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 . 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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. The applicant's submission, the “RESPONSE TO FINAL OFFICE ACTION” filed on 03 November 2025 (hereinafter referred to as the “Response”), has been entered.
Status of the Claims
The pending claims in the present application are claims 1-5, 7, and 23-33 of the Response.
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 1-5, 7, and 23-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection, with the rationales being based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “method” of claims 1-5, 7, and 23-33 constitutes a process under 35 USC 101. Accordingly, claims 1-5, 7, and 23-33 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using independent claim 1 as an example, the claim recites the following abstract idea limitations:
“A ... method for generating a skills graph, comprising: ...” - See below regarding MPEP 2106.04(a), mental processes
“... a) processing ... at least one input document to generate a list of candidate skill topics; ...” - See below regarding MPEP 2106.04(a), mental processes
“... b) generating ... a list of skill topics based on the list of candidate skill topics ...” - See below regarding MPEP 2106.04(a), mental processes
“... c) generating ... a skill relation score for each pair of skill topics, by a process comprising the steps of; ...” - See below regarding MPEP 2106.04(a), mental processes
“... i) generating a vector representation in a high-dimensional vector space for each skill topic, and ...” - See below regarding MPEP 2106.04(a), mathematical concepts, and mental processes
“... ii) computing a metric in the vector space based on context information associated with each pair of skill topics, ...” - See below regarding MPEP 2106.04(a), mathematical concepts, and mental processes
“... wherein the skill relation score is generated based on the value of the metric for the pair of skill topics; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, and mental processes
“... d) generating ... a plurality of skill labels associated with at least some of the skill topics, using the at least one input document and/or a knowledge base ...” - See below regarding MPEP 2106.04(a), mental processes
“... e) generating ... a label association score for each skill label by a process comprising the steps of: ...” - See below regarding MPEP 2106.04(a), mental processes
“... i) associating a probability distribution to the skill label, wherein the probability distribution is defined over all skill topics associated with the skill label, and ...” - See below regarding MPEP 2106.04(a), mathematical concepts, and mental processes
“... ii) computing a label association score as the marginal probability of the skill label over the probability distribution of the skill label; and ...” - See below regarding MPEP 2106.04(a), mathematical concepts, and mental processes
“... f) generating ... a skills graph, wherein the skills graph comprises at least 10,000 nodes and/or weighted edges, by ...” - See below regarding MPEP 2106.04(a), mental processes
“... defining a skill topic node for each skill topic; ...” - See below regarding MPEP 2106.04(a), mental processes
“... defining a skill label node for each skill label; ...” - See below regarding MPEP 2106.04(a), mental processes
“... defining a weighted edge between each pair of skill topics, wherein the weight of the edge corresponds to the skill relation score; ...” - See below regarding MPEP 2106.04(a), mental processes
“... defining a weighted edge between each pair of skill topic and skill label, wherein the weight of the edge corresponds to the label association score; and ...” - See below regarding MPEP 2106.04(a), mental processes
“... removing edges and/or nodes from the skills graph until all nodes satisfy a predetermined minimum amount of connectedness; and ...” - See below regarding MPEP 2106.04(a), mental processes
“... g) generating a human-readable description for each skill topic node, in a plurality of languages ...” - See below regarding MPEP 2106.04(a), mental processes
The above-listed limitations of independent claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as mathematical relationships and/or mathematical calculations (e.g., the recited “generating a vector representation,” “computing a metric in the vector space,” “the skill relation score is generated,” “associating a probability distribution,” and “computing a label association score” limitations), which fall under the mathematical concepts grouping of abstract ideas (see MPEP 2106.04(a)). For example, generating a vector representation in a high-dimensional vector space could simply be plotting a series of values in a multi-dimensional graph or 3D plot. Computing a metric in a vector space could simply be calculating a number associated with a point in a multi-dimensional graph or 3D plot. Computing a label association score as something over something else could simply entail performing division, or making a fraction. Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “processing ... at least one input document” limitation), and evaluation, judgment, and/or opinion (e.g., the recited “generating ... a list,” “generating ... a skill relation score,” “generating a vector,” “computing a metric,” “generating ... a plurality of skill labels,” “generating ... a label association score,” “associating a probability distribution,” “computing a label association score,” “generating ... a skills graph,” “defining a skill topic node,” “defining a skill label node,” “defining a weighted edge,” “defining a weighted edge,” “removing edges and/or nodes,” and “generating a human-readable description” limitations), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use independent claim 1 as an example, the claim recites the following additional element limitations:
The claimed “method” is “computer-implemented” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “processing” is performed “by a computer” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generating ... a list” is performed “by the computer” and involves “using a trained classifier” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generating ... a skill relation score” is performed “by the computer” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generating ... a plurality of skill labels” is performed “by the computer” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “knowledge base” is “stored in a memory accessible to the computer” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generating ... a label association score” is performed “by the computer” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generating ... a skills graph” is performed “by the computer” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generating a human-readable description” is performed “using a large language model” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of independent claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, mere automation of manual processes, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering, and selecting a particular data source or type of data to be manipulated in the form of selecting information, based on types of information and availability of information in a particular environment, for collection, analysis, and display, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, independent claim 1 fails to meet the criteria of Step 2B. Further, claim 1 also fails to meet the criteria of Step 2B because at least some of the additional elements are analogous to: performing repetitive calculations, electronic recordkeeping, and storing and retrieving information in memory, which courts have recognized as well-understood, routine, conventional activity, and as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding pending claims 2-5, 7, and 23-33, the claims depend from independent claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recites abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “wherein the input document is a textual data source and step a) comprises i) analyzing, using ... parsing, a grammatical structure of the input document; and/or ii) extracting sequences of words, from the input document, to generate the list of candidate skill topics” of claim 2, the “further comprising the following step between steps a) and b): filtering the list of candidate skill topics, wherein the filtering comprises matching each candidate skill topic against a knowledge base, wherein the knowledge base comprises a public encyclopedia” of claim 3, the “wherein step b) comprises the following steps: i) classifying, each candidate skill topic into one of a plurality of classes, comprising at least one skill-class and at least one non-skill class, wherein the classifying is performed ...; and ii) removing each candidate skill topic that is classified into a non-skill class from the list of candidate skill topics” of claim 4, the “further comprising the following steps between steps b) and c): i) identifying synonymous skill topics, optionally by checking whether a pair of skill topics relates to the same document in the knowledge base, or querying ... to determine substitutability; and ii) for each skill topic, removing corresponding synonymous skill topics from the list of skill topics” of claim 5, the “wherein step c) comprises the following steps: i) generating a vector representation in a high-dimensional vector space for each skill topic; and ii) computing a metric in the vector space based on context information associated with each pair of skill topics; wherein the skill relation score is generated based on the value of the metric for the pair of skill topics” of claim 6, the “wherein step d) comprises at least one of the following: i) extracting phrases from the at least one input document; ii) manipulating phrases extracted from the input document by removing prefixes and/or suffixes; and/or iii) generating or extracting an abbreviation corresponding to a skill topic using a knowledge base; and iv) generating a list of synonymous skills with the skill topic as skill labels” of claim 7, the “wherein step e) comprises: associating a probability distribution to the skill label, wherein the probability distribution is defined over all skill topics associated with the skill label; and computing a label association score as the marginal probability of the skill label over the probability distribution of the skill label” of claim 8, the “method for determining gap skills for a source entity with respect to a target entity, wherein the method comprises: a) generating a skills graph according to the method of claim 1; b) generating respective skill profiles of the source entity and the target entity; c) determining a list of gap skills, based on a difference between the skill profile of the target entity and the skill profile of the source entity; d) for each gap skill from the list of gap skills, i) checking whether there exists a proxy skill topic in the skill profile of the source entity such that a relation score of the gap skill and the proxy skill topic exceeds a threshold value, and ii) removing the gap skill from the list of gap skills if the proxy skill topic exists; and e) providing the list of gap skills, wherein the list of gap skills is provided in a sorted order in accordance with levels of the corresponding skill topics in the skill profile of the target entity” of claim 23, the “further comprising the following steps: i) identifying at least one training based on the list of gap skills; and ii) providing a list of trainings to a user, the list comprising the at least one identified training, in a sorted order; wherein the sorted order is sorted in accordance with levels of the corresponding skill topics in the skill profile of the target entity” of claim 24, the “A ... method for mapping organizational competencies, wherein the method comprises the following steps: a) generating a skills graph according to the method of claim 1; b) processing at least one input list of competencies associated with an organization by processing a description of at least one job role or job posting associated with the organization; c) for each competency, identifying associated skill topics from the skills graph to generate a competency model; and d) for each competency and one or more source entities, determining a competency gap based on the competency model and corresponding skill profiles of the one or more source entities, wherein each source entity comprises a person, a job, project, or a work assignment” of claim 25, the “wherein step d) comprises: determining one or more gap skills for the one or more source entities with respect to the competency model as a target entity; wherein the competency gap includes at least some of the determined gap skills” of claim 26, the “further comprising: identifying at least one training or a series of trainings, based on the one or more gap skills” of claim 27, the “method for generating descriptions for an entity, wherein the entity comprises an organizational job role, a job posting, a project, or a work assignment, wherein the method comprises: a) generating a skills graph according to the method of claim 1; ... c) receiving, as a user input, a title for the entity; d) receiving, as a user input, a list of skill topics from the skills graph; e) generating a human-readable description of the entity, in a plurality of languages, by applying ... on the title, and/or one or more skill topics; and f) generating a list of responsibilities, activities, and qualifications for the entity by using template queries ..., the template queries comprising the title and the list of skill topics” of claim 28, the “wherein the input document is a textual data source and step a) comprises: i) analyzing, using ... parsing, a grammatical structure of the input document; and ii) extracting sequences of words, from the input document to generate the list of candidate skill topics” of claim 29, the “further comprising the following steps after step f): i) removing edges from the skills graph whose weight is less than a threshold value; and ii) removing isolated skill label nodes from the skills graph” of claim 30, the “wherein step d) comprises: i) extracting phrases from the at least one input document; ii) manipulating phrases extracted from the input document by removing prefixes and/or suffixes; iii) generating or extracting an abbreviation corresponding to a skill topic using a knowledge base; and iv) generating a list of synonymous skills with the skill topic as skill labels” of claim 31, the “wherein the vector representation in a high-dimensional vector space for each skill topic represents or encodes a semantic embedding of the skill topic as a vector in an n-dimensional real vector space, indicating its relationships to other skill topics” of claim 32, and the “wherein the vector representation in a high-dimensional vector space for each skill topic is computed by combining vectors” of claim 33). Further, at least claims 24 and 28 also recite limitations falling under the certain methods of organizing human activity grouping of abstract ideas, as they involve activities of users. The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “computer-implemented” of claim 2-9 and 23-31, the “automatic” of claim 2, the “using a trained classifier, the trained classifier being augmented by a large language model” of claim 4, the “large language model” of claim 5, the “b) training or fine-tuning ..., using textual data descriptive of skills, job roles, job postings and/or projects ... applying the large language model ... on the large language model” of claim 28, the “automatic” of claim 29, and the “from the layers of a deep neural network pre-trained on definitional text about skill topics” of claim 33). Accordingly, claims 2-5, 7, and 23-33 also are rejected as ineligible under 35 USC 101.
Examiner Remarks
In view of the amendments to the claims of the Response, the applicant’s remarks regarding the amendments, and the reasons set forth below, the claim rejections under 35 USC 103 have been reconsidered and withdrawn. No additional prior art rejections have been asserted against the claims. Independent claim 1 has been amended to include the recited “e) generating, by the computer, a label association score for each skill label, by a process comprising the steps of: i) associating a probability distribution to the skill label, wherein the probability distribution is defined over all skill topics associated with the skill label, and ii) computing a label association score as the marginal probability of the skill label over the probability distribution of the skill label” limitation. As acknowledged previously on pp. 33-35 of the Final Office Action of 01 July 2025, the cited Kenthapadi, Macskassy, Rosenkranz, and Brants references fail to disclose, teach or suggest the claimed concept.
The examiner relied on the cited Chua reference to disclose, teach, or suggest said concept. Chua discloses, “Attribute repository 234 stores data that represents standardized, organized, and/or classified attributes (e.g., attribute 1 222, attribute x 224) in profile data 216 and/or jobs data 218. For example, skills in profile data 216 and/or jobs data 218 may be organized into a hierarchical taxonomy that is stored in attribute repository 234 and/or another repository” (para. [0029]), “a similarity score between skills 302 and 304 may be divided by sum 318 to obtain normalized similarity score 322; the same similarity score between skills 302 and 304 may also be divided by sum 320 to obtain normalized similarity score 324. Thus, normalized similarity score 322 may represent a ‘probability’ ranging from 0 to 1 that skill 302 precedes skill 304 in sequence 326, and normalized similarity score 324 may represent a “probability” ranging from 0 to 1 that skill 304 precedes skill 302 in sequence 326. Sequence 326 may then be selected to reflect the order of skills 302 and 304 associated with the higher normalized similarity score” (para. [0047]), and “skills 302-304 may have a similarity score of 0.75, sum 318 may be calculated by adding similarity scores 310 between skill 302 and the 20 most similar skills 306 to obtain a value of 10, and sum 320 may be calculated by adding similarity scores 312 between skill 304 and the 20 most similar skills 308 to obtain a value of 8. Normalized similarity score 322 may be calculated as 0.75/10, or 0.075, and normalized similarity score 324 may be calculated as 0.75/8, or 0.09375” (para. [0048]).
While Chua discloses normalized similarity scores representing probabilities, that potentially could read on the recited “probability distribution” limitation of independent claim 1, Chua does not appear to disclose, teach, or suggest computing a marginal normalized similarity score over a distribution of normalized similarity scores, and thus, Chua also does not appear to read on the recited “computing a label association score as the marginal probability of the skill label over the probability distribution of the skill label” limitation. Because Chua fails to remedy the deficiencies of the other cited references, claim 1 distinguishes over the closest prior art of record.
Response to Arguments
On pp. 8-14 of the Response, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. More specifically, the applicant argues that new guidance from the Office for inventions related to AI and ML provide rationales for eligibility. (See Response, 9 and 10.) The examiner finds the arguments unpersuasive. The new guidance appears to address training steps for AI and ML. The applicant’s claims do not recite training steps for AI or ML, and thus, the asserted guidance and rationales are not applicable.
The applicant also argues that the human mind is not equipped to perform encoding operations used in ML, including the encoding of inputs into higher-dimensional vector space, as recited in the claims. (See Response, p. 10.) The applicant states that the PTAB has reversed ineligibility rejections after recognizing that vectorizing inputs does not qualify as a mental process, citing Ex Parte Baker. Ex Parte Baker appears to involve a decision surface and a classifier--neither of which has any corollary in the applicant’s claims. The PTAB states, on p. 12 of the decision, “As an initial matter, we agree with the Examiner that the steps of computing scores for the data examples and back propagating the partial derivatives to obtain vectors involve mathematical computations, which can be performed mentally or using pen and paper. Ans. 19. However, we are persuaded by Appellant’s argument that the step of adjusting the classifier necessarily involves applying the computation results to a real-world classifier to reduce detected errors therein. Appeal Br. 25–26. As such, we agree with Appellant that the claims are not directed to a mental process or a mathematical concept categories of abstract idea. Id.” Thus, without the claim including something similar to the classifier in Ex Parte Baker, Ex Parte Baker appears to support the examiner’s contention that the vector limitations of the pending claims can be performed mentally or using pen and paper.
The applicant also argues that step f) requires the generation of a skill graph comprising at least 10,000 nodes and/or weighted edges, where the graph includes a skill topic node for each skill topic, a skill label node for each skill label, and weighted edges based on the skill relation score and the label associations score. The examiner finds the arguments unpersuasive. The wording of the claims permits interpreting the claims as referring to a skill graph of 9,990 dots and connecting lines depicted visually with no meaning behind them, and only 10 nodes and lines relating to skill topics, skill labels, relation scores, and associations scores. The claim does not recite, for example, that there are thousands of skill topics and nodes, thousands of skill labels and nodes, and thousands of weighted edges therebetween. Processes involving a handful of nodes and edges with meaning could be performed near instantly in the mind.
The applicant also argues that the expert declaration of Sarath Kondreddi supports the arguments. The examiner finds the argument and declaration unpersuasive. See the remarks above regarding the number of meaningful skill graph nodes and edges, which addresses both remarks from the Response and issues raised by the declaration. Further, the complexity of defining a skill topic node for each skill topic depends on the number of skill topics, which the claim does not actually define. The declaration discusses handling millions of candidates, which is not claimed. The declaration also discusses the urgency of rapidly generating large skills graphs to provide timely insights, and manually generating requiring several years. But the claims do not recite any time periods, expiration dates, or the like. Nothing in the claims implies that updated information is required within some timeframe. The claims read like they could be used simply for generating as large of a skill graph as possible simply for historical record-keeping purposes. Overall, the declaration sets forth evidence and opinions that are not commensurate with the scope of the claims under broadest reasonable interpretation, and is not persuasive. Furthermore, even if such processing could not be performed mentally, as the declaration contends, the claim limitations still fall under the mathematical concepts grouping of abstract ideas.
The applicant also argues that claim limitations (previously recited in claims 6 and 8) where identified as mathematical concepts without any substantive analysis to support such a position. Substantive analysis has now been provided in the 35 USC 101 section above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
U.S. Pat. App. Pub. No. 2022/0374812 A1 to Riedl discloses, “generation and traversal of a skill representation graph using machine learning is provided. The system includes a computing device configured to receive a plurality of data of a plurality of individuals including individual skill levels corresponding to a common skill of a plurality of common skills. The computing device determines a relative skill level of the plurality of individuals from the plurality of data and generate a skill representation graph representing a plurality of skill interrelations. Generating the graph further includes generating a plurality of nodes representing a skill, generating a plurality of interconnections representing a process and/or path to master a subsequent skill of a first skill, generating the plurality of interrelations as a function of at least the plurality of data and a machine-learning model, and assembling the graph. The system finally includes a user device configured to display the skill representation graph.” (Abstract.)
Holland, Alexander, and Madjid Fathi. "Creating graphical models as representation of personalized skill profiles." 2005 IEEE international conference on systems, man and cybernetics. Vol. 1. IEEE, 2005.
Gugnani, Akshay, Vinay Kumar Reddy Kasireddy, and Karthikeyan Ponnalagu. "Generating unified candidate skill graph for career path recommendation." 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624