DETAILED ACTION
----- Second Non-Final Office Action -----
This Office Action is a second Non-Final Office Action. In the first Office Action, claim 11 was inadvertently not rejected under 35 U.S.C. 101 for failing to claim a statutory category of invention, notwithstanding that claim 11, as drafted, is reasonably susceptible under its broadest reasonable interpretation to a construction covering a transitory, propagating signal per se. This omission is corrected in the present Office Action by the addition of the rejection set forth below. Because the rejection of claim 11 under 35 U.S.C. 101 added herein is not a new ground of rejection necessitated by an amendment to the claims, by an information disclosure statement, or by other papers filed by applicant, but rather corrects an inadvertent omission from the first Office Action, this Office Action is properly made Non-Final rather than Final. See MPEP 706.07(a).
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
Claims 1-12 are pending and have been examined.
This action is in reply to the papers filed on 04/22/2025 (effective filing date 10/24/2022).
Information Disclosure Statement
The information disclosure statement(s) submitted: 04/22/2025, has/have been considered by the Examiner and made of record in the application file.
Preliminary Amendment
The present Office Action is based upon the original patent application filed on 04/22/2025 as modified by the preliminary amendment filed on 04/22/2025.
Claim Interpretation
Claim 12 recites the limitation "means for carrying out the steps of the method of claim 1" in the claim "A Computer system comprising means for carrying out the steps of the method of claim 1."
This claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses a generic placeholder "means" that is coupled with functional language ("for carrying out the steps of the method of claim 1") without reciting sufficient structure to perform the recited function, and the generic placeholder is not preceded by a structural modifier. See MPEP § 2181(I).
Because claim 12 uses a means-plus-function limitation, it will be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof, pursuant to 35 U.S.C. 112(f). See MPEP §§ 2181–2186; Williamson v. Citrix Online, LLC, 792 F.3d 1339, 1349 (Fed. Cir. 2015) (en banc) (holding that a limitation lacking the word "means" nevertheless may invoke § 112(f) where it fails to recite sufficiently definite structure, and, conversely, a limitation using "means" invokes § 112(f) absent sufficient recited structure).
Because a computer-implemented means-plus-function limitation directed to a general-purpose computer must correspond to an algorithm disclosed in the specification sufficient to perform the entire claimed function, the Office has reviewed the specification for corresponding structure. See Aristocrat Techs. Austl. Pty Ltd. v. Int'l Game Tech., 521 F.3d 1328, 1333 (Fed. Cir. 2008); WMS Gaming, Inc. v. Int'l Game Tech., 184 F.3d 1339, 1349 (Fed. Cir. 1999); MPEP § 2181(II)(B). Applicant’s specification at pages 3, 4, and 8, discloses an algorithm for computing the score, S, according to a weighted sum of n skills’ values, etc….
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.
Claims 1–12 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention, due to a lack of clear antecedent basis and inconsistent claim terminology, as follows:
Claim 1 recites, in step (a), "receiving a set of data of a vacancy," but subsequently recites "storing the set of vacancy data on a database." There is insufficient antecedent basis for "the set of vacancy data," as this exact term was never previously introduced; it is unclear whether "the set of vacancy data" is intended to refer back to "a set of data of a vacancy," or whether it introduces a new, undefined element.
Similarly, claim 1 recites, in step (b), "receiving ... a set of data of ... a candidate," but subsequently recites "storing each set of candidate data on the database." There is insufficient antecedent basis for "set of candidate data," for the same reason as above.
Claim 1 recites "searching on the database ... a set of candidate skills matching the set of vacancy skills," and later recites "when the set of vacancy skills matches with at least a set of candidate skills." It is unclear whether "a set of candidate skills" and "at least a set of candidate skills" are intended to refer to the "set of candidate skills" of step (b) (of which there is one per candidate), or whether these phrases introduce new, separately antecedent-less elements. The shifting use of "a," "at least a," and "the" renders it unclear how many sets of candidate skills are actually being claimed and compared.
Claim 1 recites, in step (d), "assigning the score to the set of candidate data." As noted above, "candidate data" (as opposed to "data of a candidate") lacks antecedent basis. It is further unclear whether "the set of candidate data" refers to the same data set as "the matching set of candidate skills" recited immediately prior, since "candidate data" and "candidate skills" are used inconsistently throughout the claim to apparently refer to overlapping, but not clearly identical, subject matter.
Claim 1 recites "wherein the score is computed according to a weighted sum of the values of the candidate skills." It is unclear whether "the candidate skills" refers back to "the set of candidate skills" recited earlier in the claim (i.e., an antecedent basis mismatch between singular "skills" and the previously-recited "set of candidate skills"), and it is further unclear how this "wherein" clause—reciting a weighted sum of "values of the candidate skills" generally—relates to, or is consistent with, the more specific score computation recited in dependent claim 2 (weighted sum of education/certification/skill-set scores).
For purposes of examination, the Office has interpreted "the set of vacancy data" as referring to "the set of data of the vacancy" of step (a), and "the set of candidate data" as referring to "each set of data of a candidate" of step (b). Applicant should amend the claims to use consistent antecedent terminology throughout or clarify the relationship between these terms.
Claims 2–12 depend from claim 1 (directly or indirectly) and are rejected for at least the same reasons, as they inherit the indefinite antecedent bases identified above and fail to remedy them.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1–12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to patent-ineligible subject matter. The claims are analyzed using the 2019 Revised Patent Subject Matter Eligibility Guidance (as incorporated into MPEP § 2106).
Step 1 — Statutory Category
Claims 1–9 are directed to a method (process), which is one of the four statutory categories of invention. Claim 10 is directed to a "computer program product comprising instructions." Claim 11 is directed to a "computer-readable storage medium." Claim 12 is directed to a "computer system," a machine.
With respect to claim 10, the claim does not recite that the "instructions" are embodied on a non-transitory computer-readable medium. Under the broadest reasonable interpretation, "instructions which, when the program is executed" are not limited to physical, non-transitory storage and may be interpreted to encompass a transitory, propagating signal (e.g., a signal carrying the program instructions over a transmission medium). A claim directed to a transitory signal per se does not fall within any of the four statutory categories and is therefore not eligible subject matter under 35 U.S.C. 101. See In re Nuijten, 500 F.3d 1346, 1356–57 (Fed. Cir. 2007); MPEP § 2106.03. Accordingly, claim 10, as currently drafted, is unpatentable under 35 U.S.C. 101 as failing to claim one of the four statutory categories, in addition to the analysis under Step 2A/2B below (which applies regardless of how the antecedent-basis/transitory-signal issue is resolved). Applicant may resolve this issue by amending claim 10 to recite "a non-transitory computer program product" or "a non-transitory computer-readable medium comprising instructions."
Claims 1–9, 11, and 12 satisfy Step 1, and the analysis proceeds to Step 2A.
Step 2A, Prong One — Does the Claim Recite a Judicial Exception?
Independent claim 1 recites the following limitations (with exact claim language quoted):
"receiving a set of data of a vacancy ... comprising a set of vacancy skills, wherein the set of vacancy skills comprise logical conditions and numerical values"
"receiving at least a set of data of at least a candidate ... comprising a set of candidate skills, wherein the set of candidate skills comprise education skills' values, certification skills' values and skill-set skills' values"
"storing the set of vacancy data on a database ..., storing each set of candidate data on the database ..., searching on the database ... a set of candidate skills matching the set of vacancy skills"
"computing a score for the set of candidate skills"
"assigning the score to the set of candidate data"
"wherein the score is computed according to a weighted sum of the values of the candidate skills"
These limitations, under their broadest reasonable interpretation, recite a mathematical concept — specifically, a mathematical formula/calculation (a "weighted sum") applied to numerical and logical values — which falls within the "Mathematical Concepts" grouping of abstract ideas identified in MPEP § 2106.04(a)(2)(I). See SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 1167 (Fed. Cir. 2018) (claims to a series of mathematical calculations based on selected information are directed to an abstract idea, even when a "novel mathematical technique" is claimed).
The claim additionally recites, and is directed to, certain methods of organizing human activity, specifically a fundamental economic practice / commercial interaction — matching a job candidate to a job vacancy based on comparing qualifications — which falls under MPEP § 2106.04(a)(2)(II). This is analogous to matching loan applicants to lenders based on qualifying criteria, which the Federal Circuit has held to be an abstract idea. See Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324 (Fed. Cir. 2016) (claims reciting "generic computer components," a database, and comparison of borrower data against lender criteria to "anonymously match" borrowers to lenders were directed to the abstract idea of "the basic concepts" of a loan-shopping process); see also buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (matching or guaranteeing suitability between transacting parties is a fundamental economic practice).
Because the claim recites steps of receiving/collecting data, storing it, searching/comparing it, and computing a score (a mathematical result) from it, the claim is also analogous to claims directed to collecting, analyzing, and presenting/using data, which the Federal Circuit has repeatedly held to be a mental process capable of being performed by a human using pen and paper, or in the human mind, and therefore abstract. See Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353–54 (Fed. Cir. 2016); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372–73 (Fed. Cir. 2011); MPEP § 2106.04(a)(2)(III).
Accordingly, claim 1 recites at least one, and arguably several overlapping, judicial exceptions: a mathematical concept, a certain method of organizing human activity, and a mental process.
Dependent claims 2–9 further narrow the mathematical concept recited in claim 1 by reciting additional mathematical operations, e.g., claim 2's "weighted sum of education skills' scores, certification skills' scores and skill-set skills' scores by adding the results of multiplying [a score] by [a] coefficient," and claims 3–5's coefficients being "proportional to the amount of [education/certification/skill-set] skills' conditions," and claims 6–8's scores being "computed by multiplying at least a[n] ... value by a ... weight value," and claim 9's "value or ... weight value is either 1 or 0." Each of these limitations recites further mathematical relationships/calculations, and therefore each dependent claim likewise recites the same mathematical-concept and mental-process abstract ideas as claim 1, without introducing any additional element outside the abstract idea itself.
Claims 10 and 11 recite instructions/a computer program that, when executed, "cause the computer system to carry out the steps of the method of claim 1," and therefore recite the same abstract ideas as claim 1 for the reasons set forth above.
Claim 12 recites a computer system comprising "means for carrying out the steps of the method of claim 1" and, under the 35 U.S.C. 112(f) claim interpretation above, is limited to the corresponding structure (a general-purpose computer performing an algorithm) for performing the steps of claim 1, and therefore likewise recites the same abstract ideas as claim 1.
Step 2A, Prong Two — Integration into a Practical Application
Having determined that claims 1–12 recite judicial exceptions, the claims are evaluated for whether they integrate the exceptions into a practical application. See MPEP § 2106.04(d).
Claim 1 recites the following additional elements beyond the abstract idea itself: "a computer system," and "a database of the computer system." These are the only structural/technological elements recited in the claim; the "receiving," "storing," and "searching" steps are recited at a high level of generality and describe generic computer functions — receiving/storing/retrieving data — rather than any specific improvement to computer functionality, database technology, or search technology.
These additional elements do not integrate the judicial exception into a practical application because:
They amount to no more than mere instructions to implement the abstract idea (matching and scoring candidates against a vacancy) on a generic computer, which does not integrate a judicial exception into a practical application. See MPEP § 2106.05(f); Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 223 (2014).
The "database" and "computer system" are recited and used only for their conventional, generic functions of storing and retrieving data, which constitutes generally linking the use of the judicial exception to a particular technological environment or field of use, rather than a technological improvement. See MPEP § 2106.05(h); see also Mortgage Grader, 811 F.3d at 1324–25 (use of an "anonymous loan shopping" server/database to perform generic computer functions did not integrate the abstract idea into a practical application).
The claim does not recite any improvement to the functioning of a computer itself, or to any other technology or technical field (contrast MPEP § 2106.05(a)); it does not recite a particular machine that is integral to the claim beyond a generic computer/database (MPEP § 2106.05(b)); it does not effect a transformation of an article (MPEP § 2106.05(c)); and it does not recite any other meaningful limitations beyond generally linking the abstract idea to a technological environment (MPEP § 2106.05(e)).
Claims 2–9 add only further mathematical detail to the score calculation and do not recite any additional element beyond the abstract idea; they therefore likewise fail to integrate the exception into a practical application.
Claims 10 and 11 merely recite that the abstract idea is implemented via "instructions" executed "by a computer system" (claim 10) or via a "computer-readable storage medium" (claim 11) — again, no more than generic, high-level computer implementation, which does not integrate the exception into a practical application. See MPEP § 2106.05(f).
Claim 12 recites a "computer system" limited (via 112(f)) to a general-purpose computer executing the algorithm of claim 1. A general-purpose computer performing generic computer functions to implement an abstract idea does not integrate the abstract idea into a practical application. See MPEP § 2106.05(f); Alice, 573 U.S. at 225–26.
Accordingly, claims 1–12 do not integrate the recited judicial exception into a practical application, and the analysis proceeds to Step 2B.
Step 2B — Inventive Concept (Significantly More)
Under Step 2B, the additional elements of claims 1–12, considered individually and as an ordered combination, are evaluated to determine whether they amount to significantly more than the judicial exception itself. See MPEP § 2106.05.
The only additional elements recited across the claims are generic computing components — "a computer system," "a database," a "computer program product," a "computer-readable storage medium," and (in claim 12) generic "means" corresponding to a general-purpose computer — performing their well-understood, routine, and conventional functions of receiving, storing, and retrieving/searching data, and outputting a computed value. Receiving/transmitting data, electronically storing/retrieving data in a database, and performing mathematical calculations using a computer are each well-understood, routine, and conventional computer functions when claimed generically. See MPEP § 2106.05(d)(II); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat'l Ass'n, 776 F.3d 1343, 1347–48 (Fed. Cir. 2014) (collecting, recognizing, and storing data in a database are well-understood, routine, conventional activities); buySAFE, 765 F.3d at 1355 (computer functions recited "at a high level of generality" performing conventional functions did not add an inventive concept).
Considered as an ordered combination, the claims simply apply the abstract mathematical scoring/matching concept using generic computer components performing their ordinary functions in an ordinary, conventional order (receive data, store data, search/compare data, compute a result). This ordered combination adds nothing that is not already present when the limitations are considered individually. See Alice, 573 U.S. at 225 (a claim directed to an abstract idea does not become patent eligible merely by requiring generic computer implementation); Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 79 (2012).
For at least these reasons, claims 1–12 do not recite significantly more than the judicial exception(s) identified above, and are therefore rejected under 35 U.S.C. 101 as being directed to patent-ineligible subject matter.
----- Claim Rejections - 35 U.S.C. § 101 (non-transitory media / signals per se) -----
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically a transitory, propagating signal, and therefore does not fall within any of the four statutory categories of invention — process, machine, manufacture, or composition of matter — required by 35 U.S.C. 101.
Claim 11 recites “A computer-readable storage medium comprising instructions which, when executed by a computer system, cause the computer system to carry out the steps of the method of claim 1.” Claim 11 does not recite that the “computer-readable storage medium” is non-transitory. Under the broadest reasonable interpretation, in light of the specification, a “computer-readable storage medium” that is not expressly limited to a non-transitory embodiment reads on transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal, a carrier wave, or other transitory signal per se transmitted over a network or transmission medium.
A transitory, propagating signal is not a “process,” “machine,” “manufacture,” or “composition of matter” within the meaning of 35 U.S.C. 101, and accordingly does not constitute patent-eligible subject matter under any statutory category. In re Nuijten, 500 F.3d 1346, 1356–57 (Fed. Cir. 2007) (holding that a transitory propagating signal is not a “process,” is not a “machine,” is not a “manufacture” because it is not a tangible article or commodity, and is not a “composition of matter,” and therefore does not fall within any of the four categories of patent-eligible subject matter under 35 U.S.C. 101); MPEP 2106.03 (a claim to a computer-readable medium, computer-readable storage medium, or similar storage medium that covers a signal per se, under its broadest reasonable interpretation in light of the specification, must be rejected under 35 U.S.C. 101 as directed to non-statutory subject matter, because such a claim does not fall within at least one of the four categories of invention).
Because claim 11, under its broadest reasonable interpretation, is not limited to a tangible, non-transitory embodiment, claim 11 does not claim one of the four statutory categories of invention and is rejected under 35 U.S.C. 101.
This rejection may be obviated by amending claim 11 to recite that the storage medium is non-transitory, for example: “A non-transitory computer-readable storage medium comprising instructions which, when executed by a computer system, cause the computer system to carry out the steps of the method of claim 1.” See MPEP 2106.03.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over: Hardtke et al. 2014/0358810; in view of Rao et al. 2015/0317610.
19/123,185 – Claim 1. (Currently amended) Hardtke et al. 2014/358810 teaches A method for establishing a score of a match on a social network, the method comprising, by a computer system (Hardtke et al. 2014/358810 [Abstract; 0004; 0006; 0010]), the steps of: (a) receiving a set of data of a vacancy, the set of data of the vacancy comprising a set of vacancy skills, wherein the set of vacancy skills comprise logical conditions and numerical values (Hardtke et al. 2014/358810 "[0021] FIGS. 6A, 6B-1, and 6B-2: Panel A: Clustering analysis of resume and job description data. Key information (e.g., the 4 key items: past job titles, employers, schools, and majors) were extracted from all the resumes in a database. A set of clustering analyses were performed to examine relationships between these categories of information. For example, for a particular major, what are the most frequently occurring job titles that a person has attained? Alternatively, does a particular employer prefer to hire people from a particular school or with a particular major? Through these analyses, it is possible to predict what jobs a person is most likely qualified for. Panels B-1 and B-2: Job titles for candidates who majored in Industrial engineering and Computational Information Systems. The area of each polygon is proportional to the number of persons having a job of that title, though the shape of a polygon and its position in a row are not important. Larger polygons are higher up the figure, for clarity. It can be seen that industrial engineering majors lead to a greater variety of job titles in the workplace than to computer information systems majors. In FIG. 6B-1, the lists of job titles for the lower rows are shown at the side of each row." and "[0045] In some embodiments, the job descriptions can also be harvested from, e.g., one or more external databases of job openings. The descriptions of job openings and/or the candidate resumes are imported into the computer program via a direct link to some third party computer system or database. For example, the system may make a network connection to an employer or to a recruiter and access a remote repository of resumes or descriptions of job openings, and then upload a batch of those documents into the system. The documents may be retrieved and uploaded according to a set schedule, such as once-daily, for example at 2 am, or once weekly, or once fortnightly, or once monthly."), (b) receiving at least a set of data of at least a candidate, each set of data of a candidate comprising a set of candidate skills, wherein the set of candidate skills comprise education skills’ values, certification skills’ values and skill-set skills’ values (Hardtke et al. 2014/358810 "[001 0] 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 (the job description), and external data such as social media information about the candidate and salary information about the positions the candidate has held is utilized to inform a set of machine-learning algorithms that match job openings to candidates by calculating a score, referred to herein as a suitability score. The result is a scoring function, a tool that combats inefficiency in the labor market by automatically and rapidly surfacing optimal candidates [0011] The suitability score serves both sides of the hiring process, both allowing candidates to find their optimal job, as well as employers to find their optimal candidates, and thereby engenders productivity in the successful employment of the most-suited individuals as well as efficiency in locating those individuals from among large applicant pools." and "[0006] The advent of social media and its recent exponential growth as a global phenomenon have prompted many researchers to consider its use in a number of situations. Social media includes Internet based services that accept and store personal data from a number of users and permit those users to communicate with one another via messaging capabilities within the social media service and not outside of it, and permit users to control access to personal data stored in the service to selected other users of the service, as well as control or limit access to other individuals who are not users of the service. For the first time, personal and biographical data of large numbers of individuals are stored in one place and in a common format. To date, we have seen the development of novel methods and approaches to enhance our understanding of many complex principles, as diverse as knowledge evolution (see, e.g., D.Barbieri, "Deductive and inductive stream reasoning for semantic social media analytics", Intelligent Systems, 25(6):32-41, 2010), and disease surveillance (C. Corley, "Text and structural data mining in web and social media", Int. J. Environ. Res. Public Health, 7(2):596-615, 2010)."), storing the set of vacancy data on a database of the computer system, storing(Hardtke et al. 2014/358810 "[0014] The present disclosure includes a computer-based method for quantifying the suitability of a candidate for a job opening, the method comprising: accepting a resume of the candidate; extracting a plurality of candidate features from the resume; receiving a job description of the job opening from a prospective employer: extracting a plurality of job features from the job description: for each feature of the plurality of candidate features, obtaining a feature score by calculating an overlap between the candidate feature and a corresponding job feature: combining the feature scores for the resume into a suitability score for the job opening: and notifying one or both of the candidate or the prospective employer if the suitability score exceeds a first suitability threshold. [0015] The present disclosure additionally includes a computer system for matching candidates to job openings, the system comprising: a first input connection that accepts a resume from a candidate; a second input connection that accepts a description of a job opening from an employer; a memory to store the resume and the description: one or more processors configured with instructions to: identify candidate features in the resume: identify job features in the description; calculate a score based on a match of candidate features with job features; a communication device for alerting the candidate if the score exceeds a first threshold; and a communication device for alerting the employer if the score exceeds a second threshold."), searching(Hardtke et al. 2014/358810 "[0026] Memory 538 stores procedures and data, typically including some or all of: an operating system 540 for providing basic system services; one or more application programs, such as a parser routine 550, and a compiler (not shown in FIG. 1 ), a file system 542, one or more databases 544 that store resumes 546, job descriptions 548, and other information, and optionally a floating point coprocessor where necessary for carrying out high level mathematical operations. The methods of the present invention may also draw upon functions contained in one or more dynamically linked libraries, not shown in FIG. 1, but stored either in memory 538, or on disk 534. [0027] The database and other routines shown in FIG. 1 as stored in memory 538 may instead, optionally, be stored on disk 534 where the amount of data in the database is too great to be efficiently stored in memory 538. The database may also instead, or in part, be stored on one or more remote computers that communicate with computer system 500 through network interface 536. [0028] Memory 538 is encoded with instructions for receiving input from a candidate and for calculating a suitability score for the candidate's resume against a job description. Instructions further include programmed instructions for performing one or more of parsing, calculating a metric, and various statistical analyses.") matching the set of vacancy skills (Hardtke et al. 2014/358810 "[0015] The present disclosure additionally includes a computer system for matching candidates to job openings, the system comprising: a first input connection that accepts a resume from a candidate: a second input connection that accepts a description of a job opening from an employer: a memory to store the resume and the description: one or more processors configured with instructions to: identify candidate features in the resume: identify job features in the description: calculate a score based on a match of candidate features with job features: a communication device for alerting the candidate if the score exceeds a first threshold: and a communication device for alerting the employer if the score exceeds a second threshold."); when the set of vacancy skills matches with at least a set of candidate skills, the method further comprises, for each matching set of candidate skills: c) computing a score for the set of candidate skills (Hardtke et al. 2014/358810 "[0010] 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 (the job description), and external data such as social media information about the candidate and salary information about the positions the candidate has held is utilized to inform a set of machine-learning algorithms that match job openings to candidates by calculating a score, referred to herein as a suitability score. The result is a scoring function, a tool that combats inefficiency in the labor market by automatically and rapidly surfacing optimal candidates. [0011] The suitability score serves both sides of the hiring process, both allowing candidates to find their optimal job, as well as employers to find their optimal candidates, and thereby engenders productivity in the successful employment of the most-suited individuals as well as efficiency in locating those individuals from among large applicant pools. [0012] The suitability score emulates optimal human behavior and, being automated, can be calculated at any time in order to get the most qualified candidates hired. [0013] The present disclosure provides for a computer-based method for identifying a best-fit candidate for a job opening, the method performed on at least one computer having a processor, a memory and input/output capability, the method comprising: receiving one or more resumes of one or more candidates: receiving one or more descriptions of job openings provided by one or more employers: identifying a plurality of job features in each of the descriptions of job openings: for each resume of the one or more resumes, identifying a plurality of candidate features in the resume: calculating a score for each of the one or more descriptions of job openings, wherein the score is based on a match between the plurality of candidate features in the resume and the plurality of job features in the description of the job opening: creating a first list of scores associated with each of the one or more descriptions: identifying for each of the one or more descriptions those resumes in the first list whose score exceeds a first threshold fit: and communicating a notification of a selected resume to an employer if the selected resume has a score that exceeds the first threshold fit for a description of a job opening provided by that employer. [0014] The present disclosure includes a computer-based method for quantifying the suitability of a candidate for a job opening, the method comprising: accepting a resume of the candidate; extracting a plurality of candidate features from the resume; receiving a job description of the job opening from a prospective employer; extracting a plurality of job features from the job description; for each feature of the plurality of candidate features, obtaining a feature score by calculating an overlap between the candidate feature and a corresponding job feature; combining the feature scores for the resume into a suitability score for the job opening; and notifying one or both of the candidate or the prospective employer if the suitability score exceeds a first suitability threshold. [0015] The present disclosure additionally includes a computer system for matching candidates to job openings, the system comprising: a first input connection that accepts a resume from a candidate; a second input connection that accepts a description of a job opening from an employer; a memory to store the resume and the description; one or more processors configured with instructions to: identify candidate features in the resume; identify job features in the description; calculate a score based on a match of candidate features with job features; a communication device for alerting the candidate if the score exceeds a first threshold; and a communication device for alerting the employer if the score exceeds a second threshold."), d) assigning the score to the set of candidate data; wherein the score is computed according to a weighted sum of the values of the candidate skills (Hardtke et al. 2014/358810 "[0102] A feature score, F, for a given feature can be calculated according to a metric selected from the group consisting of (but not limited to): cosine overlap; Tanimoto coefficient; Jaccard coefficient; Dice coefficient; and Tversky index. Generally, as described elsewhere herein, some features lend themselves to being normalized in the range [0, 1], whereas others may be binary quantities, and still other features may not have an upper bound. [0103] Typically, a suitability score, S, is a number between 0 and 100, though other normalization schemes could be used, such as a number between 0 and 10, and a number between 0 and 1,000. It is also possible that a scoring system could be unnormalized, and simply be expressed as a number proportional to the goodness of fit between a resume and a description of a job opening, in which case the larger the number (with no upper bound) the more suited is a candidate for a job opening. [0104] Typically, when calculating a suitability score, each feature score is weighted by a coefficient derived from a statistical analysis of sample resumes and sample job descriptions, whose matches to one another have been ranked by individuals whose primary profession is recruiting. A study that is the basis of such a statistical analysis is described in Example 1 herein. [0105] One method of deriving a weighting coefficient used to determine the contribution of a feature score to the suitability score is to: obtain at-statistic estimated discriminating power for the feature. This can be done by comparing the feature score to a probability distribution function for that feature obtained for a set of resumes that have been ranked by individuals whose primary profession is recruiting, thereby determining whether the feature is a quantity that indicates a good match between the candidate and the job opening. If the feature is such a quantity, a weight can be applied to the feature based on the discriminating power. If the feature is not such a quantity, it will typically still play a role in the certain types of matches because features that do not have discriminating power for typical resume-job pairs stay in the calculation of suitability score, and may be important for some employers. For example, it is possible to adapt the form of the suitability score for different employers. Features such as mis-spellings (typographical errors) in candidates' resumes may be unimportant to some employers, but may be very relevant to hiring considerations of other employers or categories of employers. The mathematical framework for calculating a suitability score for all candidate-job opening pairs can also be utilized to derive a customized score for a specific employer. In this way, the development of a suitability score can be, and preferably is, a dynamic process. The scoring function can be updated for a particular employer as and when its preferences become known. [0106] Another way of deriving a weighting coefficient for a feature is to analyze data from a large scale comparison of resumes to job openings using a method selected from machine learning; neural networks and other multi-layer perceptrons; support vector machines; principal components analysis; Bayesian classifiers; Fisher Discriminants; Linear Discriminants; Maximum Likelihood Estimation; Least squares estimation; Logistic Regressions; Gaussian Mixture Models; Genetic Algorithms; Simulated Annealing; Decision Trees; Projective Likelihood; k-Nearest Neighbor; Function Discriminant Analysis; Predictive Learning via Rule Ensembles; Natural Language Processing, State Machines; Rule Systems; Probabilistic Models; Expectation-Maximization; and Hidden and maximum entropy Markov models. Each of these methods can assess the relevance of a given feature of a resume for purposes of suitability for a job opening, and provide a quantitative weighting of each. [0107] A schematic that illustrates, without mathematical detail, an assembly of a suitability score is shown in FIG. 3. Various feature scores based on a candidate's resume, the job description, or an overlap of the two are calculated. For example, such feature scores could be based on: a calculated overlap of a resume word or property and a job description word or property 301 ; a calculated score for a piece of external data such as a ranking of an educational institution 303; a calculated score for a piece of data about the candidate obtained from social media 305; and a calculated score for an aspect of the candidate's resume such as its word count 307. [0108] Each of the respective feature scores is then weighted, 309-315, with a factor based on a probabilistic analysis of the importance of that feature. The probabilistic analysis is, as described elsewhere herein, based on a large-scale evaluation of many resume-job opening pairs. Feature scores are weighted according to how likely the value of the score for that feature is to lead to the candidate being considered a match for the job opening. The weighted feature scores are summed 317, thereby creating an overall suitability score 319.").
Hardtke et al. 2014/358810 may not expressly disclose the “candidate skills scoring” features, however, Rao et al. 2015/0317610 teaches (Rao et al. 2015/0317610 [0071 - a candidate's talent score may reflect how close the value of a candidate's credential is to the employer's preferred value for that credential. As such, the candidate's talent score may reflect whether the amount of knowledge/skill possessed by the candidate in the area of the credential is close to the amount of knowledge/skill that the employer desires candidates to have in that area. For example, if an employer indicates that 0.85 is a preferred value for the credential of having a GPA equal to 3.7, then the talent score of a candidate whose credential of having an undergraduate GPA equal to 3.7 has a value of 0.6 (e.g., when 30% of students at the candidate's school have a GPA greater than 3.7) may be lower than the talent score of a candidate whose same credential has a value of 0.85 (e.g., when 5% of students at the candidate's school have a GPA greater than 3.7).][0088 - a candidate's talent score may reflect whether the candidate possesses skills related to those desired by an employer.][0243 - generating the representation of job requirements may comprise calculating parameter values for use in calculating credential scores for a candidate's academic credentials, professional credentials, skills credentials, etc., as well as parameter values used to combine these different credential scores into a single talent score for the candidate.][0255 - generating the representation of job requirements comprises calculating one or more parameter values for use in calculating credential scores for a candidate's skills (e.g., Machine Learning, Java, etc.)][0256 - generating a representation of the job description may comprise calculating parameter values used to combine one or more academic credential scores, one or more professional credentials scores, and one or more skills scores to obtain a candidate's talent score.]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hardtke et al. 2014/358810 to include the features as taught by Rao et al. 2015/0317610. One of ordinary skill in the art would have been motivated to do so to utilize well known tools and features useful for establishing a score of a match on a social network which should prove to improve user experience, maximize profits, and optimize revenue (i.e., advertisement optimization / improve user experience).
19/123,185 – Claim 2. (Currently amended) Hardtke et al. 2014/358810 further teaches The method according to claim 1, wherein the score of the set of candidate skills is computed according to a weighted sum of education skills’ scores, certification skills’ scores and skill-set skills’ scores by adding the results of multiplying an education skills’ score by an education skills’ coefficient, a certification skills’ score by a certification skills’ coefficient, and a skill-set skills’ score by a skill-set skills’ coefficient; and wherein the education skills’ scores, the certification skills’ scores and the skill-set skills’ scores are computed as a weighted sum of the education skills’ values, the certification skills’ values and the skill-set skills’ values, respectively (Hardtke et al. 2014/358810 [Abstract; 0060; 0076 – Suitability Score; Tables 1A-1M][0104 - when calculating a suitability score, each feature score is weighted by a coefficient derived from a statistical analysis of sample resumes and sample job descriptions, whose matches to one another have been ranked…][0105 - method of deriving a weighting coefficient used to determine the contribution of a feature score to the suitability score is to…][0108]).
19/123,185 – Claim 3. (Currently amended) Hardtke et al. 2014/358810 further teaches The method according to claim 2, wherein the education skills’ coefficient is a numerical value in a given range, wherein the value of the education skills’ coefficient is proportional to the amount of education skills’ conditions of the set of vacancy skills (Hardtke et al. 2014/358810 [Fig. 5; 0020 – range…; Figs. 6A, 6B-1, 6B-2; 0021; 0077 – numerical value…; Table 1E – Skills; 0102 – coefficients; 0103 – proportional; 0149; 0151]).
19/123,185 – Claim 4. (Currently amended) Hardtke et al. 2014/358810 further teaches The method according to claim 2, wherein the certification skills’ coefficient is a numerical value in a given range, wherein the value of the certification skills’ coefficient is proportional to the amount of certification skills’ conditions of the set of vacancy skills (Hardtke et al. 2014/358810 [Fig. 5; 0020 – range…; Figs. 6A, 6B-1, 6B-2; 0021; 0077 – numerical value…; Table 1E – Skills; 0102 – coefficients; 0103 – proportional; 0149; 0151]).
19/123,185 – Claim 5. (Currently amended) Hardtke et al. 2014/358810 further teaches The method according to claim 2, wherein the skill-set skills’ coefficient is a numerical value in a given range, wherein the value of the skill-set skills’ coefficient is proportional to the amount of skill-set skills’ conditions of the set of vacancy skills (Hardtke et al. 2014/358810 [Fig. 5; 0020 – range…; Figs. 6A, 6B-1, 6B-2; 0021; 0077 – numerical value…; Table 1E – Skills; 0102 – coefficients; 0103 – proportional; 0149; 0151]).
19/123,185 – Claim 6. (Currently amended) Hardtke et al. 2014/358810 further teaches The method according to claim 2, wherein the education skills’ score is computed by multiplying at least an education skills’ value by an education skills’ weight value (Hardtke et al. 2014/358810 [0023-0032 – computing; 0081 – education; 0086 - highest educational level attained by candidate][0092 - Table 1 E lists various features based on the education and skills of candidate and those required by the Job Opening.][Table 1 E – education and skills][0108 - Each of the respective feature scores is then weighted…][Claim 2- multiplying]).
19/123,185 – Claim 7. (Currently amended) Hardtke et al. 2014/358810 further teaches The method according to claim 2, wherein the certification skills’ score is computed by multiplying at least a certification skills’ value by a certification skills’ weight value (Hardtke et al. 2014/358810 [0056 – certification…][0023-0032 – computing; 0081 – education; 0086 - highest educational level attained by candidate][0092 - Table 1 E lists various features based on the education and skills of candidate and those required by the Job Opening.][Table 1 E – certifications and skills…][0108 - Each of the respective feature scores is then weighted…][Claim 2- multiplying]).
19/123,185 – Claim 8. (Currently amended) Hardtke et al. 2014/358810 further teaches The method according to claim 2, wherein the skill-set skills’ score is computed by multiplying at least a skill-set skills’ value by a skill-set skills’ weight value (Hardtke et al. 2014/358810 [0076 – skill sets…][0056 – certification…][0023-0032 – computing; 0081 – education; 0086 - highest educational level attained by candidate][0092 - Table 1 E lists various features based on the education and skills of candidate and those required by the Job Opening.][Table 1 E –skills…][0108 - Each of the respective feature scores is then weighted…][Claim 2- multiplying]).
19/123,185 – Claim 9. (Currently amended) Hardtke et al. 2014/358810 further teaches The method according to claim 6, wherein a skills’ value or skills’ weight value is either 1 or 0 (Hardtke et al. 2014/358810 [0088; Table 1A - a value between 0 and 1…][Table 1E - the number of skills from the job description. Value is between 0 and 1.][0092 - Table 1 E lists various features based on the education and skills of candidate and those required by the Job Opening.][Table 1 E –skills…][0108 - Each of the respective feature scores is then weighted…]).
19/123,185 – Claim 10. (Currently amended) Hardtke et al. 2014/358810 further teaches A computer program product comprising instructions which, when the program is executed by a computer system, cause the computer system to carry out the steps of the method of claim 1 (Hardtke et al. 2014/358810 [0029; 0038]).
19/123,185 – Claim 11. (Currently amended) Hardtke et al. 2014/358810 further teaches A computer-readable storage medium comprising instructions which, when executed by a computer system, cause the computer system to carry out the steps of the method of claim 1 (Hardtke et al. 2014/358810 [0029; 0032; 0038]).
19/123,185 – Claim 12. (Currently amended) Hardtke et al. 2014/358810 further teaches A Computer system comprising means for carrying out the steps of the method of claim 1 (Hardtke et al. 2014/358810 [0029; 0032; 0038; 0065; 0067]).
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
PERTINENT PRIOR ART – Patent Literature
The prior-art made of record and considered pertinent to applicant's disclosure.
Smith et al. 2022/0004943 [0002] The disclosed and claimed concept relates to a method of matching job seekers and employers and, more specifically, to a method wherein a limited number of highly compatible job seekers are matched to an employer.
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
M. Lupei, A. Mitsa, I. Povkhan and V. Sharkan, "Determining the Eligibility of Candidates for a Vacancy Using Artificial Neural Networks," 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 2020, pp. 18-22, doi: 10.1109/DSMP47368.2020.9204020.
M. Mehta et al., "A Service-Oriented Human Capital Management Recommendation Platform," 2019 IEEE International Systems Conference (SysCon), Orlando, FL, USA, 2019, pp. 1-8, doi: 10.1109/SYSCON.2019.8836842.
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b
