DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Due to communications filed 10/2/25, the following is a first action, non-final office action. Claims 1-10 are pending in this application and are rejected as follows.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 10 are rejected under 35 U.S.C. § 112(b) as failing to particularly point out and distinctly claim the subject matter regarded as the invention.
Specifically, claim 1 recites that the generative AI system comprises "a knowledge-based large language model trained on federally accredited data, proprietary workforce datasets and partner insights." The terms "federally accredited data," "partner insights," and "knowledge-based" are unclear because the claim does not provide objective boundaries for their scope, and it is not reasonably certain what data or information is encompassed by these terms.
Additionally, the limitation "where the system integrates gamified workforce readiness programming and multi-channel stakeholder engagement" is indefinite because it merely states a desired result without reciting the structure or operations by which the recited components perform the integration. It is therefore unclear what specific functionality or interaction among the claimed components is required to satisfy this limitation.
Furthermore, the claim recites multiple components, including a customer relationship management system, learning management system, commerce system, volunteer and donation management system, workforce placement and career development module, custom membership card certificate system, and Azure virtual private server hosting infrastructure, but fails to define how these components are interconnected or cooperate to perform the claimed invention. Consequently, the metes and bounds of the claim are not reasonably certain.
With regard to claim 10, the terms "gamified SaaS module," "WaaS module," "knowledge-base LLM," "accredited and proprietary data sources," "real-world workforce readiness outcomes," and "multi-stakeholder dashboards" lack objective boundaries, rendering the scope of the claim uncertain. Additionally, the limitations "linking gamified milestones to real-world workforce readiness outcomes" and "generating predictive analytics for workforce pipeline management" are recited in purely functional, result-oriented terms without specifying the operations by which the results are achieved. Accordingly, one of ordinary skill in the art would not be reasonably apprised of the metes and bounds of the claimed invention, and the claim is therefore indefinite under 35 U.S.C. § 112(b).
Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title,
Claims 1-10 are rejected under 35 U.S.C, 101 because the claimed invention is directed to a judicial exception (l.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 1 recites a judicial exception. Under step 2A, Prong One, the claim 1 recites the abstract idea of organizing and managing career exploration, workforce development, workforce placement, stakeholder engagement, commerce, and volunteer activities, which falls into the “Certain Methods of Organizing Human Activity”. Thus, the claim recites an abstract idea.
With regard to Step 2A, Prong Two, claim 1 does not integrate the judicial exception into a practical application because the additional elements, including a customer relationship management system, learning management system, commerce system volunteer and donation management system, generative AI system comprising a large language model, custom membership card certificate system, workforce placement and career development module, and Azure virtual private server hosting infrastructure are recited at a high level of generality and perform their well-understood, routine and conventional functions, without improving the functioning of a computer or another technology, nor do they integrate the abstract idea into a practical application. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea.
Finally, with respect to Step 2B, claim 1 does not recite an inventive concept. The additional elements, considered individually and as an ordered combination amount to not more than well-understood, routine and conventional computer activities and therefore do not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Dependent claims 2-9 are also directed to same grouping of “Certain Methods of Organizing Human Activity”. The additional elements of the system of claims 2-9; SaaS and WaaS models of claim 5; and dashboards of claim 6 are additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With regard to independent claim 10, Step 2A, Prong One, claim 10 is directed to the abstract idea of organizing and managing workforce readiness, career development, mentor matching, job placement, and workforce pipeline management, which falls into the “certain method of organizing human activity” group. Thus, the claim recites an abstract idea.
Step 2A, Prong Two, the additional elements, including the gamified SaaS module, WaaS module, modular AI assistant comprising a knowledge-based large language model, predictive analytics, and dashboards, are recited at a high level of generality and merely implement the abstract idea using generic computer technology. Claim 10 does not recite an improvement to computer functionality or other technology, nor does it integrate the abstract idea into a practical application. Accordingly, when considered individually and as an ordered combination, the additional elements do not amount to significantly more than the judicial exception itself. The claim is ineligible.
Finally, with respect to Step 2B, claim 10 does not recite an inventive concept. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea, and the claim is therefore ineligible under 35 U.S.C. § 101.
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) 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20230196253 A1), and further in view of Qazvinian et al (US 20250013963 A1), and further in view of Ding (US 11961098 B2), and further in view of Robinson (US 20230394609 A1).
As per claim 1, Roy discloses:
A cloud-based online platform system for career exploration and workforce development, (Roy (US 20230196253 A1) ([0001] The present invention generally relates to a cloud-based, online training system. More specifically, the present invention relates to an innovated system and method for learning and working suited for any job seeker, student, new-hire employee onboarding, new skill training, team member project performance monitoring, etc.);
comprising,
a learning management system, (Roy: The gamified simulation platform of the present invention provides a just-in-time learning experience and enables the user to gain a high-level proficiency by learning and simultaneously applying the skills gained on an assigned project. To accomplish this, the method of the present invention provides a plurality of user accounts managed by at least one remote server);
a workforce placement and career development module, (Abstract: The method facilitates the entire process using actual/simulated projects and incorporates artificial intelligence technologies in various algorithms for generating performance scores for each task and matching job openings for the user);
and an Azure virtual private server hosting infrastructure, (obvious with: [0001] The present invention generally relates to a cloud-based, online training system. More specifically, the present invention relates to an innovated system and method for learning and working suited for any job seeker, student, new-hire employee onboarding, new skill training, team member project performance monitoring, etc. [since Azure is generic cloud implementation]).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the above limitations as taught by Roy, 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.
wherein the system integrates gamified workforce readiness programming, ([0034] Using the method, the user can learn, practice, and work by completing a number of typical industry projects, each increasing in complexity with the given deadlines and deliverables. Upon completing the learn/practice/work program, the user can obtain performance ranking scores for the completed projects, which can provide recruiters and clients with far more valuable information about the user’s capabilities than typical certifications can).
Roy does not disclose the following limitations, however, Qazvinian et al (US 20250013963 A1) discloses:
a customer relationship management system, (Qazvinian et al: [0089] In various embodiments, the system may include various additional data sources or systems specific to the organization's needs, such as, e.g., performance management systems, employee feedback platforms, or customer relationship management (hereinafter “CRM”) systems);
a generative AI system, (Qazvinian et al: Abstract The systems and methods described herein provide intelligent people analytics from generative artificial intelligence. In one embodiment, the system: receives a prompt related to people analytics from a client device associated with a user; generates an embedding representation of the received prompt using a generative AI system including one or more generative AI models);
comprising a knowledge-based large language model, trained on...proprietary workforce datasets, and partner insights, (Qazvinian et al: [0121] FIG. 4 is a diagram illustrating an example embodiment of a portion of a user interface for a generative AI session that enables a chat-like conversational experience. The illustration demonstrates an embodiment wherein the user is presented with a text input field whereby the user may enter textual input and submit it as a prompt to the generative AI system. Below this field, a prompt and a response are displayed, which can represent, for example, a previous question and a previous answer within the same open generative AI session. This previous conversation can be exposed and displayed to the user in the UI so that the user is able to have visibility into what they have already submitted and what the user may have conversational context for. Below this question and answer, a number of prompts are additionally displayed in the illustration. Such prompts may be example prompts, such as follow-up questions to a previously asked question, that the generative AI system can suggest to the user. The user may optionally select one of these prompts to be submitted as the next prompt to be submitted. Thus, in some embodiments, the previous conversation and one or more suggested prompts can be displayed for the user during the generative AI session. Additionally, the user may select the “New Chat” UI element in order to generate a new chat session with no previous conversation history or conversational context. In some embodiments, using this chat-like conversational UI and user experience, the user may potentially interact with a wide range of different systems, backends, databases, and more; [0122] FIG. 5 is a diagram illustrating an example embodiment of a portion of a user interface for a generative AI session that enables categorizing of different sets of prompts. In the illustrated example, a text input field is displayed to the user in the UI, along with a button to submit text input, similar to the illustration in FIG. 4. Below this text input field, a number of categories are displayed to the user. In some embodiments, the system can categorize different sets of prompts as categories, such as, e.g., prompts related to workforce planning, retention, diversity, equity, and inclusion (“DEI”), and more. The system is then configured to update the categories with user input as the user inputs more prompts which fall under one or more categories. In some embodiments, users may be able to use the categories as a guide to quickly submit prompts they want to receive responses to);
and multi-channel stakeholder engagement, (Qazvinian et al: [0100] In some embodiments, a separate generative AI model from the other generative AI models used herein is used for generating the response output. In some embodiments, the generative AI model performs a classification to determine whether the response output should be a particular form of visual data or textual input. For example, the system may determine that the response output should be, e.g., a pie chart, a bar chart, a paragraph, a number, or a percentage. The system can perform this classification, and based on this classification, the system can then generate the response output. For example, if the system classifies that the type of prompt is related to asking for pay distribution by gender, then the system may generate the response output as a pie chart. Conversely, if the system classifies that the type of prompt is asking for a number, such as the prompt, “how many employees are there?”, then the system may generate the response output as, e.g., a number, a sentence with a number included within, or a response that includes visual data such as a chart or graph with the numerical data represented. For example, one response to the prompt above may be, “Currently, your organization includes 123 employees. Please note that this figure only includes employees according to your permissions”, while another response may be simply the number 123).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the limitations as taught by Qazvinian et al in the systems of Roy, 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.
Roy does not disclose the following limitations, however, Ding (US 11961098 B2) discloses:
an e-commerce system, (Ding (US 11961098 B2): (30) In one embodiment, application tier 320 includes a user’s application 321, an accounts application 322, a works application 324, a volunteers application 326, a donations application 328, and other applications 329. Users application 321 implements the functions of user management including user registration, login, subscription, profile maintaining, user search and sort, socialization, privacy setting, etc. Accounts application 322 implements the functions of rewarding accounts maintenance and management comprising initializing accounts, logging transactions and updating balances and other information, verifying, transferring and distributing rewarding points to accounts via a rewarding module 323. Works application 324 implements the functions of philanthropic work management comprising work submission, display, search, share, comment, reward, and determining rewarding values for philanthropic works via an evaluation module 325. );
a volunteer and donation management system, (Ding (US 11961098 B2): (30) Volunteers application 326 implements the functions of philanthropic work requests management comprising processing requests, responses, previews, cancels, shares, comments, searches, matches, communications and slot scheduling and coordination via a scheduling module 327. Donations application 328 implements functions of money donation management including giving cart, online payment processing, transaction logging, account verification, receipts generation, donation history for donor/donee, donee search, other documentation, etc. Other applications 329 implements other functions and common utilities for system operation and optimization. In alternative embodiments, some of the functions may be implemented in other applications, modules, components or combinations);
a custom membership card/certificate system, (Ding: (40) FIG. 10 depicts an interface that enables a user to search and sort users based on summary information of their rewarding accounts, according to an exemplary embodiment... For example, some other embodiments may display the specific ranking number of a user, or mark a user with tier or level labels or badges).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the limitations as taught by Ding in the systems of Roy, 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.
Roy does not disclose the following limitation , however, Robinson et al (US 20230394609 A1) discloses:
trained on federally accredited data, (Discloses “ONET”: “[0015] The skills graph can be precomputed, cached, or generated “on-the-fly.” For example, the host platform may dynamically build the skills graph in response to a search request or job prediction request from the user. In this case, the user may submit a job type such as a job that they currently perform. The host platform assumes or may verify that the user has the requisite skills for such a job. In response, the host platform may identify a predefined list of skills assigned to the job type. The skills may be defined in advance and may be based on external data sources, etc. In some cases, the skills may be identified based on predefined skill codes defined by O*NET, however embodiments are not limited thereto. [0024] 2. The host platform compiles the user's work history and queries internal and/or external data sources for the skills that are commonly associated with those jobs (e.g., which are mapped to O*NET job codes, mapped to other relevant identifiers by employment data providers, associated with jobs via various government entities, etc.), based on user submitted/third-party/market level data. [0025] 3. Where applicable, the host platform may perform a validation to verify that those skills are held by the user. This validation can be achieved in several ways, including but not limited to the following: [0026] a. Surveys to determine the user's relevant experience. [0027] i. Short form questions or prompts, e.g., “Did you perform these skills at this job?” [0028] ii. Long form questions or prompts, e.g., “Describe the work that you performed that would highlight this particular skill.” [0029] b. Skills tests. [0030] i. Internal and/or external tests designed to assess the user's skills. [0031] ii. A summary of the user's responses and whether or not the user answered correctly may be included in the skills certification.
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the limitations as taught by Robinson et al in the systems of Roy, 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, Roy discloses:
wherein the learning management system is structured around a plurality of pillars, wherein each pillar combines gamified modules and activities configured to award points and milestones tied to workforce readiness outcomes, including certifications, internships, and employer-aligned benchmarks, ([0034] Using the method, the user can learn, practice, and work by completing a number of typical industry projects, each increasing in complexity with the given deadlines and deliverables. Upon completing the learn/practice/work program, the user can obtain performance ranking scores for the completed projects, which can provide recruiters and clients with far more valuable information about the user’s capabilities than typical certifications can).
As per claim 3, Roy discloses:
wherein users earn points, badges, and incentives based on completion of program activities, wherein said gamified incentives are directly linked to real-world career readiness milestones and workforce pathway progress, ([0034] Using the method, the user can learn, practice, and work by completing a number of typical industry projects, each increasing in complexity with the given deadlines and deliverables. Upon completing the learn/practice/work program, the user can obtain performance ranking scores for the completed projects, which can provide recruiters and clients with far more valuable information about the user’s capabilities than typical certifications can).
As per claim 4, Roy discloses:
wherein the generative AI system personalizes learning, provides personalized guidance, ([0099] In some embodiments, the system can personalize the response output based on user preferences and profiles. In some embodiments, it takes into account the user's historical interactions, feedback, and/or preferences to tailor the response format. For example, if a user has indicated a preference for visual representations, the system may prioritize generating response outputs that include charts or infographics);
mentor-matching signals, ([0043] As can be can in FIG. 12, in another embodiment, the method provides a mentor to the specific user during practice in the practice module, wherein the mentor offers help and evaluation of project work to the specific user when needed.);
curriculum alignment, (Abstract: The method facilitates the entire process using actual/simulated projects and incorporates artificial intelligence technologies in various algorithms for generating performance scores for each task and matching job openings for the user);
Roy does not disclose the following, however, Qazvinian et al discloses:
and predictive workforce analytics, (Qazvinian et al: [0100] In some embodiments, a separate generative AI model from the other generative AI models used herein is used for generating the response output. In some embodiments, the generative AI model performs a classification to determine whether the response output should be a particular form of visual data or textual input. For example, the system may determine that the response output should be, e.g., a pie chart, a bar chart, a paragraph, a number, or a percentage. The system can perform this classification, and based on this classification, the system can then generate the response output. For example, if the system classifies that the type of prompt is related to asking for pay distribution by gender, then the system may generate the response output as a pie chart. Conversely, if the system classifies that the type of prompt is asking for a number, such as the prompt, “how many employees are there?”, then the system may generate the response output as, e.g., a number, a sentence with a number included within, or a response that includes visual data such as a chart or graph with the numerical data represented. For example, one response to the prompt above may be, “Currently, your organization includes 123 employees. Please note that this figure only includes employees according to your permissions”, while another response may be simply the number 123).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the limitations as taught by Qazvinian et al in the systems of Roy, 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 5, Roy discloses:
further comprising a dual architecture including:
a. Software-as-a-Service (SaaS) module for learners configured to deliver assessments, guided pathways, mentorship, and gamified career progression, ([0034] As can be seen in FIG. 1 to FIG. 24, the present invention provides a cloud-based, online system and method designed to bridge the growing skills gap and enable companies to quickly scale their workforce to match demand, based on project requirements. The online system and method, game based training and work simulation platform (WSP) of the present invention, called WSP hereafter, provides a simulated on-the-job environment to offer users (e.g., students, employees, job-seekers, etc.) the next best thing to actually working on client projects. [Where the Cloud-based online platform that is accessed by students, employees, job-seekers, etc., represents the SAAS TEACHING]. In this way a user can improve their skills, project strategy, time efficiency, and performance so that the user can work confidently and efficiently on various projects, including, but not limited to, software projects. Using the WSP system and method, the user can learn, practice, and work by completing a number of typical industry projects, each increasing in complexity with the given deadlines and deliverables. On completing the learn/practice/work program, the user can obtain performance ranking scores for each project, which can provide recruiters and clients with far more valuable information about users’ capabilities than typical certifications can; [0038] With performance scores the method generates based on the outcome of the user’s actual learn/practice/work tasks, the method relays a performance ranking to the corresponding PC device of the specific user, wherein the performance ranking is generated using all performance scores for each task completed in each module (Step F). The performance scores the method generates for each task may include, but are not limited to, time score related to the total time to finish a task, quality score based on the evaluation of the project quality of the task, etc.; [0041] As can be seen in FIG. 7 and FIG. 18 to FIG. 20, the method of the present invention provides a sub-process for learn/practice/work training, the three-step program. More specifically, the method prompts the corresponding PC device of the specific user to start skills training in the learn module for each of the at least one phase of the project in Step E.; [0043] As can be can in FIG. 12, in another embodiment, the method provides a mentor to the specific user during practice in the practice module, wherein the mentor offers help and evaluation of project work to the specific user when needed.); and
b. Workforce-as-a-Service (WaaSᵀ module for employers, educators, and partners configured to deliver job and internship boards, workforce analytics, and pipeline management; wherein data flows bidirectionally between the SaaS and WaaSTM modules, ([0003] According to research, 20% of learning happens in the classroom, but almost 80% occurs on the job, while working on projects. Learning through trial and error, however, takes a long time and is an expensive way to acquire necessary knowledge and skills for a specific job. The education environment can include various parties, such as students or learners, teachers, tutors, recruiters, and the human resource (HR) department, who may maintain transactional and functional relationships of some form with one another [WaaS ™ FUNCTIONALITY TEACHING]; [0007] The present invention comprises a unique and innovative game-based training and work simulation platform (WSP) that is designed to provide a unified system and method for efficiently and effectively closing the skill gap between users and potential employers, new-hires and managers, employees and supervisors, etc. The training and work simulation method of the present invention offers businesses a powerful cloud-based, online platform to quickly scale their workforce to match demand, based on project requirements [WaaS ™ FUNCTIONALITY TEACHING]. The method provides a simulated on-the-job environment to offer users (e.g., students, employees, job-seekers, etc.) the next best thing to actually working on client projects while conducting learning and training. In this way, a user can effectively improve their skills, project strategy and efficiency, and performance, thus equipping the user with significantly improved proficiency and confidence on various projects in the field of profession. Using the method, the user can learn, practice, and work by completing a number of typical industry projects, each increasing in complexity with the given deadlines and deliverables, [PIPELINE ANALYTICS TEACHING] Upon completing the learn/practice/work program, the user can obtain performance ranking scores for the completed projects, which can provide recruiters and clients with far more valuable information about the user’s capabilities than typical certifications can, [DASHBOARD TEACHING]. The method incorporates artificial intelligence technologies in various algorithms for generating performance scores for each task, matching job openings [JOB POSTINGS TEACHING] for the user, etc.)
As per claim 6, Roy discloses:
wherein the system fosters community engagement through interactive communication tools, role-specific dashboards, and mentor-mentee collaboration features accessible to learners, mentors, educators, and employers, ([0007] The present invention comprises a unique and innovative game-based training and work simulation platform (WSP) that is designed to provide a unified system and method for efficiently and effectively closing the skill gap between users and potential employers, new-hires and managers, employees and supervisors, etc. The training and work simulation method of the present invention offers businesses a powerful cloud-based, online platform to quickly scale their workforce to match demand, based on project requirements...Upon completing the learn/practice/work program, the user can obtain performance ranking scores for the completed projects, which can provide recruiters and clients with far more valuable information about the user’s capabilities than typical certifications can, [DASHBOARD TEACHING] [0043] As can be can in FIG. 12, in another embodiment, the method provides a mentor to the specific user during practice in the practice module, wherein the mentor offers help and evaluation of project work to the specific user when needed.[MENTOR TEACHING]);
For further support, Qazvinian et al discloses:
([0025] Internal data sources 120A-120N are data sources relating to data and information kept within an organization and generally not shared outside of the organization. In some embodiments, data sources and their associated feeds can fall under a number of categories. In some embodiments, such categories can include, e.g., human resources (hereinafter “HR”), Communications, Compensation, Calendar and Task Tracking. In some embodiments, HR feeds capture employee history within the company e.g. job title change, cash compensation change, manager and location change, project history, and other associated HR data. In some embodiments, communications feeds capture communications metadata relating to which entities are communicating with whom, how often the communication occurs, and/or what the communication entails. In some embodiments, communications metadata can include, e.g., memberships in mailing lists, metadata relating to instant messaging and other conversational platforms and channels, as well as frequency and time of communication between entities. In some embodiments, communications feeds neither capture the subject line nor the content of emails or instant messages. In some embodiments, Calendar feeds capture metadata related to scheduled events and meetings, e.g., group meetings and manager one-on-one meetings. This feed helps the system understand latent networks (friends and collaborations) within the organization. In some embodiments, the system may track equity compensation through Compensation feeds. In some embodiments, task tracking feeds may collect and analyze work activity of entities by connecting to task tracking tools. In some embodiments, internal data sources may be related to one or more e-learning systems).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the limitations as taught by Qazvinian et al in the systems of Roy, 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 7, Roy does not disclose the following, however, Qazvinian et al discloses:
wherein the workforce placement module includes integration with employer and partner systems to support job placement and career pipeline development, including predictive matching of learners to
employer needs using AI-driven workforce analytics, (Qazvinian et al: [0025] Internal data sources 120A-120N are data sources relating to data and information kept within an organization and generally not shared outside of the organization. In some embodiments, data sources and their associated feeds can fall under a number of categories. In some embodiments, such categories can include, e.g., human resources (hereinafter “HR”), Communications, Compensation, Calendar and Task Tracking. In some embodiments, HR feeds capture employee history within the company e.g. job title change, cash compensation change, manager and location change, project history, and other associated HR data. In some embodiments, communications feeds capture communications metadata relating to which entities are communicating with whom, how often the communication occurs, and/or what the communication entails. In some embodiments, communications metadata can include, e.g., memberships in mailing lists, metadata relating to instant messaging and other conversational platforms and channels, as well as frequency and time of communication between entities. In some embodiments, communications feeds neither capture the subject line nor the content of emails or instant messages. In some embodiments, Calendar feeds capture metadata related to scheduled events and meetings, e.g., group meetings and manager one-on-one meetings. This feed helps the system understand latent networks (friends and collaborations) within the organization. In some embodiments, the system may track equity compensation through Compensation feeds. In some embodiments, task tracking feeds may collect and analyze work activity of entities by connecting to task tracking tools. In some embodiments, internal data sources may be related to one or more e-learning systems).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the limitations as taught by Qazvinian et al in the systems of Roy, 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 8, Roy discloses:
wherein the gamification system incorporates interactive activities, leaderboards, and achievement milestones, wherein GT3S AI adapts challenges and milestone progression based on learner performance and workforce readiness signals, ([0034] Using the method, the user can learn, practice, and work by completing a number of typical industry projects, each increasing in complexity with the given deadlines and deliverables. Upon completing the learn/practice/work program, the user can obtain performance ranking scores for the completed projects, which can provide recruiters and clients with far more valuable information about the user’s capabilities than typical certifications can).
As per claim 9, Roy discloses:
wherein data captured from user interactions is processed to generate performance insights, multi-stakeholder dashboards that provide learners with progress reports, mentors with mentee readiness insights, ([0039] As can be seen in FIG. 4, in an alternative embodiment of the present invention, the method provides a swarm artificial intelligence (AI) algorithm for determining the quality score using the outcome of the task in each module. This algorithm queries a unified collective swarm of human reviewers and/or distributed systems to evaluate the quality of the specific user’s project work, wherein the distributed systems comprise non-human sources for project quality data. Subsequently, the method generates the quality performance score from the swarm evaluations resulting in a converged performance score through the swarm AI algorithm. The non-human sources may include, but are not limited to, internal and external databases, data tables, reports, etc.);
educators with curriculum alignment cues, (Abstract: The method facilitates the entire process using actual/simulated projects and incorporates artificial intelligence technologies in various algorithms for generating performance scores for each task and matching job openings for the user);
and employers with predictive pipeline analytics, ([0034] Using the method, the user can learn, practice, and work by completing a number of typical industry projects, each increasing in complexity with the given deadlines and deliverables. Upon completing the learn/practice/work program, the user can obtain performance ranking scores for the completed projects, which can provide recruiters and clients with far more valuable information about the user’s capabilities than typical certifications can).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20230196253 A1), and further in view of Qazvinian et al (US 20250013963 A1).
As per claim 10, Roy discloses:
A method of delivering workforce readiness and pipeline development, ([0001] The present invention generally relates to a cloud-based, online training system. More specifically, the present invention relates to an innovated system and method for learning and working suited for any job seeker, student, new-hire employee onboarding, new skill training, team member project performance monitoring, etc.);
comprising:
providing learners with access to a gamified SaaS module delivering assessments skills training, mentor matching, and career pathways, ([0034] As can be seen in FIG. 1 to FIG. 24, the present invention provides a cloud-based, online system and method designed to bridge the growing skills gap and enable companies to quickly scale their workforce to match demand, based on project requirements. The online system and method, game based training and work simulation platform (WSP) of the present invention, called WSP hereafter, provides a simulated on-the-job environment to offer users (e.g., students, employees, job-seekers, etc.) the next best thing to actually working on client projects. In this way a user can improve their skills, project strategy, time efficiency, and performance so that the user can work confidently and efficiently on various projects, including, but not limited to, software projects. Using the WSP system and method, the user can learn, practice, and work by completing a number of typical industry projects, each increasing in complexity with the given deadlines and deliverables. On completing the learn/practice/work program, the user can obtain performance ranking scores for each project, which can provide recruiters and clients with far more valuable information about users’ capabilities than typical certifications can; [0038] With performance scores the method generates based on the outcome of the user’s actual learn/practice/work tasks, the method relays a performance ranking to the corresponding PC device of the specific user, wherein the performance ranking is generated using all performance scores for each task completed in each module (Step F). The performance scores the method generates for each task may include, but are not limited to, time score related to the total time to finish a task, quality score based on the evaluation of the project quality of the task, etc.; [0041] As can be seen in FIG. 7 and FIG. 18 to FIG. 20, the method of the present invention provides a sub-process for learn/practice/work training, the three-step program. More specifically, the method prompts the corresponding PC device of the specific user to start skills training in the learn module for each of the at least one phase of the project in Step E.; [0043] As can be can in FIG. 12, in another embodiment, the method provides a mentor to the specific user during practice in the practice module, wherein the mentor offers help and evaluation of project work to the specific user when needed.);
providing employers, educators, and partners with access to a WaaS™ module delivering dashboards, pipeline analytics, and job postings, ([0003] According to research, 20% of learning happens in the classroom, but almost 80% occurs on the job, while working on projects. Learning through trial and error, however, takes a long time and is an expensive way to acquire necessary knowledge and skills for a specific job. The education environment can include various parties, such as students or learners, teachers, tutors, recruiters, and the human resource (HR) department, who may maintain transactional and functional relationships of some form with one another [WaaS ™ FUNCTIONALITY TEACHING]; [0007] The present invention comprises a unique and innovative game-based training and work simulation platform (WSP) that is designed to provide a unified system and method for efficiently and effectively closing the skill gap between users and potential employers, new-hires and managers, employees and supervisors, etc. The training and work simulation method of the present invention offers businesses a powerful cloud-based, online platform to quickly scale their workforce to match demand, based on project requirements [WaaS ™ FUNCTIONALITY TEACHING]. The method provides a simulated on-the-job environment to offer users (e.g., students, employees, job-seekers, etc.) the next best thing to actually working on client projects while conducting learning and training. In this way, a user can effectively improve their skills, project strategy and efficiency, and performance, thus equipping the user with significantly improved proficiency and confidence on various projects in the field of profession. Using the method, the user can learn, practice, and work by completing a number of typical industry projects, each increasing in complexity with the given deadlines and deliverables, [PIPELINE ANALYTICS TEACHING] Upon completing the learn/practice/work program, the user can obtain performance ranking scores for the completed projects, which can provide recruiters and clients with far more valuable information about the user’s capabilities than typical certifications can, [DASHBOARD TEACHING]. The method incorporates artificial intelligence technologies in various algorithms for generating performance scores for each task, matching job openings [JOB POSTINGS TEACHING] for the user, etc.) ;
linking gamified milestones to real-world workforce readiness outcomes, ([0034] Using the method, the user can learn, practice, and work by completing a number of typical industry projects, each increasing in complexity with the given deadlines and deliverables. Upon completing the learn/practice/work program, the user can obtain performance ranking scores for the completed projects, which can provide recruiters and clients with far more valuable information about the user’s capabilities than typical certifications can).
Roy does not disclose the following limitations, however, Qazvinian et al discloses:
integrating a modular AI assistant (Qazvinian et al: Abstract The systems and methods described herein provide intelligent people analytics from generative artificial intelligence. In one embodiment, the system: receives a prompt related to people analytics from a client device associated with a user; generates an embedding representation of the received prompt using a generative AI system including one or more generative AI models);
configured as a knowledge-based LLM trained on accredited and proprietary data sources, (Qazvinian et al: [0121] FIG. 4 is a diagram illustrating an example embodiment of a portion of a user interface for a generative AI session that enables a chat-like conversational experience. The illustration demonstrates an embodiment wherein the user is presented with a text input field whereby the user may enter textual input and submit it as a prompt to the generative AI system. Below this field, a prompt and a response are displayed, which can represent, for example, a previous question and a previous answer within the same open generative AI session. This previous conversation can be exposed and displayed to the user in the UI so that the user is able to have visibility into what they have already submitted and what the user may have conversational context for. Below this question and answer, a number of prompts are additionally displayed in the illustration. Such prompts may be example prompts, such as follow-up questions to a previously asked question, that the generative AI system can suggest to the user. The user may optionally select one of these prompts to be submitted as the next prompt to be submitted. Thus, in some embodiments, the previous conversation and one or more suggested prompts can be displayed for the user during the generative AI session. Additionally, the user may select the “New Chat” UI element in order to generate a new chat session with no previous conversation history or conversational context. In some embodiments, using this chat-like conversational UI and user experience, the user may potentially interact with a wide range of different systems, backends, databases, and more; [0122] FIG. 5 is a diagram illustrating an example embodiment of a portion of a user interface for a generative AI session that enables categorizing of different sets of prompts. In the illustrated example, a text input field is displayed to the user in the UI, along with a button to submit text input, similar to the illustration in FIG. 4. Below this text input field, a number of categories are displayed to the user. In some embodiments, the system can categorize different sets of prompts as categories, such as, e.g., prompts related to workforce planning, retention, diversity, equity, and inclusion (“DEI”), and more. The system is then configured to update the categories with user input as the user inputs more prompts which fall under one or more categories. In some embodiments, users may be able to use the categories as a guide to quickly submit prompts they want to receive responses to);
generating predictive analytics for workforce pipeline management across multi-stakeholder dashboards, ([0100] In some embodiments, a separate generative AI model from the other generative AI models used herein is used for generating the response output. In some embodiments, the generative AI model performs a classification to determine whether the response output should be a particular form of visual data or textual input. For example, the system may determine that the response output should be, e.g., a pie chart, a bar chart, a paragraph, a number, or a percentage. The system can perform this classification, and based on this classification, the system can then generate the response output. For example, if the system classifies that the type of prompt is related to asking for pay distribution by gender, then the system may generate the response output as a pie chart. Conversely, if the system classifies that the type of prompt is asking for a number, such as the prompt, “how many employees are there?”, then the system may generate the response output as, e.g., a number, a sentence with a number included within, or a response that includes visual data such as a chart or graph with the numerical data represented. For example, one response to the prompt above may be, “Currently, your organization includes 123 employees. Please note that this figure only includes employees according to your permissions”, while another response may be simply the number 123).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the limitations as taught by Qazvinian et al in the systems of Roy, 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.
Prior Art Not Cited
The following art has been considered relevant by the Examiner, however has not been used in the present Office Action:
Kapcar et al (US 12112126 B2); Hatfield (US 20230145363 A1); Mukherjee (US 20240354436 A1); CLARKE et al (WO 2018053444 A1); SUBHASH (WO 2026042089 A1)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Akiba Robinson whose telephone number is 571-272-6734 and email is Akiba.Robinsonboyce@USPTO.gov. The examiner can normally be reached on Monday-Thursday 6:30am-4:30pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor, Nathan Uber can be reached on 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to the receptionist whose telephone number is (703) 305-3900.
August 6, 2026
/AKIBA K ROBINSON/Primary Examiner, Art Unit 3626
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