DETAILED ACTION
Status of the Application
Claims 1-12 have been examined in this application. This communication is the first action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97.
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 .
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.
This action is a Non-Final Action on the merits in response to the application filed on 06/09/2025.
Claims 1-12 remain pending in this application.
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 1-12 are directed towards a method of which is among the statutory categories of invention.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-12, the independent claim (claim 1) are directed to managing applicant data, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, A computer implemented method for matching educational institution applicant experience to institution coursework, the method comprising:
receiving from the applicant a plurality of documents including data reflecting an applicant's previous experience;
using a matching module to match competencies in the applicant data record with competencies stored in a course model, wherein the course model contains course data records each associating a course with both competencies learned in the course and competencies required to take the course.
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial interaction such as business relations; managing personal behavior such as social activities and following rules or instructions (See MPEP 2106.04(a)(2) II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction; managing personal behavior, then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of module, computer user interface, . The claims recite the steps are performed by the module, computer user interface.
The limitations of
formatting the applicant data with a formatting module to generate a single applicant data file reflecting the applicant data in the plurality of documents;
extracting from the applicant data file an applicant data record listing applicant competencies with a competency analysis module;
generating a matching output report with an output report module, the output report causing a computer user interface to display courses for which the applicant has the course competency and courses available for the applicant to take..
are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by module, computer user interface. The module, computer user interface are recited at a high level of generality. In limitation (a), the module, computer user interface are used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The module, computer user interface are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the module, computer user interface. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
formatting the applicant data with a formatting module to generate a single applicant data file reflecting the applicant data in the plurality of documents;
extracting from the applicant data file an applicant data record listing applicant competencies with a competency analysis module;
generating a matching output report with an output report module, the output report causing a computer user interface to display courses for which the applicant has the course competency and courses available for the applicant to take.
are recited at a high level of generality. These elements amount to transmitting and processing data are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a module, computer user interface to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-12 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claim 2 recites computer device; claims 3, 4, 7, 8, 10 recites modules. The dependent claims 2-12 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-12 recites module, computer user interface which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-12 recites module, computer user interface, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-12 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claim 1. Therefore claims 2-12 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20210133247, Chen, et al. to hereinafter Chen in view of United States Patent Publication US 20250013965A, Bowden.
Referring to Claim 1, Chen teaches a computer implemented method for matching educational institution applicant experience to institution coursework, the method comprising:
receiving from the applicant a plurality of documents including data reflecting an applicant's previous experience (
Bowden teaches accessing an HR platform containing “skills required for those roles, as well as information on skills currently held by a candidate.” See paragraph [0033]. Bowden also teaches personalized upskilling based on “current knowledge and previous roles.” See paragraph [0018].);
formatting the applicant data with a formatting module to generate a single applicant data file reflecting the applicant data in the plurality of documents (
Bowden teaches generating “a list of needed skills, known capabilities” and providing the information to an LLM using a prompt structure. See paragraph [0033]. Bowden’s claims also recite receiving “a list of required skills and known capabilities.”);
extracting from the applicant data file an applicant data record listing applicant competencies with a competency analysis module (
Bowden teaches that an LLM generates training materials by “first identifying the required skills and the user’s existing knowledge and skills.” See paragraph [0032].);
using a matching module to match competencies in the applicant data record with competencies stored in a course model, wherein the course model contains course data records each associating a course with both competencies learned in the course and competencies required to take the course (
Chen teaches a skills mapping service, graph service, skill query service, and recommendation service. The recommendation service traverses skills graphs, identifies courses tagged with a queried skill, and provides an ordered course list. Chen teaches: “a skills mapping service,” “a graph service,” “a skill query service,” and “a recommendation service configured to travers[e] the skills graphs.” See paragraphs [0007–0008], [0024–0029], [0039–0049].
Chen disclose courses for which a learner is prepared or lacks prerequisites and teaches that a learner can “filter out content courses where they lack the prerequisite skills,” search for alternative courses, and use diagnostic quizzes to determine readiness to take a course. See paragraph [0091].
Chen disclose matching learner skills with coursework. Chen teaches using “learner information such as preferences, interests, and accomplished skills” with an online-course skills map to provide “course recommendations to a learner.” See paragraph [0023].
Chen teaches: “The skills can include a set of prerequisite skills” and “taught skills or skills that can be learned … when taking the online course.” See paragraph [0022]. Chen further teaches that an instructor may tag a course with skills taught by the course and skills that are “prerequisites for the taking of, and successful completion of, the online course.” See paragraph [0027].);
generating a matching output report with an output report module, the output report causing a computer user interface to display courses for which the applicant has the course competency and courses available for the applicant to take (
Chen teaches output report and graphical user interface; a query UI in which a learner enters a skill and “one or more courses that teach the skill … is displayed in an available courses menu area.” See paragraph [0074]. Chen also teaches returning an ordered skills list and ordered course list through a query UI. See paragraphs [0029], [0069].).
Chen does not explicitly teach receiving from the applicant a plurality of documents including data reflecting an applicant's previous experience; formatting the applicant data with a formatting module to generate a single applicant data file reflecting the applicant data in the plurality of documents; extracting from the applicant data file an applicant data record listing applicant competencies with a competency analysis module
However, Bowden teaches these limitations
receiving from the applicant a plurality of documents including data reflecting an applicant's previous experience (
Bowden teaches accessing an HR platform containing “skills required for those roles, as well as information on skills currently held by a candidate.” See paragraph [0033]. Bowden also teaches personalized upskilling based on “current knowledge and previous roles.” See paragraph [0018].);
formatting the applicant data with a formatting module to generate a single applicant data file reflecting the applicant data in the plurality of documents (
Bowden teaches generating “a list of needed skills, known capabilities” and providing the information to an LLM using a prompt structure. See paragraph [0033]. Bowden’s claims also recite receiving “a list of required skills and known capabilities.”);
extracting from the applicant data file an applicant data record listing applicant competencies with a competency analysis module (
Bowden teaches that an LLM generates training materials by “first identifying the required skills and the user’s existing knowledge and skills.” See paragraph [0032].);
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chen’s skills-graph and course-recommendation system to receive and organize an applicant’s known capabilities, existing skills, and experience information as an applicant competency record for use in Chen’s skill-to-course matching workflow. The motivation would have been to provide personalized, targeted course recommendations based on demonstrated skills and prior experience. Bowden expressly teaches processing known capabilities and required skills to generate personalized educational outputs tailored to the individual’s skill level and context, see paragraphs [0002–0003], [0018], [0032–0033]. Chen already uses learner information including accomplished skills for individualized course recommendations, see paragraphs [0020–0023], [0029], [0037–0038].
Referring to Claim 2, Chen teaches the method of claim 1, wherein receiving from the applicant a plurality of documents including data reflecting an applicant's previous experience comprises receiving from a third party computing device applicant data regarding certifications received by the applicant and previous institution coursework (
Chen teaches receiving course-related information from instructors, learners, community mentors, and evaluation systems. Chen teaches data mined from “lecture transcripts,” “correct answers from online course assessments,” “learner course reviews,” “learner course forums,” and information collected from instructors and community mentors. See paragraph [0026]. Chen also discloses a networked system of multiple computing devices communicating with a computer system. See paragraphs [0039–0048].).
Referring to Claim 3, Chen teaches the method of claim 1, wherein extracting from the applicant data file an applicant data record listing applicant competencies with a competency analysis module comprising mapping applicant experience data to a set of predetermined keywords (
Chen teaches a skill library, skill identifiers, skill names, skill aliases, and course-tagged skills. Chen states that a skill profile can include “a skill name” and “one or more aliases for the skill.” See paragraph [0029]. Chen further teaches selecting a skill from a skill dropdown menu to tag a course. See paragraph [0062]. Chen teaches adding skill names and aliases to a database for later query by an enterprise search engine. See paragraph [0080].).
Referring to Claim 4, Chen teaches the method of claim 1, wherein extracting from the applicant data file an applicant data record listing applicant competencies with a competency analysis module (
Chen teaches a controlled skills vocabulary, including a skill library, skill names, aliases, skill identifiers, and course-tagged skills. A skill profile can include “a skill name” and “one or more aliases for the skill.” See paragraph [0029]. Chen also teaches learner selection or entry of skills in a query UI. See paragraphs [0069], [0074].)
Chen does not explicitly teach comprises supplying a prompt to an Al or LLM, wherein the prompt comprises predetermined keywords relating to competencies.
However, Bowden teaches comprises supplying a prompt to an Al or LLM, wherein the prompt comprises predetermined keywords relating to competencies (
Bowden teaches that “The prompt component 102 may be configured to receive prompts from the user” and that “The LLM component 104 may be configured to present prompts to the LLM which will generate the requested LLM output.” See paragraphs [0027–0028]. Bowden teaches a prompt structure including “the known context the known capabilities of the candidate, and the needed skills.” See paragraph [0033]. Bowden’s example identifies known context and needed skills in the prompt. See paragraph [0040].).
Therefore, it would have been obvious to a person of ordinary skill in the art to supply Chen’s selected/entered skill names, identifiers, and aliases as competency-related terms in Bowden’s LLM prompt structure. The motivation would have been to use an LLM to analyze candidate capabilities and generate a personalized skill-based educational output, while using Chen’s controlled skill vocabulary to reduce ambiguity and align the input to Chen’s course-skill graph. See paragraphs [0029], [0069], [0074], [0080] of Chen and paragraphs [0027–0028], [0033], [0040] of Bowden.
Referring to Claim 5, Chen teaches the method of claim 1, further comprising assigning a confidence score to matches between competencies in the applicant data record with competencies stored in a course model (
Chen teaches that a skill-to-course classification model uses tagged skills to determine a binary label, and that the label can be used “to train skill-course relationships and to predict a probability of a skill taught by a course.” See paragraph [0030]. Chen also teaches that “for each course, a score can be associated with each skill included in the skills graph,” and predicted probability can be an important input to that score. See paragraph [0031].Chen teaches: “A relevance score can be a ranking score that captures a relevance of the skill to the course.” See paragraph [0068].)
Chen does not explicitly teach and wherein matching output report comprises indications of the confidence score.
However, Bowden teaches and wherein matching output report comprises indications of the confidence score (
Bowden teaches that graphical display 14 visually presents information through one or more graphical user interfaces, including “information relating to LLM output.” See paragraph [0023]. Bowden’s delivery component presents LLM output to electronic storage and/or a graphical display. See paragraph [0030].).
Therefore, it would have been obvious to a person of ordinary skill in the art to display Chen’s skill-course probability, relevance score, or graph score in the graphical output taught by Bowden. The motivation would have been to provide an applicant or learner with a transparent indication of the strength or relevance of a competency-to-course association in personalized educational recommendations. See paragraphs [0030–0031], [0068–0069] of Chen and paragraphs [0023], [0030] of Bowden.
Referring to Claim 6, Chen teaches the method of claim 1, wherein formatting the applicant data with a formatting module to generate a single applicant data file reflecting the applicant data in the plurality of documents comprises subjecting the applicant data to an optical character recognition process, and wherein, the single applicant data file is text searchable (
Chen discloses textual course-content processing and teaches mining skills from text-based course materials including lecture transcripts, course descriptions, module descriptions, and assessment materials. See paragraphs [0026], [0066]. Chen teaches the searching of text “The recommendation service 106 can perform a skill-based search of the skills graph of content (e.g., courses) provided by an online course provider when formulating course recommendations to learners. The skill-based search can use a classification model that can predict a probability of a particular skill being taught by the course.” See paragraphs [0038]).
Referring to Claim 7, Chen teaches the method of claim 1, wherein using a matching module to match competencies in the applicant data record with competencies stored in a course model (
Chen teaches a course data model containing course content, skill library data, tagged skills, and a skill-course classification model. Chen states that skills may be mined from course content including lecture transcripts, assessments, reviews, forums, instructor data, and mentor data. See paragraph [0026]. Chen also teaches that binary labels are used “to train skill-course relationships and to predict a probability of a skill taught by a course.” See paragraph [0030].
Chen disclose course-content-based matching teaches that the skill-course classification model accesses tagged course content and determines the likelihood that a given skill is taught by a given course. See paragraphs [0065–0068].).
Chen does not explicitly teach comprises directing the matching module to formulate a prompt to an Al or LLM trained on course data records to perform a matching task.
However, Bowden teaches comprises directing the matching module to formulate a prompt to an Al or LLM trained on course data records to perform a matching task (
Bowden teaches LLM prompt processing, a prompt component that receives prompts and an LLM component that presents prompts to an LLM to generate requested output. See paragraphs [0027–0028].
Bowden teaches LLM training and skills prompts, that LLMs are trained using large text datasets and may be trained using supervised learning, comparison learning, and reinforcement learning. See paragraph [0020]. Bowden also teaches providing known candidate capabilities and needed skills through an LLM prompt structure. See paragraph [0033]).
Therefore, it would have been obvious to a person of ordinary skill in the art to employ Bowden’s LLM prompt-and-output architecture as the matching engine within Chen’s course-data and skills-graph system. The motivation would have been to use LLM natural-language processing to process candidate skill information against Chen’s course content, tagged skills, and learned skill-course relationships to generate personalized course recommendations. See paragraphs [0026], [0030], [0065–0068] of Chen and paragraphs [0020], [0027–0028], [0033] of Bowden.
Referring to Claim 8, Chen teaches the method of claim 1, further including using the matching module to partially match competencies in the applicant data record with competencies stored in a course model, and wherein the matching output report includes a record of partial matches to competencies learned in a course and competencies required to take a course (
Chen teaches skills that were taught and prerequisite course skills include prerequisite skills and taught skills. See paragraph [0022].
Chen teaches identification of missing prerequisites and alternatives that a learner can “filter out content courses where they lack the prerequisite skills,” and can “search for alternative content courses if the learner lacks identified prerequisite skills for the course.” See paragraph [0091].
Chen teaches course-specific output returning ordered courses, course-taught skills, and recommended prerequisite skills. See paragraph [0029].).
Referring to Claim 9, Chen teaches the method of claim 1, further comprising using the matching model to identify, for partial matches, course competencies not matched to an applicant data record (
Chen teaches identification of deficient prerequisites that learners can filter out courses for which they lack prerequisite skills, take diagnostic quizzes to evaluate readiness, and search for other courses if not prepared. See paragraph [0091].
Chen teaches preparatory material based on learner skills that preparatory materials may be offered based on learner skill information and a learner skills evaluation report. See paragraph [0037].).
Referring to Claim 10, Chen teaches the method of claim 1, further comprising using the output report module to generate one or more knowledge graph graphically depicting student competencies and mapping those competencies to competencies required as prerequisites for courses and competencies provided by successful completion of courses (
.
Chen teaches skills graph and graph hierarchy creating “a skills graph including tagged skills for a plurality of courses,” where the skills graph provides “a graph of a skills hierarchy for the course.” See paragraph [0005]. Chen also teaches vertices and edges representing skill relationships. See paragraphs [0006], [0010], [0025].
Chen teaches graph mapping to course content and skill dependencies that a skills graph maps skills to course content and that the skills mapping service models “a hierarchy of skill dependencies for the material taught by the course.” See paragraphs [0006], [0026].
Chen expressly teaches prerequisite skills and taught competencies skills associated with each course. See paragraphs [0021–0022], [0027], [0034].
Chen teaches graphical UI output and graphical query interfaces that receive skill data and display associated course results. See paragraphs [0069], [0074–0076].).
Referring to Claim 11, Chen teaches the method of claim 10, wherein a knowledge graph further includes an indication of a confidence score associated with each mapped connection between a student competency and a course competency (
Chen teaches graph-edge relevancy score that each edge of the skills graph can have “an associated relevancy score indicating a relevancy of a skill represented [by] the one vertex to the other vertex.” See paragraph [0006]. Chen repeats that graph edges may have associated relevancy scores. See paragraph [0010].
Chen teaches per-course skill scores and predicted probabilities that “for each course, a score can be associated with each skill included in the skills graph,” and that predicted probability from the skill-course classification model can be an input to that score. See paragraph [0031]. Chen also teaches relevance scoring for a skill relative to a course. See paragraph [0068].).
Referring to Claim 12, Chen teaches the method of claim 1, wherein the matching output report is formatted to further include career role and/or career path recommendations as a result of a competency matching process (
Chen teaches Skills-to-careers mapping: “The skills map can connect skills to one another, to content, and to careers.” See paragraph [0023]. Chen further teaches that “A subgraph can map skills to careers.” See paragraph [0008].
Chen teaches Career recommendation output that the recommendation service can traverse or query graphs of careers tagged with a particular skill and “provide a list of those careers to the learner.” See paragraph [0035]. Chen also teaches a learner can search for a career and obtain a list of courses providing needed skills for the career. See paragraph [0091].).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chua et al., U.S. Pub. 20200311683, (discussing the analyzing of a person’s skill set).
Vander Lugt et al., W.O. Pub. 2025049176, (discussing the assessment of student’s learning proficiencies).
Grájeda et al., Assessing Student-Perceived Impact Of Using Artificial Intelligence Tools: Construction Of A Synthetic Index Of Application In Higher Education, Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthet, Cogent Education, 2024 (discussing the use of artificial intelligence tools to assess students learned skills).
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