DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
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.
Election/Restrictions
Applicant’s election without traverse of claim(s) 1-27 in the reply filed on 11/20/25 is acknowledged.
Claim(s) 28-46 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 11/20/25.
Status of the Claims
This communication is in response to communications received on 11/20/25. Claim(s) none is/are amended, claim(s) none is/are cancelled, claim(s) none is/are new, and applicant does not provide any information on where support for the amendments can be found in the instant specification as there are no amendments. Therefore, Claims 1-27 is/are pending and have been addressed below.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 3/28/23 was/were considered by the examiner.
Response to Arguments
There are no arguments.
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.
Claim(s) 1-27 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 1, 26, and 27 that, under its broadest reasonable interpretation, is directed to matching jobs to candidates.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 1-25 recites a series of steps) and system (claim(s) 26-27 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s):
Claim 1: determining, by a candidate-requisition matching system and based on a machine learning model, a measure of outcome success for a set of job requisitions,
the machine learning model trained to determine measures of outcome success for job requisitions based on a set of job requisition training data comprising:
a set of historical job requisitions; and
job outcomes for the set of historical job requisitions;
ranking, by the candidate-requisition matching system and based on the measure of outcome success, the set of job requisitions to generate a ranked set of job requisitions.
Claim(s) 26 and 27: same analysis as claim(s) 1.
Dependent claims 2-25 recite the same or similar abstract idea(s) as independent claim(s) 1, 26, and 27 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with matching jobs to candidates.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea include machine learning model (claim(s) 1, 26-27), system (claim(s) 1), a non-transitory machine-readable storage medium, processor (claim(s) 26), a system comprising: a processor; and non-transitory machine-readable storage medium (claim(s) 27), machine learning model (claim(s) 4, 9, 12-13, 15, 18, 20-24).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0139]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0139]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-11 and 23-27 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Westerheide et al. (US 2022/0067665 A1).
Regarding claim 1, 26, and 27, Westerheide teaches a method for providing job requisitions, the method comprising:
{a non-transitory machine-readable storage medium having instructions stored thereon that are executable by a processor to cause the following operations for providing job requisitions: - claim 26}
{a system comprising: a processor; and non-transitory machine-readable storage medium having instructions stored thereon that are executable by the processor to cause the following operations for providing job requisitions: claim 27} [see at least Fig. 1 and [0082] “Turning now to the figures, FIG. 1 depicts a system architecture 10 according to some embodiments. The system architecture 10 may include a computing device 12 of a user (e.g., referrer), a computing device 13 of a candidate, and/or a computing device 15 of a hiring entity (e.g., employer, job poster, etc.) communicatively coupled to a cloud-based computing system 116. Each of the computing devices 12, 13, 15 and components included in the cloud-based computing system 116 may include one or more processing devices, memory devices, and/or network interface cards.”;
[0084] “In some embodiments, the cloud-based computing system 116 may include one or more servers 128 that form a distributed computing architecture. … The servers 128 may execute an artificial intelligence (AI) engine that uses one or more machine learning models 154 to perform at least one of the embodiments disclosed herein. The cloud-based computing system 128 may also include a database 129 that stores data, knowledge, and data structures used to perform various embodiments. … In some embodiments the cloud-based computing system 116 may include a training engine 152 capable of generating the one or more machine learning models 154.”]
determining, by a candidate-requisition matching system and based on a machine learning model, a measure of outcome success for a set of job requisitions,
the machine learning model trained to determine measures of outcome success for job requisitions based on a set of job requisition training data comprising:
a set of historical job requisitions; and
job outcomes for the set of historical job requisitions;
ranking, by the candidate-requisition matching system and based on the measure of outcome success, the set of job requisitions to generate a ranked set of job requisitions [for the limitations above, see at least [0085] “In some embodiments the cloud-based computing system 116 may include a training engine 152 capable of generating the one or more machine learning models 154. The machine learning models 154 may be trained to determine referrer scores, determine job match scores, match candidates with referrers, match candidates with job postings, generate contact centers, analyze data, and/or process data, among other things. The one or more machine learning models 154 may be generated by the training engine 130 and may be implemented in computer instructions executable by one or more processing devices of the training engine 152 and/or the servers 128. To generate the one or more machine learning models 154, the training engine 152 may train the one or more machine learning models 154.”;
[0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”;
Fig. 12 and [0107] list of ranked jobs for employer and presented to employer “FIG. 12 illustrates a user interface 1200 for previewing candidate matches for a job requisition according to certain embodiments of this disclosure. The user interface 1200 depicts a statement “Based on the criteria you've entered into the job requisition, these are some matches from the Talinity network. Note that matches are anonymized until Champions begin referring them to you.” For example, the hiring entity that created the job requisition may be presented with a list of job matches and associated referral strength in the score. A “Sales Representative” in Atlanta, WA is ranked first with a score of 97.”;
Fig. 22 and [0118] list of ranked jobs for candidate and presented to candidate “FIG. 22 illustrates a user interface 2200 for presenting opportunities for a referrer according to certain embodiments of this disclosure. The user interface 2200 presents a home screen showing a list of opportunities (job postings) that have been matched for the candidate. In some embodiments, the list of job postings may be sorted based on a job match score determined via one or more codified values for occupation, experience, and/or location. Further, for each of the job postings, a list of potential referrers is displayed, and the referrers are determined based on a referrer score for each job posting and referrer.”].
Regarding claim 2, Westerheide teaches the method of claim 1, further comprising:
determining, by the candidate-requisition matching system, selection of a job requisition of the ranked set of job requisitions by the job candidate; and
submitting, by a candidate-requisition matching system responsive to selection of the job requisition of the ranked set of job requisitions by the job candidate, an application for the job candidate for the job requisition selected [see at least Fig. 1 and [0082] “Turning now to the figures, FIG. 1 depicts a system architecture 10 according to some embodiments. The system architecture 10 may include a computing device 12 of a user (e.g., referrer), a computing device 13 of a candidate, and/or a computing device 15 of a hiring entity (e.g., employer, job poster, etc.) communicatively coupled to a cloud-based computing system 116. Each of the computing devices 12, 13, 15 and components included in the cloud-based computing system 116 may include one or more processing devices, memory devices, and/or network interface cards.”;
[0004] “receiving, from the candidate, an acceptance for the referrer to recommend the candidate to the hiring entity for the job, transmitting, to the computing device of the hiring entity, a recommendation for the candidate on behalf of the referrer”].
Regarding claim 3, Westerheide teaches the method of claim 2, wherein the job candidate is employed at a job corresponding to the job requisition selected based on the application for the job candidate for the job requisition selected [see at least [0127; also 0063, 0119] “For example, each participant (e.g., candidate and/or referrer may receive a certain amount of money if the candidate is hired by the hiring entity based on the referral from the referrer).”].
Regarding claim 4, Westerheide teaches the method of claim 1, wherein the set of job requisition training data comprises job requisition ages for the job requisitions of the set of historical job requisitions, wherein the machine learning model is trained to determine measures of outcome success based on the job requisition ages of the job requisitions, and wherein the set of job requisitions are ranked based on job requisition ages [see at least [0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”;
[0131] further define job data to include fill time “Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled.”;
[0093] further define job data to include create time “Further, during the setup stage, the hiring manager or recruiter may use their computing device 15 to setup a company profile and create a job requisition. In some embodiments, creating or revising a job requisition may initiate matching between a candidate member and the job requisition (job posting) and/or the candidate member and one or more champions.”].
Regarding claim 5, Westerheide teaches the method of claim 1, wherein the job outcomes comprise job offers for the set of historical job requisitions [see at least [0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates.”;
Fig. 2D and [0095] “If the hiring manager or recruiter likes the candidate, a job offer may be transmitted to the candidate. The hiring manager or recruiter may transmit a job offer to the candidate. The candidate may receive the job offer and accept it or decline it.”].
Regarding claim 6, Westerheide teaches the method of claim 1, wherein the job outcomes comprise job offers accepted for the set of historical job requisitions [see at least [0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates.”;
Fig. 2D and [0095] “If the hiring manager or recruiter likes the candidate, a job offer may be transmitted to the candidate. The hiring manager or recruiter may transmit a job offer to the candidate. The candidate may receive the job offer and accept it or decline it.”].
Regarding claim 7, Westerheide teaches the method of claim 1, wherein the set of job requisitions comprises job requisitions for short-term work [see at least Fig. 22 and [0118] list of ranked jobs for candidate and presented to candidate “FIG. 22 illustrates a user interface 2200 for presenting opportunities for a referrer according to certain embodiments of this disclosure. The user interface 2200 presents a home screen showing a list of opportunities (job postings) that have been matched for the candidate. In some embodiments, the list of job postings may be sorted based on a job match score determined via one or more codified values for occupation, experience, and/or location. Further, for each of the job postings, a list of potential referrers is displayed, and the referrers are determined based on a referrer score for each job posting and referrer.”;
[0003] “The job may be for part-time or full-time position at a company (e.g., software engineer, attorney at a law firm, sales person, etc.) or an on-demand position (e.g., landscaper, electrician, plumber, nanny, babysitter, etc.).”;
[0064] seasonality “Regarding the hiring entity, statistical methods may be applied to input variables associated with a job requisition against the candidate population to predict the relevant candidate cohort for each stage of a recruiting “funnel” where machine learning models may adjust job funnel conversion based on the following factors: network strength and referral potential of the candidates in question, job role, seasonality, compensation, industry, geography and the hiring company.”].
Regarding claim 8, Westerheide teaches the method of claim 1, wherein the set of job requisitions comprise job requisitions for short-term nursing jobs [see at least Fig. 22 and [0118] list of ranked jobs for candidate and presented to candidate “FIG. 22 illustrates a user interface 2200 for presenting opportunities for a referrer according to certain embodiments of this disclosure. The user interface 2200 presents a home screen showing a list of opportunities (job postings) that have been matched for the candidate. In some embodiments, the list of job postings may be sorted based on a job match score determined via one or more codified values for occupation, experience, and/or location. Further, for each of the job postings, a list of potential referrers is displayed, and the referrers are determined based on a referrer score for each job posting and referrer.”;
[0003] “The job may be for part-time or full-time position at a company (e.g., software engineer, attorney at a law firm, sales person, etc.) or an on-demand position (e.g., landscaper, electrician, plumber, nanny, babysitter, etc.).”;
[0076] “The occupation scoring function may include a variable associated with an occupation role, which can be a high-level occupation role, such as dentist, nurse, doctor, lawyer, etc.”].
Regarding claim 9, Westerheide teaches the method of claim 1, further comprising:
training the machine learning model to determine the measures of outcome success for the set of job requisitions based on the set of job requisition training data [see at least [0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”].
Regarding claim 10, Westerheide teaches the method of claim 9, wherein the set of job requisition training data further comprises requisition parameters for the set of historical job requisitions, wherein the requisition parameters comprise one or more of the following: requisition age, facility offer rate, number of views for requisition, and number of open positions for requisition [see at least [see at least [0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”;
[0055] further define ([0088])’s job posting information to include number of views for requisition “how many candidates select the job posting (e.g., determined via clicking on the job posting, “liking” the job posting, sharing the job posting, etc.)”].
Regarding claim 11, Westerheide teaches the method of claim 1, further comprising:
determining a set of job candidates;
matching, by the candidate-requisition matching system based on the ranked set of job requisitions, candidates of the set of job candidates to one or more job requisitions of the set of job requisitions, the matching comprising:
matching, by the candidate-requisition matching system, the job candidate to the one or more job requisitions of the ranked set of job requisitions; and
matching, by the candidate-requisition matching system, a second job candidate to one or more second job requisitions of the ranked set of job requisitions; and
providing, by the candidate-requisition matching system, the one or more second job requisitions to the second job candidate for application by the second job candidate [see at least Fig. 12 and [0107] list of ranked jobs for employer and presented to employer “FIG. 12 illustrates a user interface 1200 for previewing candidate matches for a job requisition according to certain embodiments of this disclosure. The user interface 1200 depicts a statement “Based on the criteria you've entered into the job requisition, these are some matches from the Talinity network. Note that matches are anonymized until Champions begin referring them to you.” For example, the hiring entity that created the job requisition may be presented with a list of job matches and associated referral strength in the score. A “Sales Representative” in Atlanta, WA is ranked first with a score of 97.”;
Fig. 14 and [0109] list of candidates and the list of candidates matched to the list of jobs “The user interface 1400 includes a list of candidates (e.g., Justin Davis, Mollie Bradfor, Bryan Herren, etc.) that have been referred for the job requisition of “Sales Representative”.”;
Fig. 13 and [0108] the candidates matched to other jobs from list of jobs “For example, the first job requisition is titled “Sales Representative” and the job status indicates this job requisition has been shared 346 times, referred 68 times, and received 60 application requests. There is also a link that enables the user to view all referred candidates, which once selected, may redirect the computing device of the user to FIG. 14.”;
Fig. 22 and [0118] “The user interface 2200 presents a home screen showing a list of opportunities (job postings) that have been matched for the candidate. In some embodiments, the list of job postings may be sorted based on a job match score determined via one or more codified values for occupation, experience, and/or location.”].
Regarding claim 23, Westerheide teaches the method of claim 1, wherein the machine learning model is trained iteratively [see at least [0059] “The machine learning models may be continuously trained to determine matches and/or referrer's scores based on newly received data pertaining to candidates, referrers, and/or jobs.”].
Regarding claim 24, Westerheide teaches the method of claim 1, wherein the machine learning model is trained iteratively based on one or more updated sets of job requisition training data, wherein the one or more updated sets of job requisition training data comprise updated sets of job requisitions, the measure of outcome success for the updated sets of job requisitions determined, and candidate outcomes for the updated sets of job requisitions [see at least [0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”;
[0059] “The machine learning models may be continuously trained to determine matches and/or referrer's scores based on newly received data pertaining to candidates, referrers, and/or jobs.”].
Regarding claim 25, Westerheide teaches the method of claim 1, wherein job outcomes comprise job outcomes for a set of historical job applications submitted for the set of historical job requisitions [see at least [0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Claim(s) 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Westerheide et al. (US 2022/0067665 A1) in view of Aslan et al. (US 2017/0132528 A1).
Regarding claim 12, Westerheide teaches the method of claim 1, further comprising:
determining, by the candidate-requisition matching system and based on a machine learning model, a measure of outcome success for a given job candidate and a given job requisition of the set of job requisitions,
wherein the machine learning model is trained to determine, based on a second set of job requisition training data, a second measure of outcome success for a candidate for a job requisition,
the second set of job requisition training data comprising:
a second set of historical job requisitions;
candidate information for the second set of historical job requisitions; and
candidate outcomes for the second set of historical job requisitions
[see at least [0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”].
Westerheide teaches matching jobs and candidates based on one or more machine learning models but doesn’t teach a second machine learning model however, in the similar field of machine learning Aslan discloses
determining based on a second machine learning model, data,
wherein the second machine learning model is trained to determine, based on a second set of job requisition training data, a second data, [see at least [0022] for “machine learning models for use with the techniques and systems described herein include, without limitation, tree-based models … random forests … or as an ensemble” where ensemble is joint model;
[0024] where models are based on data including “any other suitable type of data that can be processed by the machine learning models”;
[0005, 0021, 0056] for parallel training of models where one model influences at least one of the other models which is contrast to sequential training where none of the models affect others thus parallel training can yield some if not most models being essentially trained sequentially as there is only a requirement for one teaching model that affects one student model and they could be based on portions of data that is not included in subsequent operations;
[0023] for each model can be used for a different part of the data thus using a different machine-learning model to calculate the data “although FIG. 1 shows both models 100 and 102 as explicitly receiving, or having access to, the training data 104, it is to be appreciated that any individual machine learning model shown in the Figures and described herein can receive, or have access to, at least some of the training data 104 in particular implementations, even if an explicit connection between an individual model and the training data is not depicted in the Figures. In instances where a machine learning model, such as the second model 102, does not receive the training data 104 used by the first model 100, the second model 102 still has access to at least some features in order to communicate with the first model 100.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Westerheide with Aslan to include the limitation(s) above as disclosed by Aslan. Doing so would help provide clarification on how Westerheide’s matching is performed such as ensemble [see at least Aslan [0002, 0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Westerheide and b) Aslan and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 13, modified Westerheide teaches the method of claim 12, .
Modified Westerheide doesn’t teach however Aslan discloses
wherein the machine learning model comprises the second machine learning model [see at least [0022] for “machine learning models for use with the techniques and systems described herein include, without limitation, tree-based models … random forests … or as an ensemble” where ensemble is joint model;
[0024] where models are based on data including “any other suitable type of data that can be processed by the machine learning models”;
[0005, 0021, 0056] for parallel training of models where one model influences at least one of the other models which is contrast to sequential training where none of the models affect others thus parallel training can yield some if not most models being essentially trained sequentially as there is only a requirement for one teaching model that affects one student model and they could be based on portions of data that is not included in subsequent operations;
[0023] for each model can be used for a different part of the data thus using a different machine-learning model to calculate the data “although FIG. 1 shows both models 100 and 102 as explicitly receiving, or having access to, the training data 104, it is to be appreciated that any individual machine learning model shown in the Figures and described herein can receive, or have access to, at least some of the training data 104 in particular implementations, even if an explicit connection between an individual model and the training data is not depicted in the Figures. In instances where a machine learning model, such as the second model 102, does not receive the training data 104 used by the first model 100, the second model 102 still has access to at least some features in order to communicate with the first model 100.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Westerheide with Aslan to include the limitation(s) above as disclosed by Aslan. Doing so would help provide clarification on how Westerheide’s matching is performed such as ensemble [see at least Aslan [0002, 0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Westerheide and b) Aslan and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 14, modified Westerheide teaches the method of claim 12,
and Westerheide teaches further comprising: determining whether the measure of second outcome success satisfies a threshold value of outcome success; and
in response to determining that the second measure of outcome success satisfies the threshold value of outcome success, providing, by the candidate-requisition matching system to the given job candidate, the given job requisition [for the limitations above, see at least [0094] “The champion may be presented with a list of candidates for which the champion has a referrer score above a threshold score. Further, the candidate may also review matched jobs having a job match score above a threshold score.”;
[0080] “For example, numerous skills related to a job are not needed to be entered because the collaborative hire-by-referral system, which enables transmitting notifications to users when their profile, job match score, referrer score, and/or job requisition satisfies a minimum threshold, may automatically populate various skills fields.”].
Regarding claim 15, modified Westerheide teaches the method of claim 12, as well as the second machine learning model.
Westerheide teaches further comprising: determining, by the candidate-requisition matching system and based on the machine learning model, respective measures of outcome success for a second given job candidate for a second set of job requisitions; and
ranking, by the candidate-requisition matching system and based on the respective measures of outcome success, the job requisitions of the second set of job requisitions for the second given job candidate [see at least [0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”;
Fig. 12 and [0107] list of ranked jobs for employer and presented to employer “FIG. 12 illustrates a user interface 1200 for previewing candidate matches for a job requisition according to certain embodiments of this disclosure. The user interface 1200 depicts a statement “Based on the criteria you've entered into the job requisition, these are some matches from the Talinity network. Note that matches are anonymized until Champions begin referring them to you.” For example, the hiring entity that created the job requisition may be presented with a list of job matches and associated referral strength in the score. A “Sales Representative” in Atlanta, WA is ranked first with a score of 97.”;
Fig. 22 and [0118] list of ranked jobs for candidate and presented to candidate “FIG. 22 illustrates a user interface 2200 for presenting opportunities for a referrer according to certain embodiments of this disclosure. The user interface 2200 presents a home screen showing a list of opportunities (job postings) that have been matched for the candidate. In some embodiments, the list of job postings may be sorted based on a job match score determined via one or more codified values for occupation, experience, and/or location. Further, for each of the job postings, a list of potential referrers is displayed, and the referrers are determined based on a referrer score for each job posting and referrer.”].
Regarding claim 16, modified Westerheide teaches the method of claim 15,
and Westerheide teaches further comprising providing the ranking of the job requisitions of the second set of job requisitions to the second given job candidate or a job candidate pool comprising the second given job candidate [see at least Fig. 22 and [0118] list of ranked jobs for candidate and presented to candidate “FIG. 22 illustrates a user interface 2200 for presenting opportunities for a referrer according to certain embodiments of this disclosure. The user interface 2200 presents a home screen showing a list of opportunities (job postings) that have been matched for the candidate. In some embodiments, the list of job postings may be sorted based on a job match score determined via one or more codified values for occupation, experience, and/or location. Further, for each of the job postings, a list of potential referrers is displayed, and the referrers are determined based on a referrer score for each job posting and referrer.”].
Regarding claim 17, modified Westerheide teaches the method of claim 12,
and Westerheide teaches
further comprising: determining, based on the measure of outcome success for the given job candidate and the given job requisition, a ranking of one or more job requisitions of the set of job requisitions; and
providing the ranked set of job requisitions to the given job candidate [for the limitations above, see at least [0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”;
Fig. 12 and [0107] list of ranked jobs for employer and presented to employer “FIG. 12 illustrates a user interface 1200 for previewing candidate matches for a job requisition according to certain embodiments of this disclosure. The user interface 1200 depicts a statement “Based on the criteria you've entered into the job requisition, these are some matches from the Talinity network. Note that matches are anonymized until Champions begin referring them to you.” For example, the hiring entity that created the job requisition may be presented with a list of job matches and associated referral strength in the score. A “Sales Representative” in Atlanta, WA is ranked first with a score of 97.”;
Fig. 22 and [0118] list of ranked jobs for candidate and presented to candidate “FIG. 22 illustrates a user interface 2200 for presenting opportunities for a referrer according to certain embodiments of this disclosure. The user interface 2200 presents a home screen showing a list of opportunities (job postings) that have been matched for the candidate. In some embodiments, the list of job postings may be sorted based on a job match score determined via one or more codified values for occupation, experience, and/or location. Further, for each of the job postings, a list of potential referrers is displayed, and the referrers are determined based on a referrer score for each job posting and referrer.”].
Regarding claim 18, modified Westerheide teaches the method of claim 12, as well as the second machine learning model
and Westerheide teaches further comprising: training, by the candidate-requisition matching system, the machine learning model to determine the second measure of outcome success for the given job candidate based on the second set of job requisition training data [see at least [0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”].
Modified Westerheide doesn’t teach however Aslan discloses
training the second machine learning model to determine first data based on the second data [see at least [0022] for “machine learning models for use with the techniques and systems described herein include, without limitation, tree-based models … random forests … or as an ensemble” where ensemble is joint model;
[0024] where models are based on data including “any other suitable type of data that can be processed by the machine learning models”;
[0005, 0021, 0056] for parallel training of models where one model influences at least one of the other models which is contrast to sequential training where none of the models affect others thus parallel training can yield some if not most models being essentially trained sequentially as there is only a requirement for one teaching model that affects one student model and they could be based on portions of data that is not included in subsequent operations;
[0023] for each model can be used for a different part of the data thus using a different machine-learning model to calculate the data “although FIG. 1 shows both models 100 and 102 as explicitly receiving, or having access to, the training data 104, it is to be appreciated that any individual machine learning model shown in the Figures and described herein can receive, or have access to, at least some of the training data 104 in particular implementations, even if an explicit connection between an individual model and the training data is not depicted in the Figures. In instances where a machine learning model, such as the second model 102, does not receive the training data 104 used by the first model 100, the second model 102 still has access to at least some features in order to communicate with the first model 100.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Westerheide with Aslan to include the limitation(s) above as disclosed by Aslan. Doing so would help provide clarification on how Westerheide’s matching is performed such as ensemble [see at least Aslan [0002, 0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Westerheide and b) Aslan and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 19, modified Westerheide teaches the method of claim 18,
and Westerheide teaches wherein the second set of job requisition training data further comprises candidate parameters for the set of historical job requisitions, wherein the candidate parameters comprise one or more of the following: candidate assignment status, candidate historical offer rate, and candidate historical acceptance rate [see at least [see at least [0088] “The one or more machine learning models 154 may refer to model artifacts created by the training engine 152 using training data that includes training inputs and corresponding target outputs. The training engine 152 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 154 that capture these patterns. For example, the machine learning model may receive candidate information as input and output a matching job based on a pattern. The machine learning model may receive candidate information and job posting information as input and output a job match score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive candidate information and referrer information as input and output a referrer score based on one or more variables related to occupation, experience, and/or location, among others. The machine learning model may receive information related to the referrer and output a referrer score using the relationship described above. The machine learning models 154 may be continuously tuned to vary certain weights to cause some factors of candidates and/or referrers to be more important than others. Although depicted separately from the server 128, in some embodiments, the training engine 152 may reside on server 128. Further, in some embodiments, the database 150, and/or the training engine 152 may reside on the computing devices 12, 13, and/or 15.”;
[0131] “In some embodiments, the processing device may use one or more trained machine learning models 154 to determine a probability (e.g., predict) pertaining to whether the one or more job matches for each of the set of contacts will result in a hiring event. The trained machine learning models 154 may also determine a predicted number of candidates that may be matched for a job posting, a number of referrals that may be received for candidates for the job posting, a number of applications that may be received for the job posting, a number of interviews that may be conducted for the job posting, and/or a number of possible acceptances that may be received from candidates for the job posting. The machine learning model 154 may be trained based on a corpus of training data pertaining to similar job postings and/or candidates and the outcomes (e.g., number of candidates, number of referrals, number of applications, number of interviews, and/or number of possible acceptances) for those job postings and/or candidates. Such a predictive algorithm may be beneficial as it provides insights to the hiring entity whether there is a demand for their job posting, how quickly their job posting will be filled, and/or a likelihood of their job posting being filled. If the predictions are very low (e.g., poor), the hiring entity may determine to not continue with positing their job posting, which may reduce computing resources by deleting the job posting. On the other hand, if the predictions are very high, the hiring entity may continue to post the job requisition, and the job may be filled very quickly due to high demand and a lot of good candidates. Accordingly, computing resources may be reduced because the job posting is filled quickly and removed from the application and/or website.”;
[0095] further define candidate data to include assignment status “The champion may receive the notification whether the candidate accepted or declined the job offer. If the candidate accepted the job offer, the champion may receive payment in a digital wallet associated with the champion. The digital wallet may be included in PayPal®, Google Pay®, Apple Pay®, Venmo®, or any suitable digital wallet. The hiring manager or recruiter may acknowledge when the candidate has been employed for a certain time period (e.g., 60 plus days), which may trigger a payment. The candidate may receive a bonus payment that is deposited into a digital wallet associated with the candidate.”].
Regarding claim 20, Westerheide teaches the method of claim 1, .
Westerheide teaches matching jobs and candidates based on one or more machine learning models but doesn’t teach a second machine learning model however, in the similar field of machine learning Aslan discloses
wherein the machine learning model comprises an ensemble of multiple machine learning models [see at least [0022] for “machine learning models for use with the techniques and systems described herein include, without limitation, tree-based models … random forests … or as an ensemble” where ensemble is joint model;
[0024] where models are based on data including “any other suitable type of data that can be processed by the machine learning models”;
[0005, 0021, 0056] for parallel training of models where one model influences at least one of the other models which is contrast to sequential training where none of the models affect others thus parallel training can yield some if not most models being essentially trained sequentially as there is only a requirement for one teaching model that affects one student model and they could be based on portions of data that is not included in subsequent operations;
[0023] for each model can be used for a different part of the data thus using a different machine-learning model to calculate the data “although FIG. 1 shows both models 100 and 102 as explicitly receiving, or having access to, the training data 104, it is to be appreciated that any individual machine learning model shown in the Figures and described herein can receive, or have access to, at least some of the training data 104 in particular implementations, even if an explicit connection between an individual model and the training data is not depicted in the Figures. In instances where a machine learning model, such as the second model 102, does not receive the training data 104 used by the first model 100, the second model 102 still has access to at least some features in order to communicate with the first model 100.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Westerheide with Aslan to include the limitation(s) above as disclosed by Aslan. Doing so would help provide clarification on how Westerheide’s matching is performed such as ensemble [see at least Aslan [0002, 0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Westerheide and b) Aslan and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Westerheide et al. (US 2022/0067665 A1) in view of Streeter et al. (US 2020/0372305 A1).
Regarding claim 21, Westerheide teaches the method of claim 1, .
Westerheide doesn’t teach however Streeter discloses
wherein the machine learning model is trained based on optimization of a loss function [see at least [0002] “The present disclosure relates generally to machine learning. More particularly, the present disclosure relates to systems and methods for efficiently learning loss functions effective to train improved machine-learned models.”;
[0019] “Furthermore, in contrast to previous work, the proposed algorithms can make use of gradient information in the case where the error metric is differentiable (or can be approximated by a differentiable proxy function). As the experimental data shows, using gradient information can dramatically accelerate the search for a good loss function, and allows efficient discovery of loss functions with hundreds of hyperparameters on-the-fly during training.”;
[0089] “The computing device 10 includes a number of applications (e.g., applications 1 through N). Each application contains its own machine learning library and machine-learned model(s). For example, each application can include a machine-learned model. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Westerheide with Streeter to include the limitation(s) above as disclosed by Streeter. Doing so would help provide clarification on how Westerheide’s matching is performed such as loss function [see at least Streeter [0002] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Westerheide and b) Streeter and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 22, modified Westerheide teaches the method of claim 21, .
Modified Westerheide doesn’t teach however Streeter discloses
wherein the machine learning model is trained based on gradient-based optimization of the loss function [see at least [0002] “The present disclosure relates generally to machine learning. More particularly, the present disclosure relates to systems and methods for efficiently learning loss functions effective to train improved machine-learned models.”;
[0019] “Furthermore, in contrast to previous work, the proposed algorithms can make use of gradient information in the case where the error metric is differentiable (or can be approximated by a differentiable proxy function). As the experimental data shows, using gradient information can dramatically accelerate the search for a good loss function, and allows efficient discovery of loss functions with hundreds of hyperparameters on-the-fly during training.”;
[0089] “The computing device 10 includes a number of applications (e.g., applications 1 through N). Each application contains its own machine learning library and machine-learned model(s). For example, each application can include a machine-learned model. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Westerheide with Streeter to include the limitation(s) above as disclosed by Streeter. Doing so would help provide clarification on how modified Westerheide’s matching is performed such as loss function [see at least Streeter [0002] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Westerheide and b) Streeter and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Westerheide et al. – WO 2022/046914 A1 (relevant because it teaches same as US 2022/0067665 A1)
Harris – Finding the Best Job Applicants for a Job Posting: A Comparison of Human Resources Search Strategies (relevant because it teaches “We compare the ranked lists generated by executive recruiting experts with the list generated by three search strategies: one using crowdworkers in a gamified environment, a second using information retrieval-based search methods, and a third method which combines information retrieval methods and weighted feature-based approach.”)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WEBB whose telephone number is (313)446-6615. The examiner can normally be reached on M-F 10-3.
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/JAMES WEBB/Examiner, Art Unit 3624
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