Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 08, 2025, has been entered.
Response to Arguments
Applicant's arguments filed with respect to rejections under 35 USC 101 have been fully considered but they are not persuasive. Applicant argues the dynamic updating and continuous refinement offer a technological advancement. Examiner disagrees. The updating and refinement, as claimed, are abstract ideas. For example, as the employee information changes, the learning content also changes. Using computer technology to speed up the process does not integrate the abstract idea into a practical application. Any mention of machine learning is only in the context of the matrix that is claimed. The claims are not focused on any improvement to machine learning or technology. Dynamically updating a learning experience by modifying the learning content based on updated employee information is an improvement to the generating learning content which is an abstract idea. The instant claims do not recite adjustments to parameters of a machine learning model to optimize performance as in Desjardin. As the fact patterns do not align, Examiner upholds the rejection under 35 USC 101.
Applicant's arguments filed with respect to rejections under 35 USC 103 have been fully considered but they are not persuasive. Applicant argues the cited prior art does not teach the new limitations. Examiner has updated the rejection below to address the newly added limitations. Tiwari et al clearly discloses a dynamic learning system that is updated based on updated user proficiencies and other information ([0024, 0068] - the system is dynamic and can update the learning path based on the mentor's feedback and/or changes in the candidate's proficiency (e.g., as identified through testing), experience, and/or education); and
refining the interpersonal affinity-behavioral matrix based on updated performance data to provide an accurate reflection of an evolving professional profile of the employee ([0004, 0024, 0068] - the framework can respond dynamically to real-time inputs, allowing changes in the candidate's abilities and skillset evaluation to modify the recommended learning path for that candidate.).
Further, Applicants argue there is no teaching, suggestion or motivation to combine Tiwari et al with Pappada et al. KSR forecloses Applicant' s argument that a specific teaching is required for a finding of obviousness. KSR, 127 S.Ct. at 1741, 82 USPQ2d at 1396. In addition, Examiner notes that Tiwari et al relates to an employee training system and generating a dynamic learning path. Pappada et al discloses a simulation-based medical education platform and dynamic personalized training. Training and Education are analogous and therefore examiner upholds the rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-15, 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1-15, 17-20 is/are directed to a method and system. Thus, all the claims are within the four potentially eligible categories of invention (a process, a machine and an article of manufacture, respectively), satisfying Step 1 of the Subject Matter Eligibility (SME) test.
As per Prong One of Step 2A of the §101 eligibility analysis set forth in MPEP 2106, the Examiner notes that the claims recite mental processes and certain methods of organizing human activity.
More specifically, independent claims 1 and 11 recite:
collecting data pertaining to at least one of a current skill set, completed education or training, work experience, performance metrics, learning styles, or career aspirations of an employee [mental process – observation/evaluation];
analyzing the data using
identifying at least one of a skills gap or a development opportunity relative to a predefined competency model for an organizational role [mental process – observation/evaluation];
generating a personalized learning experience based on the skills gap or the development opportunity, where the personalized learning experience conforms to a learning style adapted to the employee based on the interpersonal affinity-behavioral matrix [mental process – evaluation];
dynamically updating the personalized learning experience based on performance data and feedback collected
modifying presentation of learning content based on input from the employee, the performance metrics, and preferences through the interpersonal affinity-behavioral matrix; [mental process - pen/paper]and
refining the interpersonal affinity-based matrix based on updated performance data to provide an accurate reflection of an evolving professional profile of the employee. [mental process – evaluation or pen/paper]
In addition independent claim 11 recites integrating an employee calendar to schedule a learning session which is both mental process and certain methods of organizing human activity.
The independent claims recite a process/system to collect and analyze employee information to generate a personalized learning experience based on skill gaps that is scheduled and dynamically updated based on employee information. The claims relate to teaching and managing personal behavior which is certain methods of organizing human activity. The nominal recitation of machine learning algorithm, virtual reality artifact creation, and user interface does not necessarily preclude the claim from reciting an abstract idea as evidenced by the analysis at Prong 2 of Step 2A.
Regarding Prong Two of Step 2A, a claim reciting an abstract idea must be analyzed to determine whether any additional elements in the claim integrate the judicial exception into a practical application. Limitations that are indicative of integration into a practical application include: Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
In this case, the independent claims do not include limitations that meet the criteria listed above, thus the abstract idea is not integrated into a practical application. Independent claim 1 recites a machine learning algorithm, which amounts to using a computer as a tool to implement an algorithm and does not integrate the abstract idea into a practical application. Creating virtual reality artifacts simulating real-world scenarios, as claimed, only generally link the use of the abstract idea to a particular technological environment. There is no improvement to the computer system or VR technology. In addition, presentation and collection of data with a user interface also amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application. Independent claim 11 recites a computer system comprising one or more processors and non-transitory computer-readable storage media encoding instructions to cause the computer to perform the claimed method. This amounts to using a computer as a tool to implement an algorithm and does not integrate the abstract idea into a practical application. Further, the machine learning algorithm and user interface amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application. Creating virtual reality artifacts simulating real-world scenarios, as claimed, only generally link the use of the abstract idea to a particular technological environment. There is no improvement to the computer system or VR technology.
The dependent claims further limit the abstract idea and some recite additional elements that do not integrate the abstract idea into a practical application. Claims 2 and 12 recite obtaining input from employee through questionnaires, feedback, social media interactions or collaborative tools. This data collection is mental process as it amounts to observations or evaluations that can be performed mentally or with pen and paper. Computer implementation via the claimed system does not integrate the abstract idea into a practical application for the same reasons as claim 11. Claims 3, 4, 13 and 14 recite various machine learning algorithms to establish a matrix and identify a skills gap. This amounts to using a computer as a tool to perform an abstract idea as described in the independent claims. There is no integration into a practical application. Claims 5 and 15 recites options to present personalized learning experiences which can be practically performed using pen and paper. This mental process, while implemented via computer processor, does not integrate the abstract idea into a practical application. Claim 6 recite scheduling a learning session which is both mental process and certain methods of organizing human activity. The abstract idea is not integrated into a practical application. The use of a digital calendar amounts to using a computer as a tool to perform the abstract idea. Claims 7 and 17 uses generative AI to create a learning task. The nominal recitation of generative AI amounts to using a computer as a tool to perform the abstract idea to of creating a learning task. There is no integration into a practical application. Claims 8 and 18 recite details of the personal learning experience which is certain methods of organizing human activity. Any computer implementation amounts to using a computer as a tool to perform the abstract idea and there is no integration into a practical application. Claims 9, 10, 19 and 20 recite details of employee feedback which is an abstract idea. Using an interface mechanism amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
The claims do not include limitations beyond generally linking the use of the abstract idea to a particular technological environment. When considered individually, the system and software claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. The invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense.
Lastly and in accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instruction to apply the exception using generic computer component. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al, US 2024/0330834 in view of Pappada et al, US 2023/0039882.
As per claim 1, Tawari et al teaches a computer-implemented method for enhancing employee skills and competencies, comprising:
collecting data pertaining to at least one of a current skill set, completed education or training, work experience, performance metrics, learning styles, or career aspirations of an employee ([0062] – collect data relating to skills, performance, experiences, etc);
analyzing the data using a machine learning algorithm to establish an interpersonal affinity-behavioral matrix for the employee ([0062] – feature matrix used in conjunction with machine learning model to generate a personalized upskilling program tailed to each candidate);
identifying at least one of a skills gap or a development opportunity relative to a predefined competency model for an organizational role ([0026]);
generating a personalized learning experience based on the skills gap or the development opportunity, where the personalized learning experience conforms to a learning style adapted to the employee based on the interpersonal affinity-behavioral matrix ([0026, 0072] – customized learning path based on skills gap;
providing a user interface through which the employee interacts with the personalized learning experience ([0065, 0068, 0073] – candidate accesses learning path to complete tasks for each module/skillset); and
dynamically updating the personalized learning experience based on performance data and feedback collected through the user interface ([0024, 0065, 0068, 0069, 0073] – learning path dynamically updated based on collected data)
modifying presentation of learning content based on input from the employee, the performance metrics and preferences through the interpersonal affinity-behavioral matrix ([0024, 0068] - the system is dynamic and can update the learning path based on the mentor's feedback and/or changes in the candidate's proficiency (e.g., as identified through testing), experience, and/or education); and
refining the interpersonal affinity-behavioral matrix based on updated performance data to provide an accurate reflection of an evolving professional profile of the employee ([0004, 0024, 0068] - the framework can respond dynamically to real-time inputs, allowing changes in the candidate's abilities and skillset evaluation to modify the recommended learning path for that candidate.).
Tiwari et al fails to disclose, while Pappada et al discloses creating virtual reality artifacts comprising digital environments simulating real-world scenarios relevant to the skills and competencies being developed, wherein the virtual reality artifacts are customized to mirror job conditions; and wherein the user interface includes virtual reality components that integrate the virtual reality artifacts to create a comprehensive learning experience ([0081-0089] – a personalized learning experience and platform which includes simulation scenarios consisting of learning events that are mapped to specific skills and goals; contains an intuitive set of data visualizations which provide both SBME participants and instructors insight into system measures collected over time during training exposure such that they can track progress and seek additional training when necessary). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Tiwari et al the ability to incorporate simulation of real-world scenarios as taught by Pappada et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, Tiwari et al discloses the method of claim 1, wherein the collecting of the data further includes obtaining input from at least one of direct responses to questionnaires, feedback from peers or managers, social media profiles and interactions, or collaborative tools used by the employee ([0066] – feedback from expert, live simulations, questionnaires).
As per claim 3, Tiwari et al discloses the method of claim 1, wherein the machine learning algorithm utilizes at least one of a neural network, decision tree, support vector machine, or ensemble learning method to establish the interpersonal affinity-behavioral matrix ([0062] – decision tree).
As per claim 4, Tiwari et al discloses the method of claim 1, wherein the machine learning algorithm matches the current skill set with the predefined competency model for the organizational role to identify the skills gap ([0062, 0065, 0069] – skill gap identification and monitoring progress toward target skillset).
As per claim 5, Tiwari et al discloses the method of claim 1, wherein the personalized learning experience includes multimedia content comprising at least one of interactive simulations, video tutorials, written content, or quizzes ([0066, 0068] – case studies, questionnaires, and live simulations targeting required skills and AI-generated personalized learning path).
As per claim 7, Tiwari et al discloses the method of claim 1, further comprising leveraging generative AI to create a scenario-based learning task that mimics a real-world problem ([0066, 0068] – live simulations targeting required skills and AI-generated personalized learning path).
As per claim 8, Tiwari et al discloses the method of claim 1, wherein the personalized learning experience is adapted to an employment level of understanding categorized as foundational, developmental, or advanced within the predefined competency model for the organizational role ([0067-0068] – the candidate's overall competency score (e.g., 43%) for the selected skillset - the candidate’s learning path is tailor selected based on their current skill strengths (level of understanding). While Tiwari et al fails to explicitly disclose categorizing the level of understanding as foundational, developmental or advanced, however, these differences are only found in the non-functional descriptive material and are not functionally involved in the steps recited nor do they alter the recited structural elements. The recited method steps would be performed the same regardless of the specific data. Further, the structural elements remain the same regardless of the specific data. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP ' 2106.
As per claim 9, Tiwari et al discloses the method of claim 1, wherein the user interface includes a feedback mechanism enabling the employee to rate the personalized learning experience ([0065] – the system captures the experience of the candidate and that feedback is used to automatically modify/update the candidate’s training path and modules where appropriate).
As per claim 10, Tiwari et al discloses the method of claim 9, wherein the feedback is used to adjust subsequent learning content ([0065] – the system captures the experience of the candidate and that feedback is used to automatically modify/update the candidate’s training path and modules where appropriate).
Claims 6, 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al, US 2024/0330834 in view of Pappada et al, US 2023/0039882, and Rudden et al, US 2021/0264808.
As per claim 6, While Tiwari et al discloses the interpersonal affinity-behavioral matrix and considering a user’s upcoming schedule [0068], the combination of Tiwari et al and Pappada et al fails to disclose integrate the personalized learning experience with a digital calendar of the employee to schedule a learning session according to one or more optimal times identified by the user. Ruden et al discloses and analogous learning management system wherein the system is integrated with a calendar function to schedule learning content at an optimum time [0013, 0030, 0031, 0037, 0038]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Tiwari et al the ability to consider a user’s schedule as disclosed in Ruden et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 11, Tiwari et al discloses a computer system for enhancing employee skills and competencies, comprising: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors [0005-0007], causes the computer system to:
collect data pertaining to at least one of a current skill set, completed education or training, work experience, performance metrics, learning styles, or career aspirations of an employee ([0062] – collect data relating to skills, performance, experiences, etc);
analyze the data using a machine learning algorithm to establish an interpersonal affinity-behavioral matrix for the employee ([0062] – feature matrix used in conjunction with machine learning model to generate a personalized upskilling program tailed to each candidate);
identify at least one of a skills gap or a development opportunity relative to a predefined competency model for an organizational role ([0026]);
generate a personalized learning experience based on the skills gap or the development opportunity, where the personalized learning experience conforms to a learning style adapted to the employee based on the interpersonal affinity-behavioral matrix ([0026, 0072] – customized learning path based on skills gap;
provide a user interface through which the employee interacts with the personalized learning experience ([0065, 0068, 0073] – candidate accesses learning path to complete tasks for each module/skillset); and
dynamically updating the personalized learning experience based on performance data and feedback collected through the user interface ([0024, 0065, 0068, 0069, 0073] – learning path dynamically updated based on collected data)
modify presentation of learning content based on input from the employee, the performance metrics and preferences through the interpersonal affinity-behavioral matrix ([0024, 0068] - the system is dynamic and can update the learning path based on the mentor's feedback and/or changes in the candidate's proficiency (e.g., as identified through testing), experience, and/or education); and
refine the interpersonal affinity-behavioral matrix based on updated performance data to provide an accurate reflection of an evolving professional profile of the employee ([0004, 0024, 0068] - the framework can respond dynamically to real-time inputs, allowing changes in the candidate's abilities and skillset evaluation to modify the recommended learning path for that candidate.).
Tiwari et al fails to disclose, while Pappada et al discloses create virtual reality artifacts comprising digital environments simulating real-world scenarios relevant to the skills and competencies being developed, wherein the virtual reality artifacts are customized to mirror job conditions; and wherein the user interface includes virtual reality components that integrate the virtual reality artifacts to create a comprehensive learning experience ([0081-0089] – a personalized learning experience and platform which includes simulation scenarios consisting of learning events that are mapped to specific skills and goals; contains an intuitive set of data visualizations which provide both SBME participants and instructors insight into system measures collected over time during training exposure such that they can track progress and seek additional training when necessary). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Tiwari et al the ability to incorporate simulation of real-world scenarios as taught by Pappada et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
While Tiwari et al discloses the interpersonal affinity-behavioral matrix and considering a user’s upcoming schedule [0068], the combination of Tiwari et al and Pappada et al fails to disclose integrate the personalized learning experience with a digital calendar of the employee to schedule a learning session according to one or more optimal times identified by the user. Ruden et al discloses and analogous learning management system wherein the system is integrated with a calendar function to schedule learning content at an optimum time [0013, 0030, 0031, 0037, 0038]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Tiwari et al the ability to consider a user’s schedule as disclosed in Ruden et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 12, Tiwari et al discloses the system of claim 11, wherein the collecting of the data further includes obtaining input from at least one of direct responses to questionnaires, feedback from peers or managers, social media profiles and interactions, or collaborative tools used by the employee ([0066] – feedback from expert, live simulations, questionnaires).
As per claim 13, Tiwari et al discloses the system of claim 11, wherein the machine learning algorithm utilizes at least one of a neural network, decision tree, support vector machine, or ensemble learning method to establish the interpersonal affinity-behavioral matrix ([0062] – decision tree).
As per claim 14, Tiwari et al discloses the system of claim 11, wherein the machine learning algorithm matches the current skill set with the predefined competency model for the organizational role to identify the skills gap ([0062, 0065, 0069] – skill gap identification and monitoring progress toward target skillset).
As per claim 15, Tiwari et al discloses the system of claim 11, wherein the personalized learning experience includes multimedia content comprising at least one of interactive simulations, video tutorials, written content, or quizzes ([0066, 0068] – case studies, questionnaires, and live simulations targeting required skills and AI-generated personalized learning path).
As per claim 17, Tiwari et al discloses the system of claim 11, further comprising leveraging generative AI to create a scenario-based learning task that mimics a real-world problem ([0066, 0068] – live simulations targeting required skills and AI-generated personalized learning path).
As per claim 18, Tiwari et al discloses the system of claim 11, wherein the personalized learning experience is adapted to an employment level of understanding categorized as foundational, developmental, or advanced within the predefined competency model for the organizational role ([0067-0068] – the candidate's overall competency score (e.g., 43%) for the selected skillset - the candidate’s learning path is tailor selected based on their current skill strengths (level of understanding). While Tiwari et al fails to explicitly disclose categorizing the level of understanding as foundational, developmental or advanced, however, these differences are only found in the non-functional descriptive material and are not functionally involved in the steps recited nor do they alter the recited structural elements. The recited method steps would be performed the same regardless of the specific data. Further, the structural elements remain the same regardless of the specific data. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP ' 2106.
As per claim 19, Tiwari et al discloses the system of claim 11, wherein the user interface includes a feedback mechanism enabling the employee to rate the personalized learning experience ([0065] – the system captures the experience of the candidate and that feedback is used to automatically modify/update the candidate’s training path and modules where appropriate).
As per claim 20, Tiwari et al discloses the system of claim 11, wherein the feedback is used to adjust subsequent learning content ([0065] – the system captures the experience of the candidate and that feedback is used to automatically modify/update the candidate’s training path and modules where appropriate).
Conclusion
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JOHNNA LOFTIS
Primary Examiner
Art Unit 3625
/JOHNNA R LOFTIS/Primary Examiner, Art Unit 3625