Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The instant application having Application No. 19/038,269 filed on 01/27/2025 in which claims 1-20 are pending in the application, all of which are ready for examination by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AlA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AlA the applicant regards as the invention.
Claim 2 recites the limitation “effectiveness”. The term "effectiveness" in claim 2 is a relative term which renders the claim indefinite. The term "effectiveness" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner will not give any patentable weight to these limitations and will interpret the remaining claim limitations, as ‘ordering candidates.
Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim 1 recites applying the filter criteria to job candidate data about job candidates of a candidate data repository to obtain filtered job candidate search results; applying supervised training to the model based on example job candidate records from the least a portion of the filtered job candidate search results; producing scores for job candidates of the filtered job candidate search results based on the revised model.
The limitations of applying…, producing…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “applying…, producing…,” in the context of these claims encompass the user manually applying filter criteria, applying training based on example, producing scores for job candidates of search results. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – training…, storing…, executing…, processing…, revising…. The “training”, “processing”, and “revising” limitation amounts to mere instructions to apply an exception (see MPEP 2106.05f). The “storing” and “executing” limitations are insignificant extra-solution activity (mere data gathering, outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Training”, “processing”, and “revising” amounts to mere instructions to apply an exception (see MPEP 2106.05f). The additional elements of “storing” and “executing” is a well-understood, routine, and conventional activity (data gathering, outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 2 recites ordering candidates based on effectiveness in improving predictions. The limitations of ordering…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “ordering…,” in the context of these claims encompass the user manually ordering candidates. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claims 3 and 14 recite supplying prompts to a user to obtain equating synonyms as a single feature to improve machine learning accuracy. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – supplying…. The “supplying” limitation amounts to mere instructions to apply an exception (see MPEP 2106.05f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Supplying” amounts to mere instructions to apply an exception (see MPEP 2106.05f). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 4 recites receiving a user selection of an initial model to bootstrap predictions for a position. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – receiving…. The “receiving” limitations are insignificant extra-solution activity (mere data gathering, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “receiving” is a well-understood, routine, and conventional activity (data gathering, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 5 and 18 recite masking job candidate information biased toward a demographic. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – masking…. The “masking” limitation amounts to mere instructions to apply an exception (see MPEP 2106.05f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Masking” amounts to mere instructions to apply an exception (see MPEP 2106.05f). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 6 recites parsing resumes with domain-specific features prior to sending the resumes to the model. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – parsing…. The “parsing” limitations are insignificant extra-solution activity (mere data gathering, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “parsing” is a well-understood, routine, and conventional activity (data gathering, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 7 and 16 recite generating synthetic resumes from parsed resumes. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – generating…. The “generating” limitations are insignificant extra-solution activity (mere data gathering, outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “generating” is a well-understood, routine, and conventional activity (data gathering, outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 8 recite crawling a network from a browser plugin for job candidates and producing the scores for the job candidates. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – crawling…. The “crawling” limitation amounts to mere instructions to apply an exception (see MPEP 2106.05f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Crawling” amounts to mere instructions to apply an exception (see MPEP 2106.05f). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 9 recites generating a single predicted rating for an unevaluated candidate from multiple user evaluations of a subset of candidates. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – generating…. The “generating” limitations are insignificant extra-solution activity (mere data gathering, outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “generating” is a well-understood, routine, and conventional activity (data gathering, outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 10 recites generating reports of a maximum disagreement of users about the same candidates. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – generating…. The “generating” limitations are insignificant extra-solution activity (mere data gathering, outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “generating” is a well-understood, routine, and conventional activity (data gathering, outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 11 and 20 recite ingesting data submitted to job boards and providing feedback to the job boards and applying candidates about the quality of proposed candidate-position pairings. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – ingesting…, applying…. The “ingesting” and “applying” limitation amounts to mere instructions to apply an exception (see MPEP 2106.05f). The “providing” limitations are insignificant extra-solution activity (mere data gathering, outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Ingesting” and “applying” amounts to mere instructions to apply an exception (see MPEP 2106.05f). The additional elements of “providing” is a well-understood, routine, and conventional activity (data gathering, outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 12 recites filtering job candidate data to provide filtered job candidate data; supplying the scores for the job candidates. The limitations of filtering…, supplying…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “filtering…, supplying…,” in the context of these claims encompass the user manually filtering job candidate, supplying scores for job candidates. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – collecting…, revising…, processing…. The “revising” and “processing” limitation amounts to mere instructions to apply an exception (see MPEP 2106.05f). The “collecting” limitations are insignificant extra-solution activity (mere data gathering, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Revising” and “processing” amounts to mere instructions to apply an exception (see MPEP 2106.05f). The additional elements of “collecting” is a well-understood, routine, and conventional activity (data gathering, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 13 recites using unsupervised machine learning to identify job candidates in clusters similar to a processed cluster. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – using…. The “using” limitation amounts to mere instructions to apply an exception (see MPEP 2106.05f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Using” amounts to mere instructions to apply an exception (see MPEP 2106.05f). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 15 recites collecting different weights for keywords. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – collecting…. The “collecting” limitations are insignificant extra-solution activity (mere data gathering, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “collecting” is a well-understood, routine, and conventional activity (data gathering, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 17 recites identifying candidates for positions the candidates did not apply for. The limitations of identifying…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “identifying…,” in the context of these claims encompass the user manually identifying candidates. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claim 19 recites crawling a network for job candidates. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – crawling…. The “crawling” limitation amounts to mere instructions to apply an exception (see MPEP 2106.05f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Crawling” amounts to mere instructions to apply an exception (see MPEP 2106.05f). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jersin et al. (U.S. PGPub 2019/0197192; hereinafter “Jersin”) in view of Obeid et al. (U.S. Patent 7,711,573; hereinafter “Obeid”).
As per claim 1, Jersin discloses a computer-implemented method, comprising: training a model from the job candidate data from the candidate data repository; (See paras. 30-32, 58, wherein training data and model strategy, extracting data from trained factorization machines are disclosed; as taught by Jersin.)
storing the model in a machine learning models database; (See Fig. 3, paras. 73, 184, wherein various databases and machine-learning models, tools, algorithms are disclosed; as taught by Jersin.)
executing the model with a machine learning system having the filtered job candidate search results as an input to the machine learning system; (See paras. 30-33, wherein utilizing machine learning tasks, such as filtering, search ranking, etc. are disclosed, also See para. 118, wherein executing data and filtering process in which “the search query can use explicit query parameters and filters (e.g. a ' software engineer' job title parameter). The search query can also infer implicit or soft parameters, and ordering indicators” [0118] are disclosed; as taught by Jersin.)
processing the filtered job candidate search results, after applying the filter criteria to the job candidate data, using the machine learning system to rank at least a portion of the filtered job candidate search results into a ranked subset of the filtered job candidate search results; (See Figs. 3, 10, paras. 151, 177-178, 181, wherein ranking features are disclosed; as taught by Jersin.)
applying supervised training to the model based on example job candidate records from the least a portion of the filtered job candidate search results; (See para. 32, wherein training factorization machine using examples of data records from data store process are disclosed, also See Fig. 10, paras. 67, 177, wherein supervised machine learning algorithm features and listing examples of candidates process are disclosed; as taught by Jersin.)
revising the model based on the supervised training to form a revised model; (See paras. 29-32, 58, wherein factorization machines (FMs) and field-aware factorization machines (FFMs) features on machine learning tasks are disclosed, also See para. 103, wherein trained machine learning model functions are disclosed, also paras. 194, 221, wherein re-training features in which “With the training data and the identified features, the machine-learning algorithm is trained. For instance, weights for features can be re-trained by the dynamic weight trainer and passed to the combined ordering model” [0194] and “operation 1212 re-orders the pool of candidates that have not yet been used according to the updated model (analogous to revised model). Then, the method 1200 repeatedly retrieves the candidate with the highest rating until all candidates in the pool have been exhausted” [0221] are disclosed; as taught by Jersin.)
and producing scores for job candidates of the filtered job candidate search results based on the revised model. (See paras. 183-184, 191-193, wherein ordering scores process in which “example machine-learning algorithms provide ordering scores to qualify candidates for jobs. That is, the machine-learning algorithm utilizes features for analyzing the data to generate assessments A feature such as a query-based feature or a stream refinement question-based feature is an individual measurable property of a phenomenon being observed” [0191] (analogous to producing scores) are disclosed; as taught by Jersin.)
However, Jersin fails to disclose obtaining filter criteria; applying the filter criteria to job candidate data about job candidates of a candidate data repository to obtain filtered job candidate search results.
On the other hand, Obeid teaches obtaining filter criteria; (See Fig. 7, col. 29, ll 5-26, wherein filter jobs features are disclosed; as taught by Obeid.)
applying the filter criteria to job candidate data about job candidates of a candidate data repository to obtain filtered job candidate search results. (See Fig. 7, col. 29, ll 5-26, wherein filtering jobs and displaying job list based on filters process are disclosed; as taught by Obeid.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Obeid teachings in the Jersin system. Skilled artisan would have been motivated to incorporate system to managing access to resume database taught by Obeid in the Jersin system for effective using candidate feedback in a streaming environment. In addition, both of the references (Jersin and Obeid) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data search customization. This close relation between both of the references highly suggests an expectation of success.
As per claim 2, the combination of Jersin and Obeid discloses ordering candidates based on effectiveness in improving predictions. (See paras. 27-30, 33-36, 43, wherein improving candidate stream and predicted positions of hiring are disclosed; as taught by Jersin.)
As per claims 3 and 14, the combination of Jersin and Obeid discloses supplying prompts to a user to obtain equating synonyms as a single feature to improve machine learning accuracy. (See paras. 35, 198, wherein suggesting synonymous titles process in which “suggesting software-related job titles such as software engineer, software developer and related or synonymous titles, a system can additionally suggest non-software positions based on accessing title data from similar companies and different, non-software groups and divisions at the hiring company” [0035] are disclosed; as taught by Jersin.)
As per claim 4, the combination of Jersin and Obeid discloses receiving a user selection of an initial model to bootstrap predictions for a position. (See para. 66, wherein generating query facets as part of inferred search query in generating a bootstrap query for intelligent matches process are disclosed; as taught by Jersin.)
As per claims 5 and 18, the combination of Jersin and Obeid discloses masking job candidate information biased toward a demographic. (See para. 176, wherein calculation of scores utilizing various features, such as demographic features are disclosed; as taught by Jersin.)
As per claim 6, Jersin fails to disclose parsing resumes with domain-specific features prior to sending the resumes to the model.
On the other hand, Obeid teaches parsing resumes with domain-specific features prior to sending the resumes to the model. (See Figs. 3, 8, col. 12, ll 40-54 and col. 14, ll 12-59, wherein resume parser program features are disclosed, also See Fig. 12F, col. 35, ll 1-8, wherein process of posting resume to resume management system are disclosed, as taught by Obeid.)
See claim 1 for motivation above.
As per claims 7 and 16, Jersin fails to disclose generating synthetic resumes from parsed resumes.
On the other hand, Obeid teaches generating synthetic resumes from parsed resumes. (See Figs. 3, 8, col. 12, ll 40-54 and col. 14, ll 12-59, wherein resume parser program features are disclosed, also See col. 20, ll 64-67 and col. 21, ll 1-10, wherein resume generator program features on generating new version of resume for candidate process are disclosed; as taught by Obeid.)
See claim 1 for motivation above.
As per claim 8, the combination of Jersin and Obeid discloses crawling a network from a browser plugin for job candidates and producing the scores for the job candidates. (See paras. 92, 100, 229, wherein aggregating attributes and browser-based applications in which “query builder 306 may be implemented as a system component or block that is configured to aggregate the raw attributes across the input candidates, expand them to similar attributes, and then select the top attributes that most closely represent the suggested candidates” [0100] are disclosed, also See Fig. 1, para. 79, wherein web browser are disclosed, also See paras. 110-113, wherein expertise scores for suggested, candidate process are disclosed; as taught by Jersin.)
As per claim 9, the combination of Jersin and Obeid discloses generating a single predicted rating for an unevaluated candidate from multiple user evaluations of a subset of candidates. (See paras. 36, 39, 51-52, wherein user rating in which “user (e.g., a hiring manager or recruiter) can be presented with a series of potential, suggested candidates which the user provides feedback on (e.g., by rating candidates as a good fit or not a good fit)” [0050] are disclosed, also See para. 75, wherein predicting job change process are disclosed; as taught by Jersin.)
As per claim 10, the combination of Jersin and Obeid discloses generating reports of a maximum disagreement of users about the same candidates. (See para. 45, wherein negative responses on feedback in which “a candidate stream collects two types of explicit feedback. The first type of explicit feedback includes signals from a user such as acceptance, deferral, or rejection of a candidate shown or presented to the user (e.g., a recruiter). The second, type of explicit feedback includes member urns that are similar or dissimilar to urns of a desired hire. If the explicit feedback includes many negative responses, an embodiment can prompt the user to ask the user what about the profiles returned is disliked” [0045] are disclosed; as taught by Jersin.)
As per claims 11 and 20, the combination of Jersin and Obeid discloses ingesting data submitted to job boards and providing feedback to the job boards and applying candidates about the quality of proposed candidate-position pairings. (See paras. 39, wherein returning candidates to recruiters based on recruiter feedback process in which “Based on recruiter feedback, a system can revise the stream to include additional candidates and to filter out other candidates. For instance, based on explicit feedback such as, for example, a user rating (e.g., acceptance, deferral, or rejection), a user-specified job title, and a user-specified location, an intelligent matching system can return potential candidates to a user of the system (e.g., a recruiter or hiring manager)” [0039] are disclosed; as taught by Jersin.)
As per claim 12, Jersin discloses a computer-implemented method, comprising: collecting hyper-parameters specifying values to minimize a machine learning error metric or maximize a machine learning accuracy metric; (See Figs. 6-7, paras. 159, 164, wherein feed accuracy score in which “stream information 704 for the existing stream that is being edited includes the job title, probability of hire score (e.g., percentage chance of hire in the next 30 days m the example of FIG. 7), date when the position was opened, number of profiles viewed, number of messages sent, number of messages accepted, and a feed accuracy score (e.g, a percentage of the stream feed that is deemed to be accurate” [0164] are disclosed, also See paras. 118, 206, wherein intelligent matching system functions on creating search query for candidate stream by utilizing query parameters are disclosed; as taught by Jersin.)
revising machine learning models based upon the hyper-parameters to form current machine learning models for specific positions at specific companies; (See paras. 29-32, 58, wherein factorization machines (FMs) and field-aware factorization machines (FFMs) features on machine learning tasks are disclosed, also See para. 103, wherein trained machine learning model functions in which “responses to refinement questions received from the searcher can be used to refine a generated query. In another example embodiment, a machine learning model is trained, to make 'smart suggestions' to the searcher as to how to modify the generated query. The model may be trained to output suggestions based on any number of different facets” [0103] are disclosed, also See paras. 118, 206, wherein intelligent matching system functions on creating search query for candidate stream by utilizing query parameters are disclosed; as taught by Jersin.)
processing the filtered job candidate data, filtered using the search engine, with the current machine learning models to produce scores for job candidates based on fitness for a specific position at a specific company, wherein the current machine learning models are trained using supervised training based on evaluation data related to an evaluated set of candidates from the filtered job candidate data, wherein the evaluation data comprises user evaluation of the candidates in the evaluation set; (See paras. 183-184, 191-193, wherein ordering scores process in which “example machine-learning algorithms provide ordering scores to qualify candidates for jobs. That is, the machine-learning algorithm utilizes features for analyzing the data to generate assessments A feature such as a query-based feature or a stream refinement question-based feature is an individual measurable property of a phenomenon being observed” [0191] (analogous to producing scores) are disclosed; as taught by Jersin.)
and supplying the scores for the job candidates. (See paras. 110-113, wherein expertise scores for suggested, candidate process are disclosed; as taught by Jersin.)
However, Jersin fails to disclose filtering job candidate data using a search engine to provide filtered job candidate data.
On the other hand, Obeid teaches filtering job candidate data using a search engine to provide filtered job candidate data. (See Fig. 7, col. 29, ll 5-26, wherein filter jobs features are disclosed; as taught by Obeid.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Obeid teachings in the Jersin system. Skilled artisan would have been motivated to incorporate system to managing access to resume database taught by Obeid in the Jersin system for effective using candidate feedback in a streaming environment. In addition, both of the references (Jersin and Obeid) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data search customization. This close relation between both of the references highly suggests an expectation of success.
As per claim 13, the combination of Jersin and Obeid discloses using unsupervised machine learning to identify job candidates in clusters similar to a processed cluster. (See paras. 33, wherein using titles that similar companies have hired people to produce list of suggested title in which “avoid the issue of presenting closely-related or synonymous titles such as, for example "Software Engineer" and "Software Developer" at the same time. That is, embodiments improve the efficiency of suggesting titles to a user by filtering out titles that are semantically identical or duplicative” [0033] are disclosed; as taught by Jersin.)
As per claim 15, the combination of Jersin and Obeid discloses collecting different weights for keywords. (See paras. 60-61, wherein query term weights, adjusted weights in which “Query term weights may be adjusted in response to the answers given to stream refinement questions” [0061] are disclosed; as taught by Jersin.)
As per claim 17, the combination of Jersin and Obeid discloses identifying candidates for positions the candidates did not apply for. (See paras. 76-77, wherein promoting active job seekers higher in stream are disclosed, also See para. 179, query builder generating queries based on other companies outside of particular company in which “for a particular company, given suggested candidate profiles, the query builder 306 can generate queries based on a set of other companies, outside of the particular company, that are likely to have candidates similar to the particular company's suggested candidates in their suggested candidate profiles…; as taught by Jersin.)
As per claim 19, the combination of Jersin and Obeid discloses crawling a network for job candidates. (See paras. 92, 100, 229, wherein aggregating attributes and browser-based applications in which “query builder 306 may be implemented as a system component or block that is configured to aggregate the raw attributes across the input candidates, expand them to similar attributes, and then select the top attributes that most closely represent the suggested candidates” [0100] are disclosed; as taught by Jersin.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Dialani et al. (U.S. PGPub 2018/0232702) discloses system for using feedback to re-weight candidate features in a streaming environment.
2) Zhang et al. (U.S. PGPub 2017/0061382) discloses system for recruitment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIN LIN M HTAY whose telephone number is (571)272-7293. The examiner can normally be reached on M-F, 7am-3pm, PST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L. L. H./
Examiner, Art Unit 2153
/KAVITA STANLEY/Supervisory Patent Examiner, Art Unit 2153
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