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 Accordingly, the previously issued claim rejection under 35 U.S.C. 112(a) has been withdrawn.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 t