e 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 .
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 1-4, 10-13, 15-18, and 21-28 rejected under 35 U.S.C. § 101 are directed to an abstract idea without significantly more.
The claims do not provide significantly more than the judicial exception under the subject matter eligibility two-part statutory analysis, as provided below.
Regarding Step 1,
Step 1 addresses whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter according to MPEP §2106.03. Claims 1-28 all fall within one of the four statutory categories.
Regarding Step 2A [prong 1],
The claimed invention recites an abstract idea according to MPEP §2106.04. Independent claim 1, also representative of independent claims 10 and 15 for the same abstract features, is underlined below which recite the following claim limitations, as an abstract idea.
Claims 1, 10 and 15:
Receiving (1) target workforce data and (2) candidate pool data associated with the target workforce data; randomly selecting a plurality of candidates from the candidate pool data, each candidate from the plurality of candidates associated with role data from the target workforce data;
generate a matching score distribution associated with the target workforce data and a candidate matching score from a plurality of candidate matching scores for each candidate from the plurality of candidates; executing a statistical model to generate a fit score from a plurality of fit scores for each candidate from the plurality of candidates, the plurality of fit scores including a plurality of percentile ranks within the matching score distribution;
filtering the plurality of candidates associated with the target workforce data, based on the plurality of fit scores and a fit score threshold, to produce a filtered candidate pool; extracting a plurality of natural language-based identifiers from a plurality of role data from the target workforce data; and
generating a recommendation for the target workforce data based on the plurality of fit scores of the filtered candidate pool and the plurality of natural language-based identifiers, the recommendation configured to refine the target workforce data by embedding a plurality of natural language-based identifiers into the target workforce data, to produce an updated target workforce data.
updating model parameters of the learning model using a first subset of fit scores from the plurality of fit scores until a pre-defined likelihood of correctness is obtained;
generate a tested learning model using a second subset of fit scores from the plurality of fit scores;
generate a validated second learning model by validating the tested second model using a third subset of fit scores from the plurality of fit scores;
execute, without using the matching score distribution or the statistical model, the validated second learning model to generate a fit score for a candidate not included in the plurality of candidates; and
generate a recommendation based on the fit score for the candidate.
The claim limitations above, under its broadest reasonable interpretation, fall under “Certain Methods of Organizing Human Activities” grouping of abstract ideas, and includes at least managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP §2106.04(a)(2)(II).
But for the recitation of generic implementation of computer system components, the claimed invention merely recites a process for managing personal behavior/relationships or interactions between people because the claimed steps recite filtering the plurality of candidates associated with the target workforce data and generating a recommendation for the target role data. Accordingly, since the claimed invention describes a process that falls under “Certain Methods of Organizing Human Activities” grouping, the claimed invention recites an abstract idea.
Regarding Step 2A [prong 2],
The judicial exception is not integrated into a practical application according to MPEP §2106.04(d). Claims 1, 10 and 15 include the following additional elements:
A system comprising :a processor; and a memory operatively coupled to the processor,
execute a machine learning model;
generate a trained second machine learning model by updating model parameters of the trained second machine learning model using a first subset of fit scores from the plurality of fit scores until a pre-defined likelihood of correctness is obtained by the trained second machine learning model;
generate a tested second machine learning model by testing the trained second machine learning model using a second subset of fit scores from the plurality of fit scores;
generate a validated second machine learning model by validating the tested second machine learning model using a third subset of fit scores from the plurality of fit scores;
execute, without using the matching score distribution or the statistical model, the validated second machine learning model to generate a fit score for a candidate not included in the plurality of candidates; and
generate a trained second machine learning model by updating model parameters of the trained second machine learning model using a first subset of fit scores from the plurality of fit scores until a pre-defined likelihood of correctness is obtained by the trained second machine learning model;
generate a tested second machine learning model by testing the trained second machine learning model using a second subset of fit scores from the plurality of fit scores;
generate a validated second machine learning model by validating the tested second machine learning model using a third subset of fit scores from the plurality of fit scores;
execute, without using the matching score distribution or the statistical model, the validated second machine learning model to generate a fit score for a candidate not included in the plurality of candidates; and
generate a recommendation based on the fit score for the candidate.
In particular, the additional elements cited above beyond the abstract idea are recited at a high-level of generality and simply equivalent to a generic recitation and basic functionality that amount to no more than mere instructions to apply the judicial exception using generic computer technology components.
The claimed invention merely provides an abstract-idea-based-solution implemented with generic computer processes and components recited at a high-level of generality (receiving, storing, determining, and comparing data) using computer instructions to implement the abstract idea on a computer, and merely “apply it” without any meaningful technological limits or any improvement to technology, technical field or improvement to the functioning of the computer itself.
Therefore, the additional elements fail to integrate the recited abstract idea into any practical application since they do not impose any non-generic meaningful limits on practicing the abstract idea. Thus, the claimed invention is directed to an abstract idea.
Regarding Step 2B,
The claimed invention does not include additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP §2106.05.
As discussed above, the claimed additional elements recited above amounts to no more than mere instructions to implement the abstract idea by adding the words “apply it” using generic computer components and functionality. See MPEP §2106.05(h). Mere instructions to apply the judicial exception using generic computer components are insufficient to provide an inventive concept. Furthermore, the claimed additional elements merely limit the abstract idea to be executed in a computer environment, thus do nothing more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h).
Considered as an ordered combination, the additional elements are claimed at a high-level of generality and add nothing that is not already present when the steps are considered separately. The sequence of the claimed limitations is equally generic and otherwise held to be abstract since the combination of these additional elements is no more than mere instructions to apply the judicial exception using generic computer components operating in their ordinary and generic capacities of what is typically expected of computers storing and updating data, and receiving and transmitting data between generic computer devices. The claimed invention is not patent eligible because the additional elements are merely invoked as tools to execute the abstract idea and thus are insufficient to amount to an inventive concept significantly more than the judicial exception.
As for dependent claims, all dependent on Claims 1, 10 and 15 comprise the following additional elements: wherein the machine learning model being a first trained machine learning model, the processor is further configured to: generate a trained second machine learning model. These additional elements merely further narrow and reiterate the same abstract ideas for receiving data, filtering and transmitting data using generic data storage and transmittal techniques with the same additional elements as recited above which provide nothing more than applying the abstract idea using generic computer technology components.
The additional elements do not provide any improvement to technology, technical field or improvement to the functioning of the computer itself, and at best simply applying the abstract idea executed in a general-purpose computer environment. Therefore the dependent claims are also directed to ineligible subject matter since they do not provide significantly more than the abstract idea itself.
Thus, after considering all claim elements in Claims 1-4, 10-13, 15-18, and 21-28 both individually and as an ordered combination, it has been determined that the claimed invention as a whole, is not enough to transform the abstract idea into a patent-eligible invention since nothing in the claim limitations provide significantly more than the abstract idea under 35 U.S.C. § 101.
Response to Amendment and Arguments
Applicant’s amendment and arguments have been acknowledged and considered however, they are unpersuasive.
The features of generating a trained second machine learning model by updating model parameters using a first subset of fit scores and generating a tested second machine learning model by testing the trained second machine learning model using a second subset of fit scores; and generating a validated second machine learning model by validating the tested second machine learning model using a third subset of fit scores from the plurality of fit scores are merely applied on data gathering and data processing to compare and identify patterns and scores in the data analysis as claimed. There is no indication in the disclosure of how any of the mentioned machine learning contain any technical inner-workings of the intelligence aspect of any AI algorithms since there is no disclosure that provides how the AI algorithm is implemented and integrated in any specialized technical structure that serves any specialized technical purpose other than merely being used as a tool implemented by the computer environment with no improvement to the technology or the computer.
Furthermore, the claims are not rooted in machine learning technology, and the claims do not solve a technical problem that only arises in AI or machine learning technology. MPEP § 2106.05(a). The AI models being referred to do nothing more than provide as a tool for computational instructions to be implemented in a computer processing environment, without improving the computer or technology. Thus, merely using these AI elements as additional tools and linking them to a computer processing environment are not sufficient to integrate the judicial exception into a practical application. The Specification fails to clearly evidence, how the use of a machine learning or neural networks, or any type of AI deep learning is an actual technological improvement over, or differs from, the general concept of these AI/learning models. Thus, the additional elements only serve to further limit the abstract idea utilizing the computer and AI/machine learning as a tool and generally link the use of the abstract idea to a particular technological environment, and hence fail to impose any meaningful limits on practicing the abstract idea.
Relevant Prior Art
As a whole, the claimed invention in this application is deemed to be directed to a nonobvious improvement over the closest prior art of record. The prior art of record does not anticipate nor render obvious the combination of limitations claimed in this application for the independent claims of this application.
Prior art, POLLI (US 20210264371) discloses identifying top candidates using a fitness score threshold, and natural language processing using a screening system configured to receive data from a plurality of users, including employees, or job candidates, however, does not teach nor suggest the specific claimed limitations as a whole, either alone or in combination.
Prior art, MA (US 20200311684) discloses producing a filtered candidate pool that includes a filtered subset of candidates. However, the specific claimed limitations in the allowed independent claims are neither taught nor suggested by Ma, either alone or in combination.
Prior art, Janapareddy (US 20200065770) discloses embedding natural language identifiers to generate an updated target role associated with an updated appeal value that is more appealing to validate or revise the listing, however, does not teach nor suggest the specific claimed limitations as a whole, either alone or in combination.
The prior art teachings as recited above fail to set forth any sufficient rationale for combining or otherwise modifying any of the relevant prior art to arrive at the claimed invention, as a whole. To arrive at the claimed invention with the precise combination of claimed features would not have been obvious to one of ordinary skill in the art without relying on improper hindsight to substantially reconstruct Applicant’s claimed invention.
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of References Cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA YESILDAG whose telephone number is (571)270-5066. Examiner interviews are available using the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. For sending Interview agendas, the Examiner’s direct fax number is (571) 270-6066. For filing Authorization for Internet Communication, please see https://www.uspto.gov/sites/default/files/documents/sb0439.pdf. The Examiner's Part-Time work schedule and general availability is typically 9:00 AM - 4:00 PM. If attempts to reach the Examiner are unsuccessful, the Examiner’s Supervisor, Lynda Jasmin, can be reached at (571) 272-6782.
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/Laura Yesildag/
Primary Patent Examiner, Art Unit 3629
Prosecution Insights