DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The following is a final office action.
Claims [1-2, 4, 6-7, 9-10, and 13-25] are currently pending and have been examined on their merits.
Claims 1 and 15 are currently amended see REMARKS January 21, 2026.
Claim 7 and 16 in newly cancelled see REMARKS January 21, 2026
Double Patenting (Non-Statutory)
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-2, 4, 6, 9-10, 13-15, and 17-25 of Application No. 18/642186 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of Patent No. 11995612.
Although the conflicting claims are not identical, they are not patentably distinct from each other because the application (18/642186) is directed to a common and substantially similar subject matter claimed of the patent (11995612).
Independent claims 1 and 15 of the instant application ’186 are a broader version of the claims 1, 3, and 13 of patent 11995612. One of ordinary skill in the art would have recognized that the method of receiving a request for potential candidates for a job, identifying using a knowledge engine a plurality of candidates, associated each knowledge entity with at least a plurality of categories, determine a weighted relationship between each of the plurality of candidates and the job, updating the knowledge engine, generating a list of ranked candidates for the job, and sending a response to the request for the potential candidates for the job disclosed in claim 1 and the method of receiving a request from an operator for an applicant that applied to a job posting of a company, retrieving a knowledge profile associated with the applicant, identifying a candidate that has not applied to the job posting based on a weighted relationship between a knowledge profile associated with the candidate and one or more knowledge profiles associated with the applicant, and sending a response to the operator based on the request as disclosed by claim 15 are substantially similar to the method of receiving a request for a potential candidate for a job, sending a response to the request for the potential candidate for the job, receiving the identity associated with the operator, retrieving a weighted knowledge graph associated with the job, and employer, determining a plurality of candidates for the job based on the weighted knowledge graph associated with each of the plurality of candidates, the employer, and the job, determining a weighted relationship between each of the plurality of candidates, generating a list of ranked candidates of the job, and sending a response to the request for the potential candidate for the job as disclosed by claims 10, 20, and 39 of the prior application.
Thus, the conflicting claims are not patentably distinct from each other since the same inventive concept is being claimed twice utilizing slightly alternative/obvious language wherein the instant claims 1 and 15 of the ‘186 application are broader in scope and is anticipated by and/or obvious over the claims 1, 3, and 13 of patent 11995612
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to recognize that the system and method recited in claims 1 and 15 of application 18/642186 are an obvious variant of the method recited in claims 1, 3, and 13 of patent 11995612. Thus, the conflicting claims and their respective dependent claims are not patentably distinct from each other since substantially similar inventive features are being claimed in both the instant application and the former application utilizing equivalent and/or obvious variants of each other.
Therefore, claims 1-2, 4, 6, 9-10, 13-15, and 17-25 are rejected under Double patenting.
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 1-2, 4, 6, 9-10, 13-15, and 17-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception that is an abstract idea without a practical application or significantly more.
Step 1: Claims 1-2, 4, 6, 9-10, 13-15, and 17-25 recite a method (i.e. a process such as an act or series of steps) and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claim 1 recites: A method comprising: receiving a request for potential candidates for a job of a company; identifying, a plurality of candidates for the job based on a weighted relationship of a knowledge graph, the weighted relationship of the knowledge graph comprising a weighted relationship between a knowledge graph associated with each of the plurality of candidates, a knowledge graph associated with the job, and a knowledge graph associated with the company; extracting at least one knowledge entity relating to each of the plurality of candidates from a plurality of data files; associating each of the knowledge entities with at least one of a plurality of categories; determining, a weighted relationship between each of the plurality of candidates and the job, wherein the determined weighted relationships are each associated with a relationship strength, a relationship type, and a relationship direction; generating, a list of ranked candidates for the job and a respective numerical score for each of the ranked candidates; and sending a response to the request for the potential candidate for the job, wherein the response displays the generated list of ranked candidates, the respective numerical score for each of the ranked candidates in the list, and an indication of whether each candidate of the ranked candidates in the list applied for the job.
Claim 15: A method comprising: receiving a request from an operator for an applicant that applied to a job posting of a company; retrieving a knowledge profile associated with the applicant, a knowledge profile associated with the job posting, and a knowledge profile associated with the company; determining a weighted relationship between a knowledge profile associated with the applicant, a knowledge profile associated with the company, and one or more of the knowledge profiles associated with the applicant of the job posting; identifying a candidate that has not applied to the job posting from a passive interaction of the candidate and based on the weighted relationship between the knowledge profile associated with the candidate, the knowledge profile associated with the company, and the one or more of the knowledge profiles associated with the applicant, the job posting, wherein the weighted relationship is associated with a relationship strength, a relationship type, and a relationship direction, and wherein the candidate had previously applied for another job posting of the company; and sending a response to the operator based on the request, wherein the response, an indication of the applicant to the job posting and the candidate that has not applied to the job posting.
The claims recite a certain method of organizing human activity. The claims recite a certain method of organizing human activity as the disclosure is directed to managing personal behavior or relationships or interactions between people. The claims merely recite a method for receiving a request for potential job candidates, retrieving knowledge profile information associated with an applicant, a job posting, and a company, and identifying candidates that have not applied to a job based on a relationship of a user and a candidate. Merely generating a list of potential job applicants for a position based on a plurality of information such weighted knowledge graph of job candidates, a job opening, and a company is an abstract idea. As individuals such as recruiters routinely perform these series of steps to identify a list of potential job applicants for a position.
Alternatively, the claims recite a mental process. The examiner finds the claims to be similar to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis." The claims merely recite a method for creating and updating a knowledge graph associated with a plurality of job candidates and a job position and comparing the information pertaining to the candidates and position to determine a ranking of job candidates to present to a user such as a recruiter. Therefore, the examiner finds the claims to be similar to examples the courts have identified as reciting a mental process including observations, evaluations, judgements, and opinions. As the method of receiving a request, identifying a plurality of candidates for a job, associating knowledge entities with a category, determining a weighted relationship, and generating a list of ranked candidates are all functions that can be performed in the mind of an individual such as a recruiter seeking to identify potential job candidates for a position.
Therefore, the examiner finds the claims to be directed to an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: at least one machine learning model of a knowledge engine, updating the knowledge engine based on the determined weighted relationship between each of the plurality of candidates and the job, and a display.
Claim 15: at least one machine learning model of a knowledge engine and a display.
The additional element of using generic computer elements to perform the abstract idea are directed to merely applying the known use of a computer to store and execute the method in the recited claim limitations. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, 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. As the claims are merely directed to utilizing a generic computer to perform the abstract idea of receiving a request, identifying a plurality of candidates, extracting information, associating information, determining a weighted information, updating a knowledge engine, generating a list of ranked candidates, and sending a response to the request.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The claims merely recite using generic computer elements and generic knowledge engine elements to perform the abstract idea. Therefore, the additional elements do not amount to significantly more as they do not recite any improvements to a technology or technical field.
Dependent claims 2, 4, 6, 9-10, 13-14, 23-24 further narrow the abstract idea of generating a ranked list of candidates for a job.
Dependent claims 17-21 and 25 further narrow the abstract idea of identifying a candidates that has not applied to a job posting based on a weighted relationship between a candidate and one or more other associated applicants.
The dependent claims recite the following additional elements:
Claim 2: updating the knowledge engine.
However, the additional elements are directed to merely “apply it” or applying generic computer elements to perform the abstract idea.
Therefore, claims 1-2, 4, 6, 9-10, 13-15, and 17-25 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-2, 4, 6, 9-10, 13-15, and 17-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Daly (US 2014/0278633) in view of Kenthapadi (US 2019/0066054) further in view of Hardtke (US 2014/0122355).
Claim 1: Daly discloses A method comprising: receiving a request for potential candidates for a job of a company (Paragraph [0015-0018]; [0024-0025]; [0047]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. A user interface that enables a user to enter candidate search criteria to submit a search for candidates matching the provided search criteria),
identifying, using at least one machine learning model of a knowledge engine, a plurality of candidates for the job based on a weighted relationship of a knowledge graph, the weighted relationship of the knowledge graph comprising a weighted relationship between a knowledge graph associated with each of the plurality of candidates, a knowledge graph associated with the job, and a knowledge graph associated with the company (Paragraph [0015-0018]; [0021-0025]; [0043-0048]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals (The examiner notes that the broadest reasonable interpretation of a knowledge graph associated with a candidate, job, and or company would include a profile including information pertaining to each entity such as skills, requirements, compensation benefits, etc. As the specification states “a knowledge engine may create a knowledge graph that may be directed or undirected (e.g., or profile)… The knowledge engine may receive a data file. The data file may comprise one or more knowledge entities… The knowledge entities may comprise a word and/or a group of words… The knowledge engine may associated each of the knowledge entities with at least one of a plurality of categories… For example, the plurality of categories may include a technical skills category… The knowledge engine may determine one or more weighted dependencies (e.g. or relationships) between each of the knowledge entities and their associated category… The knowledge engine may generate the knowledge graph using the knowledge entities and the weighted dependencies” (see Specification [0011-0012]). Therefore, the examiner finds that a knowledge graph is merely a profile for a job, company, or candidate comprising a series of terms grouped into categories such as skills, requirements, job title etc.). A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings. The skill generator module ranks the potential candidates based at least in part on the determined match scores. The skill generator module may apply any search filters to the resulting list of search results. The resulting list of the top ranked candidate who meet the minimum criteria may be provided to the searcher. In some embodiments, the skill generator module may generate an automatic connection request between the searcher and one or more of the top-ranking candidates. The skill generator module determines a match score for at least a subset of individuals for whom records were retrieved. The match score may be determined based at least in part by comparing the received mandatory skills, skill category, and/or optional skills with reach retrieved record (e.g. each resume and/or user profile). In some embodiments, the match score for a given record may be based in part on various weights applied to each element of the provided search criteria (The examiner notes that the broadest reasonable interpretation of a weighted relationship between knowledge graphs would include a weighted match score between information pertaining to entities such as a user’s resume, company details, and job requirements));
extracting at least one knowledge entity relating to each of the plurality of candidates from a plurality of data files (Paragraph [0033-0035]; [0042]; Figs. 5 and 7, the user may select a business area and skill category associated with their resume. The available options for skill category in some embodiments may be based on industry standard terms and/or terms recognized by an organization. A skill generator module retrieves profile information and/or resume records for a number of individuals form a data store, such as user data. In some embodiments, the skill generator module may additionally or alternatively maintain a profile for each user that includes skill information, skill category information, job history, and/or other information);
associating each of the knowledge entities with at least one of a plurality of categories (Paragraph [0033-0035]; [0042]; Figs. 5 and 7, the user may select a business area and skill category associated with their resume. The available options for skill category in some embodiments may be based on industry standard terms and/or terms recognized by an organization. A skill generator module retrieves profile information and/or resume records for a number of individuals form a data store, such as user data. In some embodiments, the skill generator module may additionally or alternatively maintain a profile for each user that includes skill information, skill category information, job history, and/or other information);
generating, based on the updated knowledge engine, a list of ranked candidates for the job and a respective numerical score for each of the ranked candidates (Paragraph [0015-0018]; [0021-0025]; [0045-0048]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals. A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings. The skill generator module ranks the potential candidates based at least in part on the determined match scores. The skill generator module may apply any search filters to the resulting list of search results. The resulting list of the top ranked candidate who meet the minimum criteria may be provided to the searcher. In some embodiments, the skill generator module may generate an automatic connection request between the searcher and one or more of the top-ranking candidates);
and sending a response to the request for the potential candidate for the job, wherein the response displays, on a display, the generated list of ranked candidates and the respective numerical score for each of the ranked candidates in the list (Paragraph [0013-0014]; [0043-0046]; Figs. 7 and 9, according to one embodiment of the present disclosure, a user interface may be presented that enables a recruiter to enter ranked search criteria for a candidate. A skill search module may determine matching candidate results based on the search criteria including ranking the matches based on optional skills and/or other criteria. The skill generator module determines a match score. The match score may be determined based on comparing the mandatory skills with reach retrieved record. A skill generator module ranks the potential candidates based at least in part on the determined matched score. The resulting list of the top ranked candidates may be provided to the searcher).
Although Daly discloses all of the limitations above, including receiving a request for potential job candidates, retrieving a weighted knowledge entity associated with a job, determining a plurality of candidates for the job based on a knowledge entity, generating a list of ranked candidates and displaying the list of ranked candidates to a user, Daly does not explicitly disclose the following claim limitations: wherein the knowledge engine comprises at least one of machine learning, deep learning, or predictive modeling; determining, by the knowledge engine, a weighted relationship between each of the plurality of candidates and the job, wherein the determined weighted relationships are each associated with a relationship strength, a relationship type, and a relationship direction; updating the knowledge engine based on the determined weighted relationship between each of the plurality of candidates and the job; and an indication of whether each candidate of the ranked candidates in the list applied for the job.
Kenthapadi, in the same field of endeavor of assessing job candidate’s skills teaches wherein the knowledge engine comprises at least one of machine learning, deep learning, or predictive modeling (Paragraph [0049-0050] the graph system determines that the weight of an edge between two nodes equals the number of occurrences of the concept phrases corresponding to the nodes with the internal dataset. The graph system determines a weighted combination based on a machine-learning model that uses linear regression. The model is taught (e.g. trained) with respect to a ground truth dataset);
determining, by the knowledge engine, a weighted relationship between each of the plurality of candidates and the job, wherein the determined weighted relationships are each associated with a relationship strength, a relationship type, and a relationship direction (Paragraph [0023-0025]; [0027-0029]; Fig. 1, a universal concept graph hereinafter also as “UCG” is generated. The UCG includes a unified and standardized set of concept phrases. A graph system may construct the UCG based on combining internal concept phrases extracted from internal data assets (e.g. a set of member profiles, a set of job descriptions, and associated content). The graph system may use the UCG to determine member-job and job-member similarity score values that may facilitate the generation of one or more accurate job recommendations and talent match identifications. In various embodiments, the graph system accesses a first record of a database. The first record identifies a UCG that includes a first set of nodes and edges. The graph system accesses a second record of the database. The second record identifies a concept graph associated with a job description. The graph system accesses a third record. The third record identifies a second induced concept graph associated with a member profile);
updating the knowledge engine based on the determined weighted relationship between each of the plurality of candidates and the job (Paragraph [0034-0035]; [0040-0042]; [0087-0090]; Fig. 1, The UCG may evolve with time, as the underlying information changes over time. The graph system may periodically update the UCG to add new nodes and edges for new concept phrases and relationships among the nodes of the UCG. The graph system determines the set of relationship edges and the edge weight function by taking into account the content similarity in the internal and external datasets. In some embodiments, each edge between two nodes of the UCG is associated with an edge weight value. The edge weight value may be stored with an indicator. The edge weight value may represent the degree of relatedness between the two-concept represented by the two nodes connected by the edge).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of updating the weighted knowledge graph associated with the job and the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories, wherein each of the knowledge entities are further associated with respective weighted relationships, each of the respective weighted relationships being associated with a relationship strength and a relationship direction; determining a weighted relationship between each of the plurality of candidates and the job based on each of the respective weighted relationships as taught by Kenthapadi (Kenthapadi [0025]). With the motivation of helping to identify best potential applicants for various positions based on factors such as relationships and skills (Kenthapadi [0003]).
In the same field of endeavor of identifying potential job candidates for a job Hardtke teaches and an indication of whether each candidate of the ranked candidates in the list applied for the job (Paragraph [0009-0010]; [0069-0071]; [0103-0105]; [0132] the present technology is based on an approach in which a combination of information in a candidate’s resume, a description of the job opening, and external data about the candidate is utilized to inform a machine learning algorithm that matches job openings to candidates by calculating a score. The suitability score serves both sides of the hiring process, allowing employers to find their optimal candidates. Whenever an employer is provided with a list of candidates whose suitability scores exceed a threshold the employer is able to review the candidates profile and make decisions. In an alternative embodiment, an employer can request that scores are calculated for candidates who have already applied for a job opening. When calculating the suitability score, each feature score is weighted by a coefficient derived from an analysis of sample resumes and sample job descriptions, whose matches to one another have been ranked by individuals whose primary profession is recruiting);
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of and an indication of whether each candidate of the ranked candidates in the list applied for the job as taught by Hardtke (Hardtke [0071]). With the motivation of helping to identify candidates for a job based on comparing candidate information and job description information (Hardtke [0003]).
Claim 2: Modified Daly discloses the method as per claim 1. However, Daly does not disclose wherein the knowledge engine comprises a plurality of clusters, wherein each of the plurality of clusters is associated with one or more gradients, and wherein updating the knowledge engine comprises updating the one or more gradients associated with each of the plurality of clusters using backpropagation.
Kenthapadi, in the same field of endeavor of assessing job candidate’s skills teaches wherein the knowledge engine comprises a plurality of clusters, wherein each of the plurality of clusters is associated with one or more gradients, and wherein updating the knowledge engine comprises updating the one or more gradients associated with each of the plurality of clusters using backpropagation (Paragraph [0049-0050] the graph system determines that the weight of an edge between two nodes equals the number of occurrences of the concept phrases corresponding to the nodes with the internal dataset. The graph system determines a weighted combination based on a machine-learning model that uses linear regression. The model is taught (e.g. trained) with respect to a ground truth dataset).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of updating the weighted knowledge graph associated with the job and the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories, wherein each of the knowledge entities are further associated with respective weighted relationships, each of the respective weighted relationships being associated with a relationship strength and a relationship direction; determining a weighted relationship between each of the plurality of candidates and the job based on each of the respective weighted relationships as taught by Kenthapadi (Kenthapadi [0025]). With the motivation of helping to identify best potential applicants for various positions based on factors such as relationships and skills (Kenthapadi [0003]).
Claim 4: Modified Daly discloses the method as per claim 1. However, Daly does not disclose wherein the score for each candidate comprises a fit for each candidate, and wherein the method comprises determining the fit for each candidate in the list of ranked candidates based on the relationship strength, the relationship type, and the relationship direction.
Kenthapadi, in the same field of endeavor of assessing job candidate’s skills teaches wherein the score for each candidate comprises a fit for each candidate, and wherein the method comprises determining the fit for each candidate in the list of ranked candidates based on the relationship strength, the relationship type, and the relationship direction (Paragraph [0025]; [0034-0035]; [0040-0042]; [0087-0090]; Fig. 1, the graph system may also use the UCG to determine member-job and job-member similarity score values that may facilitate the generation of more accurate job recommendations and talent match identification. The graph system determines the set of relationship edges and the edge weight function by taking into account the content similarity in the internal and external datasets. In some embodiments, each edge between two nodes of the UCG is associated with an edge weight value. The edge weight value may be stored with an indicator. The edge weight value may represent the degree of relatedness between the two-concept represented by the two nodes connected by the edge).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of updating the weighted knowledge graph associated with the job and the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories, wherein each of the knowledge entities are further associated with respective weighted relationships, each of the respective weighted relationships being associated with a relationship strength and a relationship direction; determining a weighted relationship between each of the plurality of candidates and the job based on each of the respective weighted relationships as taught by Kenthapadi (Kenthapadi [0025]). With the motivation of helping to identify best potential applicants for various positions based on factors such as relationships and skills (Kenthapadi [0003]).
Claim 6: Modified Daly discloses the method as per claim 1. However, Daly does not disclose further comprising determining a likelihood of a personal connection between each of the ranked candidates and the job based on the relationship strength, the relationship type, and the relationship direction, wherein the response to the request for potential candidates further comprises an indication of the likelihood of the personal connection between each of the ranked candidates and the job.
Kenthapadi, in the same field of endeavor of assessing job candidate’s skills teaches further comprising determining a likelihood of a personal connection between each of the ranked candidates and the job based on the relationship strength, the relationship type, and the relationship direction, wherein the response to the request for potential candidates further comprises an indication of the likelihood of the personal connection between each of the ranked candidates and the job (Paragraph [0034-0035]; [0040-0042]; [0087-0090]; Fig. 1, The UCG may evolve with time, as the underlying information changes over time. The graph system may periodically update the UCG to add new nodes and edges for new concept phrases and relationships among the nodes of the UCG. The graph system determines the set of relationship edges and the edge weight function by taking into account the content similarity in the internal and external datasets. In some embodiments, each edge between two nodes of the UCG is associated with an edge weight value. The edge weight value may be stored with an indicator. The edge weight value may represent the degree of relatedness between the two-concept represented by the two nodes connected by the edge).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of updating the weighted knowledge graph associated with the job and the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories, wherein each of the knowledge entities are further associated with respective weighted relationships, each of the respective weighted relationships being associated with a relationship strength and a relationship direction; determining a weighted relationship between each of the plurality of candidates and the job based on each of the respective weighted relationships as taught by Kenthapadi (Kenthapadi [0025]). With the motivation of helping to identify best potential applicants for various positions based on factors such as relationships and skills (Kenthapadi [0003]).
Claim 9: Modified Daly discloses the method as per claim 1. Daly further discloses wherein weighted knowledge graph associated with the job is associated with a domain, and wherein the each of the knowledge entities are associated with at least one of the plurality of categories based on the domain (Paragraph [0015-0018]; [0021-0025]; [0045-0048]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals. A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings. The skill generator module ranks the potential candidates based at least in part on the determined match scores. The skill generator module may apply any search filters to the resulting list of search results. The resulting list of the top ranked candidate who meet the minimum criteria may be provided to the searcher. In some embodiments, the skill generator module may generate an automatic connection request between the searcher and one or more of the top-ranking candidates. The refinement options include skill category, skills, job title, education, etc.).
Claim 10: Modified Daly discloses the method as per claim 1. Daly further discloses wherein weighted knowledge graph associated with the company is associated with a domain, and wherein the each of the knowledge entities are associated with at least one of the plurality of categories based on the domain (Paragraph [0015-0018]; [0021-0025]; [0045-0048]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals. A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings. The skill generator module ranks the potential candidates based at least in part on the determined match scores. The skill generator module may apply any search filters to the resulting list of search results. The resulting list of the top ranked candidate who meet the minimum criteria may be provided to the searcher. In some embodiments, the skill generator module may generate an automatic connection request between the searcher and one or more of the top-ranking candidates. The refinement options include skill category, skills, job title, education, etc.).
Claim 13: Modified Daly discloses the method as per claim 1. Daly further disclose associating each of the knowledge entities with at least one of a plurality of categories comprises passing vectors associated with each of the plurality of data files to the knowledge engine (Paragraph [0015-0018]; [0021-0025]; [0045-0048]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals. A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings. The skill generator module ranks the potential candidates based at least in part on the determined match scores. The skill generator module may apply any search filters to the resulting list of search results. The resulting list of the top ranked candidate who meet the minimum criteria may be provided to the searcher. In some embodiments, the skill generator module may generate an automatic connection request between the searcher and one or more of the top-ranking candidates. The refinement options include skill category, skills, job title, education, etc.).
Claim 14: Modified Daly discloses the method as per claim 1. Daly further disclose further comprising updating the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories (Paragraph [0015-0018]; [0021-0025]; [0045-0048]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals. A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings. The skill generator module ranks the potential candidates based at least in part on the determined match scores. The skill generator module may apply any search filters to the resulting list of search results. The resulting list of the top ranked candidate who meet the minimum criteria may be provided to the searcher. In some embodiments, the skill generator module may generate an automatic connection request between the searcher and one or more of the top-ranking candidates. The refinement options include skill category, skills, job title, education, etc.).
Claim 15: Daly discloses a method comprising: receiving a request from an operator for an applicant that applied to a job posting of a company (Paragraph [0015-0018]; [0021-0025]; Figs. 1, 3 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals. A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings);
retrieving a knowledge profile associated with the applicant, a knowledge profile associated with the job posting, and a knowledge profile associated with the company (Paragraph [0015-0018]; [0021-0025]; [0045-0048]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals. A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings. The skill generator module ranks the potential candidates based at least in part on the determined match scores. The skill generator module may apply any search filters to the resulting list of search results. The resulting list of the top ranked candidate who meet the minimum criteria may be provided to the searcher. In some embodiments, the skill generator module may generate an automatic connection request between the searcher and one or more of the top-ranking candidates);
and sending a response to the operator based on the request, wherein the response displays, on a display, an indication of the applicant to the job posting and the candidate that has not applied to the job posting (Paragraph [0013-0014]; [0044-0046]; Fig. 7, according to one embodiment of the present disclosure, a user interface may be presented that enables a recruiter to enter ranked search criteria for a candidate. A skill search module may determine matching candidate results based on the search criteria including ranking the matches based on optional skills and/or other criteria. A skill generator module ranks the potential candidates based at least in part on the determined matched score. The resulting list of the top ranked candidates may be provided to the searcher).
Although Daly discloses all of the limitations above, including receiving a request for potential job candidates, retrieving a weighted knowledge entity associated with a job, determining a plurality of candidates for the job based on a knowledge entity, generating a list of ranked candidates and displaying the list of ranked candidates to a user, Daly does not explicitly disclose the following claim limitations: determining, by at least one machine learning model of a knowledge engine, a weighted relationship between a knowledge profile associated with the applicant, a knowledge profile associated with the company, and one or more of the knowledge profiles associated with the applicant or the job posting; identifying a candidate that has not applied to the job posting from a passive interaction of the candidate and based on a weighted relationship between a knowledge profile associated with the candidate, a knowledge profile associated with the company, and one or more of the knowledge profiles associated with the applicant or the job posting, wherein the weighted relationship is associated with a relationship strength, a relationship type, and a relationship direction.
Kenthapadi, in the same field of endeavor of assessing job candidate’s skills teaches determining, by at least one machine learning model of a knowledge engine, a weighted relationship between a knowledge profile associated with the applicant, a knowledge profile associated with the company, and one or more of the knowledge profiles associated with the applicant or the job posting (Paragraph [0023-0025]; [0027-0029]; Fig. 1, a universal concept graph hereinafter also as “UCG” is generated. The UCG includes a unified and standardized set of concept phrases. A graph system may construct the UCG based on combining internal concept phrases extracted from internal data assets (e.g. a set of member profiles, a set of job descriptions, and associated content). The graph system may use the UCG to determine member-job and job-member similarity score values that may facilitate the generation of one or more accurate job recommendations and talent match identifications. In various embodiments, the graph system accesses a first record of a database. The first record identifies a UCG that includes a first set of nodes and edges. The graph system accesses a second record of the database. The second record identifies a concept graph associated with a job description. The graph system accesses a third record. The third record identifies a second induced concept graph associated with a member profile);
identifying a candidate that has not applied to the job posting based on a weighted relationship between a knowledge profile associated with the candidate, a knowledge profile associated with the company, and one or more of the knowledge profiles associated with the applicant or the job posting, wherein the weighted relationship is associated with a relationship strength, a relationship type, and a relationship direction (Paragraph [0023-0025]; [0027-0029]; Fig. 1, a universal concept graph hereinafter also as “UCG” is generated. The UCG includes a unified and standardized set of concept phrases. A graph system may construct the UCG based on combining internal concept phrases extracted from internal data assets (e.g. a set of member profiles, a set of job descriptions, and associated content). The graph system may use the UCG to determine member-job and job-member similarity score values that may facilitate the generation of one or more accurate job recommendations and talent match identifications. In various embodiments, the graph system accesses a first record of a database. The first record identifies a UCG that includes a first set of nodes and edges. The graph system accesses a second record of the database. The second record identifies a concept graph associated with a job description. The graph system accesses a third record. The third record identifies a second induced concept graph associated with a member profile).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of updating the weighted knowledge graph associated with the job and the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories, wherein each of the knowledge entities are further associated with respective weighted relationships, each of the respective weighted relationships being associated with a relationship strength and a relationship direction; determining a weighted relationship between each of the plurality of candidates and the job based on each of the respective weighted relationships as taught by Kenthapadi (Kenthapadi [0025]). With the motivation of helping to identify best potential applicants for various positions based on factors such as relationships and skills (Kenthapadi [0003]).
In the same field of endeavor of identifying potential job candidates for a job Hardtke teaches identifying a candidate that has not applied to the job posting from a passive interaction of the candidate (Paragraph [0009-0010]; [0069-0071]; [0103-0105]; [0132] the present technology is based on an approach in which a combination of information in a candidate’s resume, a description of the job opening, and external data about the candidate is utilized to inform a machine learning algorithm that matches job openings to candidates by calculating a score. The suitability score serves both sides of the hiring process, allowing employers to find their optimal candidates. Whenever an employer is provided with a list of candidates whose suitability scores exceed a threshold the employer is able to review the candidates profile and make decisions. In an alternative embodiment, an employer can request that scores are calculated for candidates who have already applied for a job opening. When calculating the suitability score, each feature score is weighted by a coefficient derived from an analysis of sample resumes and sample job descriptions, whose matches to one another have been ranked by individuals whose primary profession is recruiting);
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of identifying a candidate that has not applied to the job posting from a passive interaction of the candidate as taught by Hardtke (Hardtke [0071]). With the motivation of helping to identify candidates for a job based on comparing candidate information and job description information (Hardtke [0003]).
Claim 16: Modified Daly discloses the method as per claim 15. However, Daly does not disclose wherein the candidate had previously applied for another job posting of the company, and wherein the job posting and the other job posting are of a similar scope.
Kenthapadi, in the same field of endeavor of assessing job candidate’s skills teaches wherein the candidate had previously applied for another job posting of the company, and wherein the job posting and the other job posting are of a similar scope (Paragraph [0025]; [0034-0035]; Fig. 1, the graph system may also use the UCG to determine member job and job member similarity score values that facilitate the generating of more accurate job recommendations and talent match identification).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of updating the weighted knowledge graph associated with the job and the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories, wherein each of the knowledge entities are further associated with respective weighted relationships, each of the respective weighted relationships being associated with a relationship strength and a relationship direction; determining a weighted relationship between each of the plurality of candidates and the job based on each of the respective weighted relationships as taught by Kenthapadi (Kenthapadi [0025]). With the motivation of helping to identify best potential applicants for various positions based on factors such as relationships and skills (Kenthapadi [0003]).
Claim 17: Modified Daly discloses the method as per claim 15. However, Daly does not disclose wherein the knowledge profile associated with the applicant comprises a plurality of knowledge entities associated with the applicant, the knowledge profile associated with the job posting comprises a plurality of knowledge entities associated with the job posting, the knowledge profile associated with the company comprises a plurality of knowledge entities associated with the company, and the knowledge profile associated with the candidate comprises a plurality of knowledge entities associated with the candidate.
Kenthapadi, in the same field of endeavor of assessing job candidate’s skills teaches wherein the knowledge profile associated with the applicant comprises a plurality of knowledge entities associated with the applicant, the knowledge profile associated with the job posting comprises a plurality of knowledge entities associated with the job posting, the knowledge profile associated with the company comprises a plurality of knowledge entities associated with the company, and the knowledge profile associated with the candidate comprises a plurality of knowledge entities associated with the candidate (Paragraph [0034-0035]; [0040-0042]; [0087-0090]; Fig. 1, The UCG may evolve with time, as the underlying information changes over time. The graph system may periodically update the UCG to add new nodes and edges for new concept phrases and relationships among the nodes of the UCG. The graph system determines the set of relationship edges and the edge weight function by taking into account the content similarity in the internal and external datasets. In some embodiments, each edge between two nodes of the UCG is associated with an edge weight value. The edge weight value may be stored with an indicator. The edge weight value may represent the degree of relatedness between the two-concept represented by the two nodes connected by the edge).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of updating the weighted knowledge graph associated with the job and the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories, wherein each of the knowledge entities are further associated with respective weighted relationships, each of the respective weighted relationships being associated with a relationship strength and a relationship direction; determining a weighted relationship between each of the plurality of candidates and the job based on each of the respective weighted relationships as taught by Kenthapadi (Kenthapadi [0025]). With the motivation of helping to identify best potential applicants for various positions based on factors such as relationships and skills (Kenthapadi [0003]).
Claim 18: Modified Daly discloses the method as per claim 15. However, Daly does not disclose further comprising: receiving a request from the candidate to access the job posting; and determining a relationship strength associated with a weighted relationship between the knowledge profile associated with the candidate and the knowledge profile associated with the job posting based on the request to access the job posting.
Kenthapadi, in the same field of endeavor of assessing job candidate’s skills teaches further comprising: receiving a request from the candidate to access the job posting; and determining a relationship strength associated with a weighted relationship between the knowledge profile associated with the candidate and the knowledge profile associated with the job posting based on the request to access the job posting (Paragraph [0034-0035]; [0040-0042]; [0087-0090]; Fig. 1, The UCG may evolve with time, as the underlying information changes over time. The graph system may periodically update the UCG to add new nodes and edges for new concept phrases and relationships among the nodes of the UCG. The graph system determines the set of relationship edges and the edge weight function by taking into account the content similarity in the internal and external datasets. In some embodiments, each edge between two nodes of the UCG is associated with an edge weight value. The edge weight value may be stored with an indicator. The edge weight value may represent the degree of relatedness between the two-concept represented by the two nodes connected by the edge).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of updating the weighted knowledge graph associated with the job and the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories, wherein each of the knowledge entities are further associated with respective weighted relationships, each of the respective weighted relationships being associated with a relationship strength and a relationship direction; determining a weighted relationship between each of the plurality of candidates and the job based on each of the respective weighted relationships as taught by Kenthapadi (Kenthapadi [0025]). With the motivation of helping to identify best potential applicants for various positions based on factors such as relationships and skills (Kenthapadi [0003]).
Claim 19: Modified Daly discloses the method as per claim 15. Daly further discloses wherein the response indicates a relationship strength associated with a weighted relationship between the knowledge profile associated with the candidate and the knowledge profile associated with the job posting (Paragraph [0015-0018]; [0021-0025]; [0045-0048]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals. A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings. The skill generator module ranks the potential candidates based at least in part on the determined match scores. The skill generator module may apply any search filters to the resulting list of search results. The resulting list of the top ranked candidate who meet the minimum criteria may be provided to the searcher. In some embodiments, the skill generator module may generate an automatic connection request between the searcher and one or more of the top-ranking candidates. The refinement options include skill category, skills, job title, education, etc.).
Claim 20: Modified Daly discloses the method as per claim 15. However, Daly does not disclose wherein the candidate is identified based on the weighted relationship between the knowledge profile associated with the candidate and one or more of the knowledge profiles associated with the applicant, the job posting, or the company comprises a relationship strength above a threshold.
Kenthapadi, in the same field of endeavor of assessing job candidate’s skills teaches wherein the candidate is identified based on the weighted relationship between the knowledge profile associated with the candidate and one or more of the knowledge profiles associated with the applicant, the job posting, or the company comprises a relationship strength above a threshold (Paragraph [0034-0035]; [0040-0042]; [0044-0045]; Fig. 1, in some example embodiments, the graph system determines that an edge connects the first node and the second node a weighted similarity value between the content of the documents corresponding to the two nodes exceeds a threshold value).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of updating the weighted knowledge graph associated with the job and the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories, wherein each of the knowledge entities are further associated with respective weighted relationships, each of the respective weighted relationships being associated with a relationship strength and a relationship direction; determining a weighted relationship between each of the plurality of candidates and the job based on each of the respective weighted relationships as taught by Kenthapadi (Kenthapadi [0025]). With the motivation of helping to identify best potential applicants for various positions based on factors such as relationships and skills (Kenthapadi [0003]).
Claim 21: Modified Daly discloses the method as per claim 15. Daly further disclose comprising generating the one or more knowledge profiles associated with the applicant, the knowledge profile associated with the job posting, and the knowledge profile associated with the company using at machine learning model of a knowledge engine (Paragraph [0033-0035]; [0042]; Figs. 5 and 7, the user may select a business area and skill category associated with their resume. The available options for skill category in some embodiments may be based on industry standard terms and/or terms recognized by an organization. A skill generator module retrieves profile information and/or resume records for a number of individuals form a data store, such as user data. In some embodiments, the skill generator module may additionally or alternatively maintain a profile for each user that includes skill information, skill category information, job history, and/or other information).
Claim 22: Modified Daly discloses the method as per claim 21. Daly further disclose comprising: generating a knowledge profile associated with the operator using the at machine learning model of a knowledge engine (Paragraph [0033-0035]; [0042]; Figs. 5 and 7, the user may select a business area and skill category associated with their resume. The available options for skill category in some embodiments may be based on industry standard terms and/or terms recognized by an organization. A skill generator module retrieves profile information and/or resume records for a number of individuals form a data store, such as user data. In some embodiments, the skill generator module may additionally or alternatively maintain a profile for each user that includes skill information, skill category information, job history, and/or other information).
In the same field of endeavor of identifying potential job candidates for a job Hardtke teaches and identifying the candidate that has not applied to the job posting from the passive interaction of the candidate and based on a weighted relationship between the knowledge profile associated with the operator, the knowledge profile associated with the candidate, the knowledge profile associated with the company, and one or more of the knowledge profiles associated with the applicant or the job posting (Paragraph [0009-0010]; [0069-0071]; [0103-0105]; [0132] the present technology is based on an approach in which a combination of information in a candidate’s resume, a description of the job opening, and external data about the candidate is utilized to inform a machine learning algorithm that matches job openings to candidates by calculating a score. The suitability score serves both sides of the hiring process, allowing employers to find their optimal candidates. Whenever an employer is provided with a list of candidates whose suitability scores exceed a threshold the employer is able to review the candidates profile and make decisions. In an alternative embodiment, an employer can request that scores are calculated for candidates who have already applied for a job opening. When calculating the suitability score, each feature score is weighted by a coefficient derived from an analysis of sample resumes and sample job descriptions, whose matches to one another have been ranked by individuals whose primary profession is recruiting);
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of and identifying the candidate that has not applied to the job posting from the passive interaction of the candidate and based on a weighted relationship between the knowledge profile associated with the operator, the knowledge profile associated with the candidate, the knowledge profile associated with the company, and one or more of the knowledge profiles associated with the applicant or the job posting as taught by Hardtke (Hardtke [0071]). With the motivation of helping to identify candidates for a job based on comparing candidate information and job description information (Hardtke [0003]).
Claim 23: Modified Daly discloses the method as per claim 22. Daly further disclose comprising: generating a list of ranked candidates for the job and a respective score for each of the ranked candidates; and sending the response to the operator based on the knowledge profile associated with the operator, and wherein the response displays a numerical score of the applicant to the job posting and displays respective numerical scores of each of the ranked candidates in the list (Paragraph [0015-0018]; [0021-0025]; [0045-0048]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals. A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings. The skill generator module ranks the potential candidates based at least in part on the determined match scores. The skill generator module may apply any search filters to the resulting list of search results. The resulting list of the top ranked candidate who meet the minimum criteria may be provided to the searcher. In some embodiments, the skill generator module may generate an automatic connection request between the searcher and one or more of the top-ranking candidates).
Claim 24: Modified Daly discloses the method as per claim 1. Daly further disclose wherein the list of ranked candidates comprises all candidates for the job, and wherein the response displays a generated list of all ranked candidates for the job and the respective numerical score for each of the ranked candidates (Paragraph [0013-0014]; [0043-0046]; Figs. 7 and 9, according to one embodiment of the present disclosure, a user interface may be presented that enables a recruiter to enter ranked search criteria for a candidate. A skill search module may determine matching candidate results based on the search criteria including ranking the matches based on optional skills and/or other criteria. The skill generator module determines a match score. The match score may be determined based on comparing the mandatory skills with reach retrieved record. A skill generator module ranks the potential candidates based at least in part on the determined matched score. The resulting list of the top ranked candidates may be provided to the searcher).
Claim 25: Modified Daly discloses the method as per claim 1. Daly further disclose comprising: generating a knowledge graph associated with an operator using the at machine learning model of the knowledge engine; determining, by the at least one machine learning model of the knowledge engine, a weighted relationship between each of the plurality of candidates and the operator based on the knowledge graph associated with the operator and a respective knowledge graph associated with each of the plurality of candidates; determining, by the at least one machine learning model of the knowledge engine, a weighted relationship between each of the plurality of candidates and the company based on the knowledge graph associated with the company and a respective knowledge graph associated with each of the plurality of candidates (Paragraph [0015-0018]; [0021-0025]; [0043-0048]; Figs. 1 and 7, an illustrative operating environment in which user computing devices may send and receive information form one or more skill management systems, skill groups system, skill exchange system, candidate search system, job search system, and/or a job broadcast system. User computing devices may be operated by an employer, a job seeker, and/or other individuals. The skill management system includes a skill data store a user data store and a job data store. The user data store may include information associated with a number of users that have registered for an account. For example, data stored in the user data store may include profile information, resume information, and/or skill information associated with a number of individuals. A job search system may communicate with the skill management system in response to a job seeking user submitting a search request for job openings. The skill generator module ranks the potential candidates based at least in part on the determined match scores. The skill generator module may apply any search filters to the resulting list of search results. The resulting list of the top ranked candidate who meet the minimum criteria may be provided to the searcher. In some embodiments, the skill generator module may generate an automatic connection request between the searcher and one or more of the top-ranking candidates. The skill generator module determines a match score for at least a subset of individuals for whom records were retrieved. The match score may be determined based at least in part by comparing the received mandatory skills, skill category, and/or optional skills with reach retrieved record (e.g. each resume and/or user profile). In some embodiments, the match score for a given record may be based in part on various weights applied to each element of the provided search criteria).
Kenthapadi, in the same field of endeavor of assessing job candidate’s skills teaches and updating the knowledge engine based on the determined weighted relationship between each of the plurality of candidates and the operator and based on the determined weighted relationship between each of the plurality of candidates and the company (Paragraph [0034-0035]; [0040-0042]; [0087-0090]; Fig. 1, The UCG may evolve with time, as the underlying information changes over time. The graph system may periodically update the UCG to add new nodes and edges for new concept phrases and relationships among the nodes of the UCG. The graph system determines the set of relationship edges and the edge weight function by taking into account the content similarity in the internal and external datasets. In some embodiments, each edge between two nodes of the UCG is associated with an edge weight value. The edge weight value may be stored with an indicator. The edge weight value may represent the degree of relatedness between the two-concept represented by the two nodes connected by the edge).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating a skill management system to assess a job candidate and match them to job requirements as disclosed by Daly (Daly [0014]) with the system of updating the weighted knowledge graph associated with the job and the weighted knowledge graphs associated with each of the plurality of candidates based on each of the knowledge entities associated with the at least one of the plurality of categories, wherein each of the knowledge entities are further associated with respective weighted relationships, each of the respective weighted relationships being associated with a relationship strength and a relationship direction; determining a weighted relationship between each of the plurality of candidates and the job based on each of the respective weighted relationships as taught by Kenthapadi (Kenthapadi [0025]). With the motivation of helping to identify best potential applicants for various positions based on factors such as relationships and skills (Kenthapadi [0003]).
Therefore, claims 1-2, 4, 6-7, 9-10, and 13-25 are rejected under 35 U.S.C. 103.
Response to arguments
Applicant’s arguments, see REMARKS, filed January 21, 2026, with respect to the rejections of claims 1-2, 4, 6-7, 9-10, and 13-25 under U.S.C. 101 have been fully considered but are not persuasive.
Representative argues that the newly added claims do not recite an abstract idea as they recite claim limitations that cannot be performed in the human mind. However, the examiner respectfully disagrees as the claims recite receiving a request for potential candidate for a job of a company; identifying a plurality of candidates based on weighted relationships of a knowledge graph associated with a plurality of candidates, the job, and a company; extracting at least knowledge entity relating to each of the plurality of candidates form a plurality of data files; associated each of the knowledge entities with at least one of a plurality of categories; determining a weighted relationship between each of the plurality of candidates and the job; generating a list of ranked candidates for the job; and sending a response to the request for potential candidates for the job including the generated list of ranked candidates and the respective score for each of the ranked candidates in the list. The examiner finds that merely receiving a request to identify candidates for a job, evaluating candidate and job information to determine a “score” or a value by comparing information such as skills and requirements, and generating a list of potential candidates for a job are a mental process as a person such as a recruiter would be capable of mentally, or by using simple tools such as pen and paper, evaluating information pertaining to a job, company, and a candidate and determining a score for a plurality of potential candidates based on determining a “weighted relationship” between a plurality of categories associated with a “knowledge graph” or information pertaining to each entity. The claims are therefore, found to recite concepts the courts have identified as reciting a mental process such as evaluation, observation, judgement, and opinion. Alternatively, the claims recite a certain method of organizing human activity as they recite managing personal behavior or relationships or interactions between people. The claims simply recite a series of steps for conducting a search for potential job applicants for a position by receiving and evaluating information pertaining to a position and a plurality of users to generate a ranked list of candidates. Therefore, the claims are found to recite an abstract idea.
The additional elements of a display for displaying information and a machine learning algorithm to determine a weighted relationship are directed to merely “apply it.” As the claims merely recite applying generic computer elements to perform the abstract idea of receiving information such as candidate information, job posting information, and company information; analyzing the information by comparing the information to determine a similarity score; and presenting a result of the analysis. The claims do not recite an improvement to a technology or technical field. Therefore, the additional elements are not directed to a practical application.
Therefore, the examiner maintains the current 101 rejection.
Claims 2, 4, 6, 9-10, 14, and 17-20 are dependent on claims 1 and 15 and therefore are rejected under the same rejection.
Applicant’s arguments, see REMARKS, filed January 21, 2026, with respect to the rejections of Claims 1-2, 4, 6, 9-10, 13-15, and 17-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Daly (US 2014/0278633) in view of Kenthapadi (US 2019/0066054) further in view of Hardtke (US 2014/0122355) are not persuasive as the claims were amended which required further search and consideration and new art was applied.
Claims 1 and 15: The applicant argues that the current prior art does not disclose the newly amended claim limitations of “an indication of whether each candidates of the ranked candidates in the list applied for the job.” However, upon further search and consideration the examiner finds that Hardtke can be used in combination with the current prior art to teach the newly amended claim limitations. Daly discloses a system of determining a ranked list of candidates for a position by matching their characteristics to that of a job opening (Daly [0045]). The module then presents a user such as a candidate seeker with the list of ranked candidates who meet a minimum mandatory criteria. The examiner finds that a system for displaying records including profiles that are sorted in descending order based on the determined match score would include the score associated with each profile in the list (Daly [0045]). Daly can be used in combination with Hardtke which teaches a system of identifying potential candidates for a job opening by using a scoring function which compares candidates information such as a resume to a job description and applying a weight. Hardtke further teaches allowing a job recruiter to view and sort the scores of users who have applied for a job or random users identified (Hardtke [00132]). Therefore, the examiner finds that Hardtke can be used in combination with the current prior art to disclose the newly amended claim limitations as it would allow for a system of identifying and presenting the scores of candidates who have or have not previously applied for the job opening.
Therefore, claims 1 and 15 are newly rejected under U.S.C. 103
Claims 2, 4, 6-7, 9-10, 13-14, and 16-25 are argued as being allowable as being dependent on claims 1 and 15. Therefore, they are also newly rejected under the same rejection as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Whitehead (US 2023/0237438) Machine learning systems for predictive targeting and engagement.
Jersin (US 2019/0197486) Probability of hire scoring for job candidate searches.
Crow (US 2005/0080657) Matching job candidate information.
Bubna (US 2015/0317606) Scoring model methods and apparatus.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30.
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/COREY RUSS/Primary Examiner, Art Unit 3629
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