Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 are all the claims pending in the application.
Claims 1-3, 6, 13-17, 19, and 20 are amended.
Claims 1-20 are rejected.
The following is a Final Office Action in response to amendments and remarks filed January 24, 2025.
Response to Arguments
Regarding the 101 rejections, the rejections are maintained for the following reasons. First, Applicant asserts the claims reflect a technical solution to a technical problem of predicting job posting performance. Examiner respectfully does not find this assertion persuasive because predicting job posting performance is not a technical problem, it is a business or advertising problem, which is an abstract idea and an improvement in the abstract idea itself is not an improvement in technology, see MPEP 2106.05(a) (discussing Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019)).
Second, Applicant asserts the claims are analogous to Examples 37 and 39. Examiner respectfully does not find this assertion persuasive because it is not clear how the claims are analogous to Example 37 because the present claims do not involve the layout of a user interface and because, unlike Example 39, the claims were not rejected as reciting a mental process.
Please see below for the new 101 rejections of the claims as amended.
Regarding the 102 and 103 rejections, the rejections are withdrawn at least because the cited references do not teach grouping job postings, as claimed. Please see below for the new 103 rejections of the claims as amended.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
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.
Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying Step 1 to the claims it is determined that: claims are directed to a process 1-12; and claims are directed to a machine 13-20. Therefore, we proceed to Step 2.
Independent Claim 1
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Independent claim 1 recites an abstract idea. Specifically, independent claim 1 recites an abstract idea in the limitations (emphasized):
…training, by a web platform that hosts a job posting corpus, a machine learning model for predicting job posting engagement performance using the job posting corpus by:
performing feature extraction against job posting data of the job posting corpus to extract features from content associated with one or more job posting fields;
performing feature-based aggregation against the extracted features to determine feature-aggregated job posting groups of the job posting corpus;
performing pattern recognition against respective job posting fields of the feature- aggregated job posting groups to determine training weights; and
producing a trained machine learning model according to the training weights;
obtaining, by the web platform, first information associated with a job posting for publication via the web platform;
determining, by the web platform, an initial score for the job posting by the trained machine learning model evaluating a first combination of fields of the first information wherein the trained machine learning model evaluates the first combination of fields by applying, against content within the first combination of fields, first training weights associated with the first combination of fields;
determining, by the web platform using the trained machine learning model, one or more recommendations for increasing engagement performance for the job posting based on the initial score and the first information;
presenting, by the web platform prior to a publication of the job posting and within a graphical user interface rendered for the job posting, the initial score and interactive prompts for updating the job posting according to the one or more recommendations;
obtaining, by the web platform, second information associated with the job posting from one or more web forms accessible, via the interactive prompts of the graphical user interface, to a user device connected to the web platform;
determining, by the web platform in real-time in response to the second information, an updated score for the job posting by the trained machine learning model evaluating a second combination of fields of the first information and the second information, wherein the trained machine learning model evaluates the second combination of fields by applying, against content within the second combination of fields, second training weights associated with the second combination of fields; and
presenting, by the web platform prior to the publication of the job posting, the updated score within the graphical user interface.
These limitations recite an abstract idea because these limitations commercial or legal interaction (i.e., advertising). That is, these limitations essentially encompass reviewing job postings to make suggestions for improving the job posting (e.g., proof reading and editing job postings). Claims that encompass commercial or legal interaction (i.e., advertising) fall within the “Certain methods of Organizing Human Activity” grouping of abstract ideas. Claim 1 recites an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional limitations that integrate the abstract idea into a practical application.
The additional elements of claim 1 do not integrate the abstract idea into a practical application. Claim 1 recites additional elements in the limitations (emphasized):
…training, by a web platform that hosts a job posting corpus, a machine learning model for predicting job posting engagement performance using the job posting corpus by:
performing feature extraction against job posting data of the job posting corpus to extract features from content associated with one or more job posting fields;
performing feature-based aggregation against the extracted features to determine feature-aggregated job posting groups of the job posting corpus;
performing pattern recognition against respective job posting fields of the feature- aggregated job posting groups to determine training weights; and
producing a trained machine learning model according to the training weights;
obtaining, by the web platform, first information associated with a job posting for publication via the web platform;
determining, by the web platform, an initial score for the job posting by the trained machine learning model evaluating a first combination of fields of the first information wherein the trained machine learning model evaluates the first combination of fields by applying, against content within the first combination of fields, first training weights associated with the first combination of fields;
determining, by the web platform using the trained machine learning model, one or more recommendations for increasing engagement performance for the job posting based on the initial score and the first information;
presenting, by the web platform prior to a publication of the job posting and within a graphical user interface rendered for the job posting, the initial score and interactive prompts for updating the job posting according to the one or more recommendations;
obtaining, by the web platform, second information associated with the job posting from one or more web forms accessible, via the interactive prompts of the graphical user interface, to a user device connected to the web platform;
determining, by the web platform in real-time in response to the second information, an updated score for the job posting by the trained machine learning model evaluating a second combination of fields of the first information and the second information, wherein the trained machine learning model evaluates the second combination of fields by applying, against content within the second combination of fields, second training weights associated with the second combination of fields; and
presenting, by the web platform prior to the publication of the job posting, the updated score within the graphical user interface.
These additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of the web platform hosting a job posting corpus, performing steps in real-time, and a graphical user interface with interactive prompts and web forms, when considered individually or in combination, as claimed, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components, see MPEP 2106.05(f).
Second, the additional elements of training and using a machine learning model by applying weights in the various steps, as claimed, when considered individually or in combination, as claimed, do not integrate the abstract idea into a practical application because the additional elements are only mere instructions to apply the exception, see MPEP 2106.05(f). That is, the training and using machine learning withing weightings is recited sufficiently broadly such that it amounts to no more than a generic use of computing functions.
Third, the additional elements of obtaining first and second information, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of receiving data (i.e., receiving user input), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Fourth, the additional elements of presenting the initial and update score, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of sending data (i.e., displaying outputs), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claim 1 is directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 1 is not patent eligible.
Independent Claim 13
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Independent claim 13 recites an abstract idea. Specifically, independent claim 13 recites an abstract idea in the limitations (emphasized):
A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising:
producing, by a web platform that hosts a job posting corpus, a trained machine learning model according to training weights determined by:
performing feature extraction against job posting data of the job posting corpus to extract features from content associated with one or more job posting fields;
performing feature-based aggregation against the extracted features to determine feature-aggregated job posting groups of the job posting corpus; and
performing pattern recognition against respective job posting fields of the feature- aggregated job posting groups to determine the training weights;
determining, for a job posting for publication via the web platform, an initial score and one or more recommendations for increasing the initial score by the trained machine learning model evaluating a first combination of fields of first information associated with the job posting, wherein the trained machine learning model evaluates the first combination of fields by applying, against content within the first combination of fields, first training weights associated with the first combination of fields;
presenting, within a graphical user interface for the job posting, the initial score and interactive prompts for updating the job posting according to the one or more recommendations;
determining, in real-time in response to second information obtained by the web platform from one or more web forms accessible via the interactive prompts, an updated score for the job posting by the trained machine learning model evaluating a second combination of fields of the first information and the second information, wherein the trained machine learning model evaluates the second combination of fields by applying, against content within the second combination of fields, second training weights associated with the second combination of fields; and
presenting, prior to a publication of the job posting, the updated score within the graphical user interface.
These limitations recite an abstract idea because these limitations commercial or legal interaction (i.e., advertising). That is, these limitations essentially encompass reviewing job postings to make suggestions for improving the job posting (e.g., proof reading and editing job postings). Claims that encompass commercial or legal interaction (i.e., advertising) fall within the “Certain methods of Organizing Human Activity” grouping of abstract ideas. Claim 13 recites an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional limitations that integrate the abstract idea into a practical application.
The additional elements of claim 13 do not integrate the abstract idea into a practical application. Claim 13 recites additional elements in the limitations (emphasized):
A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising:
producing, by a web platform that hosts a job posting corpus, a trained machine learning model according to training weights determined by:
performing feature extraction against job posting data of the job posting corpus to extract features from content associated with one or more job posting fields;
performing feature-based aggregation against the extracted features to determine feature-aggregated job posting groups of the job posting corpus; and
performing pattern recognition against respective job posting fields of the feature- aggregated job posting groups to determine the training weights;
determining, for a job posting for publication via the web platform, an initial score and one or more recommendations for increasing the initial score by the trained machine learning model evaluating a first combination of fields of first information associated with the job posting, wherein the trained machine learning model evaluates the first combination of fields by applying, against content within the first combination of fields, first training weights associated with the first combination of fields;
presenting, within a graphical user interface for the job posting, the initial score and interactive prompts for updating the job posting according to the one or more recommendations;
determining, in real-time in response to second information obtained by the web platform from one or more web forms accessible via the interactive prompts, an updated score for the job posting by the trained machine learning model evaluating a second combination of fields of the first information and the second information, wherein the trained machine learning model evaluates the second combination of fields by applying, against content within the second combination of fields, second training weights associated with the second combination of fields; and
presenting, prior to a publication of the job posting, the updated score within the graphical user interface.
These additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of a non-transitory computer readable medium, the web platform hosting a job posting corpus, performing steps in real-time, and a graphical user interface with interactive prompts and web forms, when considered individually or in combination, as claimed, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components, see MPEP 2106.05(f).
Second, the additional elements of training and using a machine learning model by applying weights in the various steps, as claimed, when considered individually or in combination, as claimed, do not integrate the abstract idea into a practical application because the additional elements are only mere instructions to apply the exception, see MPEP 2106.05(f). That is, the training and using machine learning withing weightings is recited sufficiently broadly such that it amounts to no more than a generic use of computing functions.
Third, the additional elements of presenting the initial and update score, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of sending data (i.e., displaying outputs), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claim 13 is directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 13 is not patent eligible.
Independent Claim 16
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Independent claim 16 recites an abstract idea. Specifically, independent claim 16 recites an abstract idea in the limitations (emphasized):
…a memory; and a processor configured to execute instructions stored in the memory to:
produce a trained machine learning model according to training weights determined by:
performing feature extraction against job posting data of a job posting corpus to extract features from content associated with one or more job posting fields;
performing feature-based aggregation against the extracted features to determine feature-aggregated job posting groups of the job posting corpus; and
performing pattern recognition against respective job posting fields of the feature-aggregated job posting groups to determine the training weights;
determine, prior to a publication of a job posting, an initial score for the job posting and one or more recommendations for increasing the initial score by the trained machine learning model evaluating a first combination of fields of first information associated with the job posting, wherein the trained machine learning model evaluates the first combination of fields by applying, against content within the first combination of fields, first training weights associated with the first combination of fields; and
present, within a graphical user interface for the job posting prior to the publication of the job posting, the initial score and interactive prompts for updating the job posting according to the one or more recommendations.
These limitations recite an abstract idea because these limitations commercial or legal interaction (i.e., advertising). That is, these limitations essentially encompass reviewing job postings to make suggestions for improving the job posting (e.g., proof reading and editing job postings). Claims that encompass commercial or legal interaction (i.e., advertising) fall within the “Certain methods of Organizing Human Activity” grouping of abstract ideas. Claim 16 recites an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional limitations that integrate the abstract idea into a practical application.
The additional elements of claim 16 do not integrate the abstract idea into a practical application. Claim 16 recites additional elements in the limitations (emphasized):
…a memory; and a processor configured to execute instructions stored in the memory to:
produce a trained machine learning model according to training weights determined by:
performing feature extraction against job posting data of a job posting corpus to extract features from content associated with one or more job posting fields;
performing feature-based aggregation against the extracted features to determine feature-aggregated job posting groups of the job posting corpus; and
performing pattern recognition against respective job posting fields of the feature-aggregated job posting groups to determine the training weights;
determine, prior to a publication of a job posting, an initial score for the job posting and one or more recommendations for increasing the initial score by the trained machine learning model evaluating a first combination of fields of first information associated with the job posting, wherein the trained machine learning model evaluates the first combination of fields by applying, against content within the first combination of fields, first training weights associated with the first combination of fields; and
present, within a graphical user interface for the job posting prior to the publication of the job posting, the initial score and interactive prompts for updating the job posting according to the one or more recommendations.
These additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of the memory, processor, and a graphical user interface with interactive prompts, when considered individually or in combination, as claimed, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components, see MPEP 2106.05(f).
Second, the additional elements of training and using a machine learning model by applying weights in the various steps, as claimed, when considered individually or in combination, as claimed, do not integrate the abstract idea into a practical application because the additional elements are only mere instructions to apply the exception, see MPEP 2106.05(f). That is, the training and using machine learning withing weightings is recited sufficiently broadly such that it amounts to no more than a generic use of computing functions.
Third, the additional elements of presenting the initial score, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of sending data (i.e., displaying outputs), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claim 16 is directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 16 is not patent eligible.
Dependent Claims
The dependent claims are rejected under 35 USC 101 as directed to an abstract idea for the following reasons.
Claims 2 and 3 recite the additional elements of using an unsupervised machine learning model to assign different weights and inference the first information. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning techniques) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claim 4 recites the same abstract idea as the independent claims because the initial score being a measure of the posting against other postings is a part of proof reading advertisements.
Claims 5, 6, 19 and 20 recite the same abstract idea as independent claims because the job postings being analyzed based on the claimed elements is a part of proof reading advertisements.
Claims 5, 6, 19 and 20 further recites the additional elements of assigning different weights. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (natural language processing) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claim 7 recites the additional elements of using an natural language processing to assign different weights. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (natural language processing) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claims 8-10 recite the additional elements of assigning weights based on various inputs. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning techniques) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claim 11 recites the additional elements of using color when presenting the score. These additional elements, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of sending data (i.e., displaying outputs), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claim 12 recites the same abstract idea the second information reflecting the changes is a part of the proof reading (i.e., assessing whether the changes are beneficial).
Claims 14 recites the same abstract idea as the job postings being analyzed based on the claimed elements is a part of proof reading advertisements.
Claim 14 further recites the additional elements of using an unsupervised machine learning to assign weights based on various inputs. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning techniques) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claim 15 recites the additional elements of assigning different weights and inferencing the first information. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning techniques) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claim 17 recites limitations similar to various limitations claimed in claims 1 and 13. Accordingly the limitations in claim 17 are rejected for similar reasons those in claims 1 and 13.
Claim 18 recites the additional elements of visualizing the updated score. These additional elements do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of sending data (i.e., displaying outputs), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al, US Pub. No. 2020/0394592, herein referred to as “Shi” in view of Arran et al, US Pub. No. 2023/0252420, herein referred to as “Arran”.
Regarding claim 16, Shi teaches:
a memory; and a processor configured to execute instructions stored in the memory to (memory, processor and instructions, ¶¶[0070]-[0073]):
produce a trained machine learning model according to training weights determined by (trains machine-learning model to determine affinity score for a job posting which indicates how likely the corresponding user is to view the job posting, apply to the job posting, etc. ¶¶[0056]-[0057]; see also e.g., ¶¶[0043], [0059] discussing applying weighting learned during machine learning training):
performing feature extraction against job posting data of a job posting corpus to extract features from content associated with one or more job posting fields (extracts text from job posting, ¶¶[0037], [0041] and Fig. 2; see also e.g., ¶¶[0020], [0025], and Fig. 1 discussing analyzing database of job postings);
and performing pattern recognition against respective job posting fields to determine the training weights (machine learning model learns and applies weights to skills, ¶¶[0043], [0059]);
determine an initial score for the job posting (generates scores for skills using machine learning model, ¶¶[0023], [0042]; see also ¶¶[0021]-[0022] discussing machine learning)
and one or more recommendations for increasing the initial score by the trained machine learning model evaluating a first combination of fields of first information associated with the job posting (ranks skills, ¶[0045]; see also ¶[0016] noting purpose of system is to suggest skills to improve user engagement);
wherein the trained machine learning model evaluates the first combination of fields by applying, against content within the first combination of fields, first training weights associated with the first combination of fields (machine learning model learns and applies weights to skills, ¶¶[0043], [0059]);
and present, within a graphical user interface for the job the initial score (presents subset of scores skills on a screen of a computing device, ¶[0045]; see also Fig. 2 summarizing process)
and interactive prompts for updating the job posting according to the one or more recommendations (presents job skills for selection to job poster, ¶[0048] and Fig. 3).
However Shi does not explicitly teach:
prior to a publication of a job posting
Nevertheless, it would have been obvious, before the effective filing date of the claimed invention, to provide the suggested skills to add, e.g., as in Fig. 3 of Shi, before the job posting is published because it is proper to take into account not only specific teachings of a reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom, see MPEP 2144.01. That is, Shi teaches a system that recommends adding skills to a job posting to improve user engagement, etc., ¶[0016] and Fig. 3. One skilled in the art would infer this would be done prior to publishing the job posting because adding the skills after the job posting was published would accomplish nothing (i.e., there is no point to adding skill to a posting that will not be viewed).
However Shi does not teach but Arran does teach:
performing feature-based aggregation against the extracted features to determine feature-aggregated job posting groups of the job posting corpus (classifies job postings to categories, e.g., ¶¶[0005], [0059]; see also e.g., ¶¶[0054]-[0055] discussing training);
and performing pattern recognition against respective job posting fields of the feature-aggregated job posting groups to determine the training weights (applies weights as part of training and using neural network, ¶¶[0046], [0052]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring of Shi with the classification of Arran because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Shi is directed to improving user engagement with job postings, ¶[0016]. One of ordinary skill would have recognized the job postings would be further improved with the classification process for rating the quality of job postings taught by Arran.
Claim(s) 1-4, 10-13, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi and Arran further in view of Mathiesen et al, US Pub. No. 2021/0065094, herein referred to as “Mathiesen”.
Regarding claim 1, Shi teaches:
training, by a web platform that hosts a job posting corpus, a machine learning model a machine learning model for predicting job posting engagement performance using the job posting corpus by (trains machine-learning model to determine affinity score for a job posting which indicates how likely the corresponding user is to view the job posting, apply to the job posting, etc. ¶¶[0056]-[0057]; see also e.g., ¶¶[0018], [0067] discussing using web applications; and e.g., ¶¶[0020], [0025], and Fig. 1 discussing analyzing database of job postings; and e.g., ¶[0053] discussing training using job postings)
performing feature extraction against job posting data of the job posting corpus to extract features from content associated with one or more job posting fields (extracts text from job position, ¶¶[0037], [0041] and Fig. 2);
performing pattern recognition against respective job posting fields of the feature- aggregated to determine training weights (machine learning model learns and applies weights to skills, ¶¶[0043], [0059]);
and producing a trained machine learning model according to the training weights (machine learning model learns and applies weights to skills, ¶¶[0043], [0059]);
obtaining, by the web platform, first information associated with a job posting for publication via the web platform (extracts skills from job postings, e.g. ¶¶[0013], [0037]);
determining, by the web platform, an initial score for the job posting by the trained machine learning model evaluating a first combination of fields of the first information (generates scores for skills using machine learning model, ¶¶[0023], [0042]; see also ¶¶[0021]-[0022] discussing machine learning; and ¶¶[0051]-[0053] discussing training),
wherein the trained machine learning model evaluates the first combination of fields by applying, against content within the first combination of fields, first training weights associated with the first combination of fields (machine learning model learns and applies weights to skills, ¶¶[0043], [0059]);
determining, by the web platform using the trained machine learning model, one or more recommendations for increasing engagement performance for the job posting based on the initial score and the first information (ranks skills, ¶[0045]; see also ¶[0016] noting purpose of system is to suggest skills to improve user engagement);
presenting, by the web platform and within a graphical user interface rendered for the job posting, the initial score (presents subset of scores skills on a screen of a computing device, ¶[0045]; see also Fig. 2 summarizing process);
and interactive prompts for updating the job posting according to the one or more recommendations (presents job skills for selection to job poster, ¶[0048] and Fig. 3);
obtaining, by the web platform, second information associated with the job posting from one or more web forms accessible, via the interactive prompts of the graphical user interface, to a user device connected to the web platform (receives input selecting a subset of the presented skills, ¶[0047]; see also e.g., ¶¶[0018], [0045] discussing web applications and user devices);
wherein the trained machine learning model evaluates the second combination of fields by applying, against content within the second combination of fields, second training weights associated with the second combination of fields (machine learning model learns and applies weights to skills, ¶¶[0043], [0059]; see also e.g., ¶¶[0053]-[0054] discussing second machine learning model).
However Shi does not explicitly teach:
prior to a publication of the job posting
Nevertheless, it would have been obvious, before the effective filing date of the claimed invention, to provide the suggested skills to add, e.g., as in Fig. 3 of Shi, before the job posting is published because it is proper to take into account not only specific teachings of a reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom, see MPEP 2144.01. That is, Shi teaches a system that recommends adding skills to a job posting to improve user engagement, etc., ¶[0016] and Fig. 3. One skilled in the art would infer this would be done prior to publishing the job posting because adding the skills after the job posting was published would accomplish nothing (i.e., there is no point to adding skill to a posting that will not be viewed).
However Shi also does not explicitly teach:
and presenting the updated score within the graphical user interface.
Nevertheless, it would have been obvious at the time of filing to display an updated score because duplication of parts is obvious unless a new and unexpected result is produced, see MPEP 2144.04.VI.B. That is, Shi teaches displaying a score, ¶[0045]. Examiner finds no evidence displaying a second, updated score would produce new or unexpected results.
However Shi does not teach but Arran does teach:
performing feature-based aggregation against the extracted features to determine feature-aggregated job posting groups of the job posting corpus (classifies job postings to categories, e.g., ¶¶[0005], [0059]; see also e.g., ¶¶[0054]-[0055] discussing training);
and performing pattern recognition against respective job posting fields of the feature- aggregated job posting groups to determine training weights (applies weights as part of training and using neural network, ¶¶[0046], [0052]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring of Shi with the classification of Arran because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Shi is directed to improving user engagement with job postings, ¶[0016]. One of ordinary skill would have recognized the job postings would be further improved with the classification process for rating the quality of job postings taught by Arran.
However the combination of Shi and Arran does not teach but Mathiesen does teach:
determining, in real-time in response to the second information, an updated score for the job posting by evaluating the first information and the second information against the one or more models (determines second performance metric to compare to first performance metric of the job posting, ¶¶[0073]-[0074]).
That is, Shi teaches scoring job postings and Mathiesen teaches assessing performance of job postings based on other job postings to determine the significance of missing information to suggest additional information, e.g. Fig. 2 and ¶[0061] and effects to user interactions of changes to the job posting. Thus the combination of Shi, Arran and Mathiesen teaches determining the difference between job postings with and without various information (i.e., determining the significant of addition information to a job posting).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring of Shi and Arran with the suggesting corrective actions of Mathiesen because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized users of Shi and Arran would likely not only be interested in suggestions for adding skills to the job posting, but also would be interested in other suggestions and accordingly would have modified Shi to include the various other suggestions taught by Mathiesen.
Regarding claim 2, the combination of Shi, Arran and Mathiesen teaches all the limitations of claim 1 and Shi further teaches:
machine learning model trained to recognize patterns in one or more criteria of untagged job posting data from the job posting corpus for each of multiple job types and multiple job locations (trained based on job postings, ¶[0053]; see also e.g. ¶[0036] discussing examples of different jobs and ¶[0061] discussing location).
and wherein, based on the patterns, the unsupervised machine learning model assigns different weights to the one or more criteria for different job type and job location combinations derived from the multiple job types and the multiple job locations (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]).
However Shi does not teach but Arran does teach
wherein the one or more models include an unsupervised machine learning model (unsupervised machine learning, ¶¶[0044], [0048])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring of Shi with the classification of Arran because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Shi is directed to improving user engagement with job postings, ¶[0016]. One of ordinary skill would have recognized the job postings would be further improved with the classification process for rating the quality of job postings taught by Arran.
Regarding claim 3, the combination of Shi, Arran and Mathiesen teaches all the limitations of claim 2 and Shi further teaches:
wherein determining the initial score for the job posting comprises: determining from amongst the different weights the different weights to use the first training weights for the job posting based on a job type and job location combination associated with the job posting (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]; see also e.g. ¶[0036] discussing examples of different jobs and ¶[0061] discussing location);
and inferencing, using the unsupervised machine learning model, the first information against at least some of the one or more criteria according to the first training weights (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]).
Regarding claim 4, the combination of Shi, Arran and Mathiesen teaches all the limitations of claim 3 and Shi further teaches:
wherein the initial score represents a qualitative measure of the job posting against other job postings of the job type and job location combination (first performance metric of the job posting is compare against other jobs, ¶¶[0073]-[0074]; see also ¶¶[0013], [0039] discussing similar job types and locations).
Regarding claim 10, the combination of Shi, Arran and Mathiesen teaches all the limitations of claim 2 and Shi further teaches:
wherein ones of the different weights are updated over time based on tracked user engagements with job postings of the job posting corpus (assigns weights to ea