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 .
DETAILED ACTION
This Non-Final rejection is in reply to the application filed 9/5/2025.
Claims 1-20 are pending.
Drawings
New corrected drawings in compliance with 37 CFR 1.121(d) are required in this application because the figures fail to provide the structural details, nomenclature, labels or data as described in the specification. Applicant’s drawings are merely boxes, and/or incomplete and do not provide the requisite details essential for a proper understanding of the disclosed invention. Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claims in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims 1-7 include one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a user profile management module configured to”, “a real-time data integration module configured to”, “a gap analysis engine configured to” [claim 1]. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites, “a user profile management module configured to”, “a real-time data integration module configured to”, “a gap analysis engine configured to” and is rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor-see applicant’s disclosure, ¶23: “b. a user profile management module configured to track a user's acquired skills and suggest potential career transitions based on those skills; c. a real-time data integration module configured to pull data from external labor market sources and dynamically update the career pathways based on current trends; and d. a gap analysis engine configured to compare a user’s current skill set to desired career roles and suggest educational or certification pathways to fill the gaps”. Applicant’s disclosure merely provides functional language for achieving a desired outcome and does not provide the requisite hardware or software components to perform the claimed limitations. Accordingly, the disclosure is not commensurate with the scope of the claim.
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 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1-7 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Applicant’s specification is vague and discloses no corresponding hardware and/or algorithm associated with a computer or microprocessor. Aristocrat, 521 F.3d at 1337-38, 86 USPQ2d at 1242. The terms “a user profile management module configured to”, “a real-time data integration module configured to”, “a gap analysis engine configured to” is/are a computer-implemented means-plus-function limitation(s), and the specification does not disclose the requisite structure nor an operative algorithm for the claim elements. While the specification describes in general terms at see applicant’s disclosure, ¶23: “b. a user profile management module configured to track a user's acquired skills and suggest potential career transitions based on those skills; c. a real-time data integration module configured to pull data from external labor market sources and dynamically update the career pathways based on current trends; and d. a gap analysis engine configured to compare a user’s current skill set to desired career roles and suggest educational or certification pathways to fill the gaps”, is simply a description of the outcome (function) of the claimed elements, not a description of the structure. Hence, the specification fails to disclose any structure, algorithm or description as to how the respective information is actually acquired, calculated and established. Further, mere reference to a general-purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting "software" (algorithm) without providing details about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239; Finisar, 523 F.3d at 1340-41, 86 USPQ2d at 1623. In addition, merely referencing a specialized computer (e.g., a "bank computer"), some undefined component of a computer system “program”, "logic," "code," or elements that are essentially a black box designed to perform the recited function, will not be sufficient because there must be some explanation of how the computer or the computer component performs the claimed function. Blackboard, Inc. v. Desire2Learn, Inc., 574 F.3d 1371, 1383-85, 91 USPQ2d 1481, 1491-93 (Fed. Cir. 2009); Net MoneyIN, Inc. v. VeriSign, Inc., 545 F.3d 1359, 1366-67, 88 USPQ2d 1751, 1756-57 (Fed. Cir. 2008); Ex parte Rodriguez, 92 USPQ2d 1395, 1405-06 (Bd. Pat. App. & Inter. 2009).
Therefore, the claim(s) is/are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. The respective dependent claims do not remedy this flaw and are therefore also rejected.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 are directed to a process (an act, or series of acts or steps); claims 11-20 are directed to a system Thus claims 1-20 fall within one of the four statutory categories.
Step 2A-Prong 1: Claim 1 recites, “a database storing a plurality of modular career pathways, each defined by a set of skills and competencies; a user profile management module configured to track a user's acquired skills and suggest potential career transitions based on those skills; a real-time data integration module configured to pull data from external labor market sources and dynamically update the career pathways based on current trends; and a gap analysis engine configured to compare a user's current skill set to desired career roles and suggest educational or certification pathways to fill the gaps.
The underlined limitations above demonstrate independent claim 1 is directed toward the abstract idea for storing a plurality of modular career pathways, tracking a user’s skills and suggesting potential career transitions, updating the career pathways based on current trends, comparing user current skill set to desired career roles, and suggesting educational or certification pathways to fill the gaps in a computing environment. Applicant’s specification emphasizes a career guidance and planning systems, and more particularly to a dynamic career guidance system that uses a skills-based approach to guide users through flexible, modular career pathways based on real- time data and the user's evolving skill set. Applicant’s disclosure discusses that the system comprises a database storing a plurality of modular career pathways, each defined by a set of skills and competencies. The system comprises a user profile management module configured to track a user's acquired skills and suggest potential career transitions based on those skills. The system comprises a real-time data integration module configured to pull data from external labor market sources and dynamically update the career pathways based on current trends. The system comprises a gap analysis engine configured to compare a user's current skill set to desired career roles and suggest educational or certification pathways to fill the gaps. Applicant’s specification further teaches a user profile management module configured to continuously update to reflect new skills acquired by the user, and that modular career pathways allow users to transition between careers across different industries by identifying transferable skills. The system may further comprise a dynamic visualization tool configured to present users with potential future career paths, lateral transitions, and progression maps based on their current and target skills (¶4, ¶14-¶16)
Claim 1 is considered an abstract idea because the (underlined) limitations as claimed, pertains to (i) concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and (ii) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). With the exception of a “system”, “database”, “user profile management module”, “real-time data integration module”, “gap analysis engine”, [claim 1]; “non-transitory computer readable medium”, “processor” [claim 15] a human being with a pen and paper can store a plurality of modular career pathways, track a user's acquired skills via a user profile, suggest potential career transitions based on those skills; update a user’s career pathway based on current trends; compare a user's current skill set to desired career roles and suggest educational or certification pathways to fill the gaps, hence the claim limitations are directed to the mental processes grouping of abstract ideas. The limitations are also directed to (ii) managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions whereby a plurality of modular career pathways are stored, a user's acquired skills via a user profile is tracked (managing personal behavior or relationships), potential career transitions based on those skills is suggested (managing personal behavior or relationships or interactions between people including social activities, teaching); a user’s career pathway based on current trends is updated (managing personal behavior or relationships); a user's current skill set to desired career roles is compared and educational or certification pathways to fill the gaps are suggested (interactions between people including social activities, teaching, following rules or instructions. Therefore, the claim is directed to certain methods of organizing human activity and recites an abstract idea--see MPEP 2106.04(II).
Step 2A-Prong 2: This judicial exception is not integrated into a practical application because the additional elements “system”, “database”, “user profile management module”, “real-time data integration module”, “gap analysis engine”, [claim 1]; “non-transitory computer readable medium”, “processor” [claim 15] for (storing, tracking, updating, comparing, suggesting, maintaining, managing, generating, integrating, performing, providing) data gathering and analysis and merely to provide instructions for managing information, and to implement the abstract idea recited above utilizing a “system”, “database”, “user profile management module”, “real-time data integration module”, “gap analysis engine”, [claim 1]; “non-transitory computer readable medium”, “processor” [claim 15] as a tool to perform the abstract idea, and generally links the abstract idea to a particular technological environment. See MPEP 2106.05 (f-h).
Independent claim 1 fails to operate “system”, “database”, “user profile management module”, “real-time data integration module”, “gap analysis engine”, [claim 1]; “non-transitory computer readable medium”, “processor” [claim 15] (which is merely a nominal recitation of a standard computer technology, database and hardware/software components) in any exceptional manner, and there is no evidence in the disclosure to suggest achieving an actual improvement in the computer functionality itself, or improvement in any specific computer technology other than utilizing ordinary computational tools to automate and perform the abstract idea for storing a plurality of modular career pathways, tracking a user’s skills and suggesting potential career transitions, updating the career pathways based on current trends, comparing user current skill set to desired career roles, and suggesting educational or certification pathways to fill the gaps in a computing environment—see MPEP 2106.05(a). Accordingly, applicant has not shown an improvement or practical application under the guidance of MPEP section 2106.04(d) or 2106.05(a). Applicant’s limitations as recited above do nothing more than supplement the abstract idea using generic computer components performing generic computer functions (storing, tracking, updating, comparing, suggesting, maintaining, managing, generating, integrating, performing, providing) such that it amounts to no more than mere instruction to apply the exception using a generic computer component-see MPEP 2106.05(f) and linking the use of the judicial exception to a particular technological environment as discussed in MPEP 2106.05(h). Independent claims 8 and 15 recite substantially similar limitations as independent claim 1 and therefore also recite the same abstract idea.
Dependent claims 2-7, 9-14 and 16-20 fail to cure the deficiencies of the above noted independent claim from which they depend and are therefore rejected under the same grounds. The dependent claims further recite the abstract idea without imposing any meaningful limits on practicing the abstract idea. Dependent claim 2 , recites in part, “wherein the user profile management module is configured to:”; claim recites in part, “wherein the modular career pathways allow users to”; claim 4, recites in part, “wherein the real-time data integration module is configured to:”; claim 5 recites in part, “further comprising a dynamic visualization tool configured to”; claim 6 recites in part, “wherein the database storing the plurality of modular career pathways comprises”; claim 7 recites in part, “wherein each of the eight broad categories further comprises”; claims 9 recites in part, “further comprising continuously updating the user's profile”; claim 10 recites in part, “wherein suggesting potential career transitions comprises”, claim11 recites in part, “wherein pulling data from external labor market sources comprises”, claim 12 recites in part, “further comprising presenting users with”, claim 13 recites in part, “; claim 14 recites in part, “wherein each of the eight broad categories further comprises; claim 16 recites in part, “wherein managing the user profile comprises; claim 17 recites in part, “wherein generating career transition suggestions comprises; claim 18 recites in part, “wherein integrating real-time labor market data comprises; claim 19 recites in part, “further comprising presenting users with”; claim 20 recites in part, “wherein the dynamic visualization includes’, which is still directed toward the abstract idea identified previously and are no more than mere instructions to apply the exception using a computer or with computing components. Therefore, the abstract idea fails to integrate into any practical application. Thus, under Step 2A-Prong Two the claims are directed to an abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above, with respect to integration of the abstract idea into a practical application, the additional element “system”, “database”, “user profile management module”, “real-time data integration module”, “gap analysis engine”, [claim 1]; “non-transitory computer readable medium”, “processor” [claim 15] amounts to no more than mere instructions to apply the exception using a generic computer component and linking the use of the judicial exception to a computing environment which does not integrate a judicial exception into a practical application nor provide an inventive concept (significantly more than the abstract idea). In this case, the “system”, “database”, “user profile management module”, “real-time data integration module”, “gap analysis engine”, [claim 1]; “non-transitory computer readable medium”, “processor” [claim 15] are generically used to further process and store received data- see applicant’s disclosure see applicant’s disclosure, ¶15: “The system comprises a database storing a plurality of modular career pathways, each defined by a set of skills and competencies. The system comprises a user profile management module configured to track a user's acquired skills and suggest potential career transitions based on those skills. The system comprises a real-time data integration module configured to pull data from external labor market sources and dynamically update the career pathways based on current trends. The system comprises a gap analysis engine configured to compare a user's current skill set to desired career roles and suggest educational or certification pathways to fill the gaps”; ¶19: “a non-transitory computer-readable medium storing instructions is provided. When executed by a processor, the instructions cause the processor to perform a method for dynamic career guidance. The method comprises maintaining a database of modular career pathways, each defined by a set of skills and competencies”; ¶23: “b. a user profile management module configured to track a user's acquired skills and suggest potential career transitions based on those skills; c. a real-time data integration module configured to pull data from external labor market sources and dynamically update the career pathways based on current trends; and d. a gap analysis engine configured to compare a user’s current skill set to desired career roles and suggest educational or certification pathways to fill the gaps”.
Further applicant’s “a dynamic visualization tool” [claims 5, 12 and 18]; is merely used as a tool to further process received data-see applicant’s disclosure ¶16: “The system may further comprise a dynamic visualization tool configured to present users with potential future career paths, lateral transitions, and progression maps based on their current and target skills”; ¶80: “A visualization interface 1124 presents analysis results in user-friendly formats that enable clear understanding of skill gaps and development requirements”. Hence, the 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.
Accordingly, even when considered as a whole, the claims do not transform the abstract idea into a patent-eligible invention since the claim limitations do not amount to a practical application or significantly more than an abstract idea for storing a plurality of modular career pathways, tracking a user’s skills and suggesting potential career transitions, updating the career pathways based on current trends, comparing user current skill set to desired career roles, and suggesting educational or certification pathways to fill the gaps in a computing environment. Therefore, claims 1-20 are directed to non-statutory subject matter and are rejected under 35 USC 101. See 2019 PEG and MPEP 2106.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8-12, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Baker et al., US Patent Application Publication No US 2023/0245067 A1, in view of Agbonlahor et al., US Patent Application Publication No US 2025/0005694 A1.
With respect to claims 1, 8 and 15,
Baker discloses,
a database storing a plurality of modular career pathways, each defined by a set of skills and competencies; maintaining a database of modular career pathways, each defined by a set of skills and competencies (Abstract: “The server is configured to store information for the system, the information including at least one of organization data, user data and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies; and implement at least an analytics engine”; ¶14: “the computer artificial intelligence model can be trained using at least one of: personal profile of an individual, including role, interests, background education, competencies, competency gaps; information from third parties including universities, the information indicating what programs lead into certain skills; crowd sourcing tagging of skills and competencies; Internet sources using semantic analysis; information pertaining to skill gaps at industry level; organizational competencies and needs in relation to competencies of current personnel; and available job opportunities”)
a user profile management module configured to track a user's acquired skills and suggest potential career transitions based on those skills; managing a user profile to track acquired skills (¶14: “the computer artificial intelligence model can be trained using at least one of: personal profile of an individual, including role, interests, background education, competencies, competency gaps; information from third parties including universities, the information indicating what programs lead into certain skills; crowd sourcing tagging of skills and competencies; Internet sources using semantic analysis; information pertaining to skill gaps at industry level; organizational competencies and needs in relation to competencies of current personnel; and available job opportunities”; ¶17: “the server is further configured to recommend a promotion for an individual to a role in connection with a personalized learning pathway to assist the individual in attaining competencies associated with the role”; ¶18; “determining, using the analytics engine, a role and/or opportunity for a user based at least on one of organization data, user data and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies; and recommending, using the analytics engine, individuals for roles based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies”; ¶19: “the method includes training a computer model over time with historical data to determine the role and/or opportunity for the user and to recommend individuals for roles”)
maintaining a database of modular career pathways, each defined by a set of skills and competencies(¶14: “the computer artificial intelligence model can be trained using at least one of: personal profile of an individual, including role, interests, background education, competencies, competency gaps; information from third parties including universities, the information indicating what programs lead into certain skills; crowd sourcing tagging of skills and competencies; Internet sources using semantic analysis; information pertaining to skill gaps at industry level; organizational competencies and needs in relation to competencies of current personnel; and available job opportunities”; ¶17: “the server is further configured to recommend a promotion for an individual to a role in connection with a personalized learning pathway to assist the individual in attaining competencies associated with the role”)
generating career transition suggestions based on the user's skills (¶54: “historical data may reveal that users with a particular combination of personal profile data have an aptitude for one or more specific personal learning pathways and/or a predisposition toward one or more specific careers”; ¶67: “recommended opportunity/career role for individuals based on a statistical analysis of the extent of overlap between competencies/interests of individuals and opportunities. For example, the recommendation can include matching roles and individuals based on a competency gap that is less than a (configurable) threshold. For individuals that do not have all competencies (or at least core competencies) to a role, the system and method can deem individuals as good enough. For individuals that are deemed good enough, the system and method can further recommend the promotion to the role in connection with a personalized pathway to assist the individual in attaining the competencies of the gap”)
a gap analysis engine configured to compare a user's current skill set to desired career roles and suggest educational or certification pathways to fill the gaps (¶14: “ the computer artificial intelligence model can be trained using at least one of: personal profile of an individual, including role, interests, background education, competencies, competency gaps; information from third parties including universities, the information indicating what programs lead into certain skills; crowd sourcing tagging of skills and competencies; Internet sources using semantic analysis; information pertaining to skill gaps at industry level; organizational competencies and needs in relation to competencies of current personnel; and available job opportunities”; ¶25: “the method can include recommending a promotion for an individual to a role in connection with a personalized learning pathway to assist the individual in attaining competencies associated with the role”; ¶50: “data may include at least one of: (I) user personal profile data; (ii) education provider data; (iii) crowd sourcing tagging of skills and/or competencies; (iv) internet sources using semantic analysis; (v) information pertaining to skill gaps at industry level; and (vi) historical data pertaining to other users that followed similar paths or developed similar competencies”; ¶51: “user personal profile data may include a currently held role, interests, background education, competencies and competency gaps, and other characteristics of the user… one or more desired outcomes of a personalized learning pathway, such as the development of one or more target competencies and/or the development of one or more competencies required for a target career”; ¶67:“recommended opportunity/career role for individuals based on a statistical analysis of the extent of overlap between competencies/interests of individuals and opportunities. For example, the recommendation can include matching roles and individuals based on a competency gap that is less than a (configurable) threshold. For individuals that do not have all competencies (or at least core competencies) to a role, the system and method can deem individuals as good enough. For individuals that are deemed good enough, the system and method can further recommend the promotion to the role in connection with a personalized pathway to assist the individual in attaining the competencies of the gap”)
performing gap analysis between user skills and desired career roles; and providing recommendations for skill development based on the gap analysis (¶25: “the method can include recommending a promotion for an individual to a role in connection with a personalized learning pathway to assist the individual in attaining competencies associated with the role”; ¶50: “data may include at least one of: (I) user personal profile data; (ii) education provider data; (iii) crowd sourcing tagging of skills and/or competencies; (iv) internet sources using semantic analysis; (v) information pertaining to skill gaps at industry level; and (vi) historical data pertaining to other users that followed similar paths or developed similar competencies”; ¶51: “user personal profile data may include a currently held role, interests, background education, competencies and competency gaps, and other characteristics of the user… one or more desired outcomes of a personalized learning pathway, such as the development of one or more target competencies and/or the development of one or more competencies required for a target career”; ¶67:“recommended opportunity/career role for individuals based on a statistical analysis of the extent of overlap between competencies/interests of individuals and opportunities. For example, the recommendation can include matching roles and individuals based on a competency gap that is less than a (configurable) threshold. For individuals that do not have all competencies (or at least core competencies) to a role, the system and method can deem individuals as good enough. For individuals that are deemed good enough, the system and method can further recommend the promotion to the role in connection with a personalized pathway to assist the individual in attaining the competencies of the gap”)
Baker discloses all of the above limitations, Baker does not distinctly disclose the following limitations, but Agbonlahor however as shown discloses,
a real-time data integration module configured to pull data from external labor market sources and dynamically update the career pathways based on current trends (¶46: “a web and/or mobile application associated with the user facilitates gathering user data, such as extracurricular activities, hobbies, skills, contemporaneous data, social media data etc., which allows the ML model predictions to change dynamically to accommodate changes in the user profile, and interest in real-time”; ¶81: “a plurality of job opportunities, scholarships etc. are retrieved from external data sources 33 and stored in the backed-end database storage 20. The stored user data is retrieved using the user token, in step 216. Next, in step 218, the system matches the user to jobs, scholarships etc., based on the interests, skills, activities etc. To match users with job or career opportunities, the system 10 may exchange data with external data sources 33 e.g., LinkedIn jobs etc. using API's such as Rapid API to dynamically capture newly posted jobs, which it then matches to the users based on the ML recommendation”)
integrating real-time labor market data to update career pathways (¶45: “a machine learning model and a data pipeline that maps user profiles into various career paths based on their skills, education, and professional and non-professional activities. To achieve this, the model maps both professional and non-professional activities to specific character traits, and then maps those specific character traits to closely matched industries and then potential careers”; ¶46: “a web and/or mobile application associated with the user facilitates gathering user data, such as extracurricular activities, hobbies, skills, contemporaneous data, social media data etc., which allows the ML model predictions to change dynamically to accommodate changes in the user profile, and interest in real-time”; ¶81: “To match users with job or career opportunities, the system 10 may exchange data with external data sources 33 e.g., LinkedIn jobs etc. using API's such as Rapid API to dynamically capture newly posted jobs, which it then matches to the users based on the ML recommendation”)
A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method for dynamic career guidance (Abstract: “A method for recommending a career to a user, the method comprising the steps of:”; ¶112: “Various embodiments are implemented fully or partially in software and/or firmware. This software and/or firmware can take the form of instructions contained in or on a non-transitory computer-readable storage medium. Those instructions can then be read and executed by one or more processors to enable performance of the operations described herein”)
Baker discloses a method/system for recommending potential careers or career paths pertaining to an individual’s user profile data, and historical information correlated to others that followed similar paths or developed similar competencies. Agbonlahor teaches method/system for career mapping which allows a machine learning model to predict career changes based on a user profile and interest in real-time. Baker and Agbonlahor are directed to the same endeavor since they are related to method/systems for recommending a career based on user profile data, job, educational history and external statistical data in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the techniques for recommending potential careers or career paths of Baker with the method/system for predicting a user career as taught by Agbonlahor since it allows for predictions to change dynamically based on changes in the user profile and interest in real-time (Figures 1-7, ¶38-46, ¶81)
With respect to claims 2, 9 and 16,
Baker and Agbonlahor disclose all of the above limitations, Agbonlahor further discloses,
wherein the user profile management module is configured to continuously update to reflect new skills acquired by the user, offering real-time career evolution based on skill growth (¶46: “a web and/or mobile application associated with the user facilitates gathering user data, such as extracurricular activities, hobbies, skills, contemporaneous data, social media data etc., which allows the ML model predictions to change dynamically to accommodate changes in the user profile, and interest in real-time”; ¶71: “the probing questions help the user reflect on the achievements of the day in a career coaching and storytelling format that captures the salient skills the user is exhibiting. Several datasets are obtained following the above-noted data acquisition steps, and instructions associated with the data preparation module 23 are executed by the processing circuitry 16 to receive the datasets for cleaning and pre-processing and standardization. The system 10 extracts the relevant skills, including skills from the non-professional activities into the user's achievement profile, and the machine 14 then pre-processes the user data”; ¶72: In step 104, the pre-processed user data is inputted to the prediction module 26 having one or more trained machine learning (ML) models 22 which autogenerate related skills associated with the inputted user data using predictive algorithms associated with the prediction module 26”; ¶81: “a plurality of job opportunities, scholarships etc. are retrieved from external data sources 33 and stored in the backed-end database storage 20. The stored user data is retrieved using the user token, in step 216. Next, in step 218, the system matches the user to jobs, scholarships etc., based on the interests, skills, activities etc. To match users with job or career opportunities, the system 10 may exchange data with external data sources 33 e.g., LinkedIn jobs etc. using API's such as Rapid API to dynamically capture newly posted jobs, which it then matches to the users based on the ML recommendation”)
Baker and Agbonlahor are directed to the same endeavor since they are related to method/systems for recommending a career based on user profile data, job, educational history and external statistical data in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the techniques for recommending potential careers or career paths of Baker with the method/system for predicting a user career as taught by Agbonlahor since it allows for predictions to change dynamically based on changes in the user profile and interest in real-time (Figures 1-7, ¶38-46, ¶71-¶74, ¶81)
With respect to claims 3, 10 and 17,
Baker and Agbonlahor disclose all of the above limitations, Agbonlahor further discloses,
wherein the modular career pathways allow users to transition between careers across different industries by identifying transferable skills (¶98: “experienced professionals can find out what their transferable skills are, and the other industries that they can transition into with respect to each country. This may be useful for new immigrants as they relocate to new countries”)
Baker and Agbonlahor are directed to the same endeavor since they are related to method/systems for recommending a career based on user profile data, job, educational history and external statistical data in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the techniques for recommending potential careers or career paths of Baker with the method/system for predicting a user career as taught by Agbonlahor since it allows for providing experienced professionals information on what other industries they can transition into with their transferable skills (¶98, ¶99)
With respect to claims 4, 11 and 18,
Baker and Agbonlahor disclose all of the above limitations, Agbonlahor further discloses,
wherein the real-time data integration module is configured to pull data from publicly available labor databases, including Linkedln's Economic Graph, ONET, and CareerOneStop (¶77: “The one or more trained machine learning (ML) models 22 generate artifacts and tags such as related skills, tools, related industries, and resume work blocks. Furthermore, these artifacts can be tagged with one or more projects to which they are related and stored against the user. Accordingly, for every user, the history of the user's skills, projects, industries, professional and non-professional activities over time (weeks, months, years, etc.) may be retrieved on-demand. Consequently, using this stored data, the machine learning models 22 can provide tailored career advice and career maps that are dynamic or contemporaneous in response to the user's ongoing interests”; ¶81: “In step 214, a plurality of job opportunities, scholarships etc. are retrieved from external data sources 33 and stored in the backed-end database storage 20. The stored user data is retrieved using the user token, in step 216. Next, in step 218, the system matches the user to jobs, scholarships etc., based on the interests, skills, activities etc. To match users with job or career opportunities, the system 10 may exchange data with external data sources 33 e.g., LinkedIn jobs etc. using API's such as Rapid API to dynamically capture newly posted jobs, which it then matches to the users based on the ML recommendation”)
Baker and Agbonlahor are directed to the same endeavor since they are related to method/systems for recommending a career based on user profile data, job, educational history and external statistical data in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the techniques for recommending potential careers or career paths of Baker with the method/system for predicting a user career as taught by Agbonlahor since it allows for providing tailored career advice and mapping that are dynamic or contemporaneous in response to the user’s ongoing interests (¶77, ¶81).
With respect to claims 5, 12 and 19,
Baker and Agbonlahor disclose all of the above limitations, Agbonlahor further discloses,
further comprising a dynamic visualization tool configured to present users with potential future career paths, lateral transitions, and progression maps based on their current and target skills (¶69: “users provide details of their activities, projects, skills, goals, hobbies, interests, projects taken, courses, etc., using a front-end user interface (UI) 30. In addition, user data may be scraped from the Internet and inputted to the machine learning (ML) models 22 or stored in storage backend 20. The ML models 22 use the user input data to provide an output associated with a potential career or job back to the front-end user interface (UI) 30”; ¶70: “the user interacts with the front-end (UI) 30 and the information is sent to the storage backend 20 (to be used by the ML models 22 in the future). In another workflow, the data collected from the front end (UI) 30 is sent to the ML models 22 to generate information such as career matches, skills, industry tags, resume work blocks”; ¶77: “he one or more trained machine learning (ML) models 22 generate artifacts and tags such as related skills, tools, related industries, and resume work blocks. Furthermore, these artifacts can be tagged with one or more projects to which they are related and stored against the user. Accordingly, for every user, the history of the user's skills, projects, industries, professional and non-professional activities over time (weeks, months, years, etc.) may be retrieved on-demand. Consequently, using this stored data, the machine learning models 22 can provide tailored career advice and career maps that are dynamic or contemporaneous in response to the user's ongoing interests. Since the user data is captured over extended periods of time, the possible career options and their percentage match to these careers, can be predicted at any time. As the captured user data evolves, so do the model predictions, hence the user can map their career paths over time even as their interests evolve”; ¶90: “FIGS. 10a-b show example user interfaces 500 showing a “STAR” based user capture achievement; FIGS. 10c-d show example user interfaces 502 showing mapping of user capture achievement to projects, school activities, etc.; and FIGS. 10e-f show example user interfaces 504 showing top career matches”)
Baker and Agbonlahor are directed to the same endeavor since they are related to method/systems for recommending a career based on user profile data, job, educational history and external statistical data in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the techniques for recommending potential careers or career paths of Baker with the method/system for predicting a user career as taught by Agbonlahor since it allows for providing output associated with a potential career or job to a user; illustrating and mapping of user achievements, career matches, predictions and the like (Figs 1, 4, 10a-10f, ¶69, ¶70, ¶77, ¶90-93).
Claims 6, 7, 13, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Baker, Agbonlahor in further view of Jagadeesan et al., (WO 2023177437 A1)
With respect to claims 6 and 13,
Baker and Agbonlahor disclose all of the above limitations, the combination of Baker and Agbonlahor does not distinctly describe the following limitations, but Jagadeesan however as shown discloses,
wherein the database storing the plurality of modular career pathways comprises a tiered career hierarchy structure including eight broad categories that group careers based on common skills, interests, and functional areas (Fig 4-Fig. 7, page 3, line13: FIG. 6 is a schematic view of a user interface including skills-related search results according to embodiments described herein; page 7, lines 11-20: “ “skills ontology” refers to the process of defining and measuring relationships between skills. In particular, skills ontology can be used to aid in the generation of skills graphs that define relationships between skills. Stated more broadly, skills ontology may be viewed as the common language of skills with respect to a particular industry or tenant… The term “skills taxonomy” refers to a hierarchical system of skill classification that can categorize and organize skills in groups or clusters. Generally speaking, skills taxonomy takes the form of a tree, while skills ontology takes the form of a graph”; page 9, lines 19-33: “a centralized skills management system that enables skills inference within the context of a global skills graph and a tenant-specific property graph that encompasses diverse tenant data sources (e.g., content, documents, positions, user profiles, learning paths, and the like), as well as multiple providers within the tenant’s ecosystem. More specifically, the skills inference techniques described herein are enabled via the consistent, standardized skill tagging of data objects obtained from such diverse tenant data sources and providers. Moreover, such skills inference techniques can be used to provide skills-related search and recommendation functionalities at the level of individual users associated with the particular tenant. Such functionalities can be provided both in the flow of work and outside the flow of work, e.g., using standard tenant-specific data objects generated during the normal course of work and nonstandard tenant-specific data objects generated outside the normal course of work. In this manner, the present techniques enable the surfacing of simple, intuitive, and holistic skills- related employee experiences that span across diverse tenant-level data sources and providers”; page 9, line 32- page 10 line 11: “the centralized skills management system provides a centralized skills ontology that is continuously updated and maintained and is highly relevant to a large number of different tenants and industries. As another example, the centralized skills management system can be automatically utilized by tenants with very little manual intervention. As another example, the architecture behind the centralized skills management system is readily scalable to large enterprise customers with diverse resources that are distributed across multiple HR solutions or providers. As another example, new tenants can be quickly integrated into the centralized skills management system, providing fast and efficient positive outcomes for such tenants. As another example, the centralized skills management system is designed to adapt with the ever-evolving skills market, with the expectation that skills will be continuously added and/or modified. As yet another example, the centralized skills management system connects resources and data across various providers to enable individual employees, as well as organizations as a whole, to seamlessly pursue their skills-related goals without accessing multiple fragmented systems”; page 10, lines 30-33: “ tenant-level data objects are modified to include standardized skills tags within the object metadata, search results may automatically surface data objects associated with the skills-related search terms, in addition to the data objects that are matched based on titles, descriptions, and the like”; page 11, lines 3-14: “the centralized skills management system provides a skills inference functionality that enables integrated skills-driven experiences across the tenant’s entire ecosystem. This is achieved, at least in part, by providing systems and methods for skills inference within the context of a global skills graph and a tenant-specific property graph. More specifically, such skills inference is enabled, at least in part, by the standardized skill tagging of tenant-level data objects using skills-related terms that define a universal skills language. Such standardized skill tagging is enabled using skills obtained from the global skills graph in combination with data objects obtained from the tenant-specific property graph, which includes content relating to a large number of diverse users and providers (e.g., application service providers) relating to the particular tenant. One non-limiting example of a suitable global skills graph is the Linkedln® Skills Graph, which is maintained by Linkedln Corporation. The Linkedln® Skills Graph is a global skills graph that forms a large-scale skills taxonomy with over 36,000 skills”; page 32, lines 10-32: “systems and components can include those components or specified sub-components, some of the specified components or sub-components, and additional components, and according to various permutations and combinations of the foregoing. Sub-components can also be implemented as components communicatively coupled to other components rather than included within parent components (hierarchical)”).
Jagadeesan teaches a centralized skills management system that enables skills inference within the context of a global skills graph and a tenant-specific property graph that encompasses diverse tenant data sources (e.g., content, documents, positions, user profiles, learning paths, and the like), as well as multiple providers within the tenant’s ecosystem. Jagadeesan also discloses a centralized skills ontology that is continuously updated and maintained and is highly relevant to a large number of different tenants and industries.
A person of ordinary skill in the art before the effective filing date of applicant’s invention would have been motivated to modify the centralized skills management system as taught by Jagadeesan to achieve the claimed invention (a tiered career hierarchy structure including eight broad categories that group careers based on common skills, interests, and functional areas) and there would have been a reasonable amount of success in doing so. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d1641, 1645 (Fed. Cir. 2006). The known techniques for standardized skill tagging using skills obtained from a global skills graph would have predictably resulted in a skills ontology to aid in the generation defining relationships between skills and a particular industry or tenant (Fig 4-Fig. 7, page 3, line13; page 7, lines 11-20; page 9, lines 19-33, page 11, lines 3-14, page 32, lines 10-15).
Baker, Agbonlahor and Jagadeesan are directed to the same endeavor since they are related to method/systems for recommending a career based on user profile data, job, educational history and external statistical data in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the techniques for recommending potential careers or career paths of Baker with the method/system for predicting a user career of Agbonlahor and the centralized skills management system as taught by Jagadeesan since it allows for connecting resources and data across various providers to enable individual employees, as well as organizations as a whole, to seamlessly pursue their skills-related goals without accessing multiple fragmented systems ((Fig 4-Fig. 7, page 3, line13; page 7, lines 11-20; page 9, lines 19-33, page 11, lines 3-14, page 32, lines 10-15).
With respect to claims 7 and 14,
Baker, Agbonlahor and Jagadeesan disclose all of the above limitations, Jagadeesan further discloses,
wherein each of the eight broad categories further comprises multiple subcategories organized based on specific professional domains or specializations that fall under the broader functional area of each primary category (Fig 4-Fig. 7, page 3, line13: FIG. 6 is a schematic view of a user interface including skills-related search results according to embodiments described herein; page 7, lines 11-20: “ “skills ontology” refers to the process of defining and measuring relationships between skills. In particular, skills ontology can be used to aid in the generation of skills graphs that define relationships between skills. Stated more broadly, skills ontology may be viewed as the common language of skills with respect to a particular industry or tenant… The term “skills taxonomy” refers to a hierarchical system of skill classification that can categorize and organize skills in groups or clusters. Generally speaking, skills taxonomy takes the form of a tree, while skills ontology takes the form of a graph”; page 9, lines 19-33: “a centralized skills management system that enables skills inference within the context of a global skills graph and a tenant-specific property graph that encompasses diverse tenant data sources (e.g., content, documents, positions, user profiles, learning paths, and the like), as well as multiple providers within the tenant’s ecosystem. More specifically, the skills inference techniques described herein are enabled via the consistent, standardized skill tagging of data objects obtained from such diverse tenant data sources and providers. Moreover, such skills inference techniques can be used to provide skills-related search and recommendation functionalities at the level of individual users associated with the particular tenant. Such functionalities can be provided both in the flow of work and outside the flow of work, e.g., using standard tenant-specific data objects generated during the normal course of work and nonstandard tenant-specific data objects generated outside the normal course of work. In this manner, the present techniques enable the surfacing of simple, intuitive, and holistic skills- related employee experiences that span across diverse tenant-level data sources and providers”; page 9, line 32- page 10 line 11: “the centralized skills management system provides a centralized skills ontology that is continuously updated and maintained and is highly relevant to a large number of different tenants and industries. As another example, the centralized skills management system can be automatically utilized by tenants with very little manual intervention. As another example, the architecture behind the centralized skills management system is readily scalable to large enterprise customers with diverse resources that are distributed across multiple HR solutions or providers. As another example, new tenants can be quickly integrated into the centralized skills management system, providing fast and efficient positive outcomes for such tenants. As another example, the centralized skills management system is designed to adapt with the ever-evolving skills market, with the expectation that skills will be continuously added and/or modified. As yet another example, the centralized skills management system connects resources and data across various providers to enable individual employees, as well as organizations as a whole, to seamlessly pursue their skills-related goals without accessing multiple fragmented systems”; page 11, lines 3-14: “the centralized skills management system provides a skills inference functionality that enables integrated skills-driven experiences across the tenant’s entire ecosystem. This is achieved, at least in part, by providing systems and methods for skills inference within the context of a global skills graph and a tenant-specific property graph. More specifically, such skills inference is enabled, at least in part, by the standardized skill tagging of tenant-level data objects using skills-related terms that define a universal skills language. Such standardized skill tagging is enabled using skills obtained from the global skills graph in combination with data objects obtained from the tenant-specific property graph, which includes content relating to a large number of diverse users and providers (e.g., application service providers) relating to the particular tenant. One non-limiting example of a suitable global skills graph is the Linkedln® Skills Graph, which is maintained by Linkedln Corporation. The Linkedln® Skills Graph is a global skills graph that forms a large-scale skills taxonomy with over 36,000 skills. The skills within the Linkedln® Skills Graph are defined, updated, and maintained within the context of Linkedln’ s professional networking environment, which supports over 24 million job postings and over 740 million members”; page 32, lines 10-32: “systems and components can include those components or specified sub-components, some of the specified components or sub-components, and additional components, and according to various permutations and combinations of the foregoing. Sub-components can also be implemented as components communicatively coupled to other components rather than included within parent components (hierarchical)”)
Jagadeesan teaches a centralized skills management system that enables skills inference within the context of a global skills graph and a tenant-specific property graph that encompasses diverse tenant data sources (e.g., content, documents, positions, user profiles, learning paths, and the like), as well as multiple providers within the tenant’s ecosystem. Jagadeesan also discloses a centralized skills ontology that is continuously updated and maintained and is highly relevant to a large number of different tenants and industries.
Baker, Agbonlahor and Jagadeesan are directed to the same endeavor since they are related to method/systems for recommending a career based on user profile data, job, educational history and external statistical data in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the techniques for recommending potential careers or career paths of Baker with the method/system for predicting a user career of Agbonlahor and the centralized skills management system as taught by Jagadeesan since it allows for continuous, consistent, standardized skill tagging of data objects obtained from diverse tenant-level data sources and providers (Fig 4-Fig. 7, page 3, line13; page 7, lines 11-20; page 9, lines 19-33, page 11, lines 3-14, page 32, lines 10-15).
With respect to claim 20,
Baker and Agbonlahor disclose all of the above limitations, the combination of Baker and Agbonlahor does not distinctly describe the following limitations, but Jagadeesan however as shown discloses,
wherein the dynamic visualization includes a tiered career hierarchy structure comprising eight broad categories that group careers based on common skills, interests, and functional areas (Fig 4-Fig. 7, page 3, line13: FIG. 6 is a schematic view of a user interface including skills-related search results according to embodiments described herein; page 7, lines 11-20: “skills ontology” refers to the process of defining and measuring relationships between skills. In particular, skills ontology can be used to aid in the generation of skills graphs that define relationships between skills. Stated more broadly, skills ontology may be viewed as the common language of skills with respect to a particular industry or tenant… The term “skills taxonomy” refers to a hierarchical system of skill classification that can categorize and organize skills in groups or clusters. Generally speaking, skills taxonomy takes the form of a tree, while skills ontology takes the form of a graph”; page 9, lines 19-33: “a centralized skills management system that enables skills inference within the context of a global skills graph and a tenant-specific property graph that encompasses diverse tenant data sources (e.g., content, documents, positions, user profiles, learning paths, and the like), as well as multiple providers within the tenant’s ecosystem. More specifically, the skills inference techniques described herein are enabled via the consistent, standardized skill tagging of data objects obtained from such diverse tenant data sources and providers. Moreover, such skills inference techniques can be used to provide skills-related search and recommendation functionalities at the level of individual users associated with the particular tenant. Such functionalities can be provided both in the flow of work and outside the flow of work, e.g., using standard tenant-specific data objects generated during the normal course of work and nonstandard tenant-specific data objects generated outside the normal course of work. In this manner, the present techniques enable the surfacing of simple, intuitive, and holistic skills- related employee experiences that span across diverse tenant-level data sources and providers”; page 9, line 32- page 10 line 11: “the centralized skills management system provides a centralized skills ontology that is continuously updated and maintained and is highly relevant to a large number of different tenants and industries. As another example, the centralized skills management system can be automatically utilized by tenants with very little manual intervention. As another example, the architecture behind the centralized skills management system is readily scalable to large enterprise customers with diverse resources that are distributed across multiple HR solutions or providers. As another example, new tenants can be quickly integrated into the centralized skills management system, providing fast and efficient positive outcomes for such tenants. As another example, the centralized skills management system is designed to adapt with the ever-evolving skills market, with the expectation that skills will be continuously added and/or modified. As yet another example, the centralized skills management system connects resources and data across various providers to enable individual employees, as well as organizations as a whole, to seamlessly pursue their skills-related goals without accessing multiple fragmented systems”; page 10, lines 30-33: “ tenant-level data objects are modified to include standardized skills tags within the object metadata, search results may automatically surface data objects associated with the skills-related search terms, in addition to the data objects that are matched based on titles, descriptions, and the like”; page 11, lines 3-14: “the centralized skills management system provides a skills inference functionality that enables integrated skills-driven experiences across the tenant’s entire ecosystem. This is achieved, at least in part, by providing systems and methods for skills inference within the context of a global skills graph and a tenant-specific property graph. More specifically, such skills inference is enabled, at least in part, by the standardized skill tagging of tenant-level data objects using skills-related terms that define a universal skills language. Such standardized skill tagging is enabled using skills obtained from the global skills graph in combination with data objects obtained from the tenant-specific property graph, which includes content relating to a large number of diverse users and providers (e.g., application service providers) relating to the particular tenant. One non-limiting example of a suitable global skills graph is the Linkedln® Skills Graph, which is maintained by Linkedln Corporation. The Linkedln® Skills Graph is a global skills graph that forms a large-scale skills taxonomy with over 36,000 skills”; page 32, lines 10-32: “systems and components can include those components or specified sub-components, some of the specified components or sub-components, and additional components, and according to various permutations and combinations of the foregoing. Sub-components can also be implemented as components communicatively coupled to other components rather than included within parent components (hierarchical)”)
each category further comprises multiple subcategories organized based on specific professional domains or specializations (Fig 4-Fig. 7, page 3, line13: FIG. 6 is a schematic view of a user interface including skills-related search results according to embodiments described herein; page 7, lines 11-20: “ “skills ontology” refers to the process of defining and measuring relationships between skills. In particular, skills ontology can be used to aid in the generation of skills graphs that define relationships between skills. Stated more broadly, skills ontology may be viewed as the common language of skills with respect to a particular industry or tenant… The term “skills taxonomy” refers to a hierarchical system of skill classification that can categorize and organize skills in groups or clusters. Generally speaking, skills taxonomy takes the form of a tree, while skills ontology takes the form of a graph”; page 9, lines 19-33: “a centralized skills management system that enables skills inference within the context of a global skills graph and a tenant-specific property graph that encompasses diverse tenant data sources (e.g., content, documents, positions, user profiles, learning paths, and the like), as well as multiple providers within the tenant’s ecosystem. More specifically, the skills inference techniques described herein are enabled via the consistent, standardized skill tagging of data objects obtained from such diverse tenant data sources and providers. Moreover, such skills inference techniques can be used to provide skills-related search and recommendation functionalities at the level of individual users associated with the particular tenant. Such functionalities can be provided both in the flow of work and outside the flow of work, e.g., using standard tenant-specific data objects generated during the normal course of work and nonstandard tenant-specific data objects generated outside the normal course of work. In this manner, the present techniques enable the surfacing of simple, intuitive, and holistic skills- related employee experiences that span across diverse tenant-level data sources and providers”; page 9, line 32- page 10 line 11: “the centralized skills management system provides a centralized skills ontology that is continuously updated and maintained and is highly relevant to a large number of different tenants and industries. As another example, the centralized skills management system can be automatically utilized by tenants with very little manual intervention. As another example, the architecture behind the centralized skills management system is readily scalable to large enterprise customers with diverse resources that are distributed across multiple HR solutions or providers. As another example, new tenants can be quickly integrated into the centralized skills management system, providing fast and efficient positive outcomes for such tenants. As another example, the centralized skills management system is designed to adapt with the ever-evolving skills market, with the expectation that skills will be continuously added and/or modified. As yet another example, the centralized skills management system connects resources and data across various providers to enable individual employees, as well as organizations as a whole, to seamlessly pursue their skills-related goals without accessing multiple fragmented systems”; page 11, lines 3-14: “the centralized skills management system provides a skills inference functionality that enables integrated skills-driven experiences across the tenant’s entire ecosystem. This is achieved, at least in part, by providing systems and methods for skills inference within the context of a global skills graph and a tenant-specific property graph. More specifically, such skills inference is enabled, at least in part, by the standardized skill tagging of tenant-level data objects using skills-related terms that define a universal skills language. Such standardized skill tagging is enabled using skills obtained from the global skills graph in combination with data objects obtained from the tenant-specific property graph, which includes content relating to a large number of diverse users and providers (e.g., application service providers) relating to the particular tenant. One non-limiting example of a suitable global skills graph is the Linkedln® Skills Graph, which is maintained by Linkedln Corporation. The Linkedln® Skills Graph is a global skills graph that forms a large-scale skills taxonomy with over 36,000 skills. The skills within the Linkedln® Skills Graph are defined, updated, and maintained within the context of Linkedln’ s professional networking environment, which supports over 24 million job postings and over 740 million members”; page 32, lines 10-32: “systems and components can include those components or specified sub-components, some of the specified components or sub-components, and additional components, and according to various permutations and combinations of the foregoing. Sub-components can also be implemented as components communicatively coupled to other components rather than included within parent components (hierarchical)”)
Jagadeesan teaches a centralized skills management system that enables skills inference within the context of a global skills graph and a tenant-specific property graph that encompasses diverse tenant data sources (e.g., content, documents, positions, user profiles, learning paths, and the like), as well as multiple providers within the tenant’s ecosystem. Jagadeesan also discloses a centralized skills ontology that is continuously updated and maintained and is highly relevant to a large number of different tenants and industries.
Baker, Agbonlahor and Jagadeesan are directed to the same endeavor since they are related to method/systems for recommending a career based on user profile data, job, educational history and external statistical data in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the techniques for recommending potential careers or career paths of Baker with the method/system for predicting a user career of Agbonlahor and the centralized skills management system as taught by Jagadeesan since it allows for continuous, consistent, standardized skill tagging of data objects obtained from diverse tenant-level data sources and providers (Fig 4-Fig. 7, page 3, line13; page 7, lines 11-20; page 9, lines 19-33, page 11, lines 3-14, page 32, lines 10-15).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Blankenship et. al., US Patent Application Publication No US 2026/0030706 A1, “Dynamic Recommendation Generation Using Statistical and Artificial Intelligence Modelling”, relating to generating recommendations and personalized learning paths.
Gupta et al., US Patent Application Publication No US 2018/0307750 A1, “Opportunity Network System for Providing Career Insights by Determining Potential next Positions and a Degree of Match to a Potential next Position”, relating to big data analytics and machine learning applied to the field of identifying and suggesting career opportunities, and more particularly to an opportunity network system and method for providing career insights by determining potential next positions and a degree of match to a potential next position to users such as professionals and students.
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/KIMBERLY L EVANS/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
