defined by the end users for variables that they are able to directly influence such as, for example, the maximum number of employees that can be retrained in a given time period,…, the minimum time taken by an employee to switch projects, the maximum time projects can wait for an employee to reskill, percentage changes in compensation permissible based on proficiencies, and the like…The simulations may be used to find the shortest path to a positive outcome for variables such as revenue, profit, project execution, project production, and the like…For example, the number of employees to retrain/cross-train/reskill on a specific proficiency can be recommended with values indicating a percentage of the proficiency gap...; paragraph 0062, discussing that FIG. 6 illustrates an example user interface 600 depicting hiring trend gaps...Table 605 may provide, separated by proficiency, information about hiring and a recommendation for each proficiency. For example, the first-row entry of table 605 indicates that for the Java proficiency, the headcount needed is 50. The hiring rate is 8 (e.g., 8 hires per year), the attrition rate is 3, and the forecasted hiring gap is −5.5 (e.g., the enterprise will be short by 5-6 people with the Java proficiency over the next year). Based on the values and others for the java proficiency, the data processing system may provide a recommendation...The recommendation for the Java proficiency shown may be, for example, “HIRE.”…Table 605 may provide the hiring trends for a specific area of interest (e.g., by department, by factory, and the like) and, the hiring trend information shown in table 605 may be ranked such that, for example, the proficiencies facing the largest gaps are highlighted at the top of the table 605; paragraph 0065).
Hancock is directed to a system and method for determining competency deficiencies and matching between particular projects and available workers. Gupta is directed to techniques for extraction and valuation of proficiencies for gap detection and remediation. Therefore, they are deemed to be analogous as they both are directed towards workforce management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hancock with Gupta because the references are analogous art because they are both directed to solutions for workforce management, which falls within applicant's field of endeavor (workforce knowledge risk mitigation), and because modifying Hancock to include Gupta’s feature for including wherein the another graphical user interface includes fourth graphical elements indicating, for each skill in the set of skills, a knowledge risk level based on a combination of: a modification frequency of the skill; and a percentage of members of the team of members that have at least a minimum skill level, in the manner claimed, would serve the motivation of accurate determination of proficiency gaps (Gupta at paragraph 0027); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Hancock-Gupta-Regan-Carpenter combination does not explicitly teach a knowledge risk level based on a combination of: a modification frequency of the skill. However, Hey in the analogous art of workforce analysis systems teaches this concept. Hey teaches:
a knowledge risk level based on a combination of: a modification frequency of the skill (paragraph 0007, discussing that one or more of the following features may be included. Determining the one or more expert rankings may be based upon, at least in part, one or more of a number of code deliveries associated with the one or more software developers, a change in code complexity associated with the one or more software developers, a code quality change associated with the one or more software developers, a persistence of a code change associated with the one or more software developers, and a peer assessment associated with the one or more software developers. The one or more current code elements may be identified based upon, at least in part, a change frequency associated with one or more code elements. The one or more current code elements may be identified based upon, at least in part, receiving a threshold number of related inputs. The one or more current code elements may be identified based upon, at least in part, an amount of time one or more code elements form part of an active view. The one or more current code elements may be identified based upon, at least in part, a time-limited analysis; paragraph 0070, discussing that the CEI (Code Expert Identification) process may identify a need for assistance from an expert regarding current code elements. As also noted above, in certain embodiments, identifying 206 a need for assistance may be based upon receiving a request from an active software developer for assistance. In certain embodiments, identifying a need for assistance may additionally/alternatively occur automatically, in whole or in part. For example, referring now also to FIG. 3, in certain embodiments CEI process may identify a need for assistance from an expert based upon, at least in part, amount of time since the active software developer last edited the identified current code elements. For example, if an active software developer has maintained a code element (or various portions of a code element) in an active view for a particularly long amount of time without making edits, the CEI process may determine that the developer is experiencing difficulty in identifying and/or composing appropriate changes to the code element (and/or related code elements). In certain, the CEI process may identify a need for assistance based upon this determination; paragraph 0074).
The Hancock-Gupta-Regan-Carpenter combination describes features related to workforce management. Hey is directed to a workforce analysis system. Therefore, they are deemed to be analogous as they both are directed towards workforce management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Hancock-Gupta-Regan-Carpenter combination with Hey because the references are analogous art because they are both directed to solutions for workforce management, which falls within applicant's field of endeavor (workforce knowledge risk mitigation), and because modifying the Hancock-Gupta-Regan-Carpenter combination to include Hey’s feature for including a knowledge risk level based on a combination of: a modification frequency of the skill, in the manner claimed, would serve the motivation of facilitating more accurate determination of expert rankings (Hey at paragraph 0069); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 16 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 6, as discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mui et al., Pub. No.: US 2003/0229529 A1 – describes a method for enterprise workforce planning.
Gidugu et al., Pub. No.: US 2014/0101068 A1 – describes techniques that provide for a dashboard where the student awards and achievements, monthly progress reports may be updated. Such an approach provides good source for parents to see their children status and can also compare with others. The system may also enable students, teachers and management were to add comments and update information to for viewing by a main administrator, as well as to update any other important information about the event. All these changes can be updated in the event calendar.
Dion, Pub. No.: US 2008/0318197 A1 – describes an assessment that particularly points out those areas, if any, where the health care professional's knowledge is deficient. If the health care professional has any areas which need improvement, a list of relevant courses is also displayed at the provider system.
Perreault et al., Pub. No.: US 2011/0055100 A1 – describes a method and system for integrated professional continuing education related services.
Bergson-Shilcock, Amanda. "Funding Resilience: How Public Policies Can Support Businesses in Upskilling Workers for a Changing Economy." National Skills Coalition (2020) – describes employer-based upskilling programs that have thrived by connecting eager workers with demand-driven training that leads to career advancement.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. 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 extension fee 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 DARLENE GARCIA-GUERRA whose telephone number is (571) 270-3339. The examiner can normally be reached M-F 7:30a.m.-5:00p.m. EST.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
Prosecution Insights