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 1-5, 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 April 23, 2026.
Response to Arguments
Regarding the 103 rejections, the rejections are withdrawn because the previous rejections do not address each and every limitation of the newly amended claims. Please see below for the new rejections of the claims as amended.
Additionally, please note, Applicant asserts the previously cited references do not teach evaluating the combination of job type and location. Examiner respectfully does not find this assertion persuasive because Shi teaches analyzing the specific job and location, ¶¶[0043], ¶[0061], as does Westerheide, ¶¶[0055], [0057]-[0058].
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 § 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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub. No. 2020/0394592, herein referred to as “Shi” in view of Westerheide et al, US Pub. No. 2022/0067665, herein referred to as “Westerheide”, further in view of Mathiesen et al, US Pub. No. 2021/0065094, herein referred to as “Mathiesen”; further in view of Official Notice; further in view of Lear et al, US Pub. No. 2024/0311861, herein referred to as “Lear”.
Regarding claim 1, Shi teaches:
training, by a web platform associated with a job posting corpus, a first machine learning model (trains machine-learning model, ¶¶[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)
to recognize patterns in 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),
according to training weights determined based on features extracted from content associated with one or more job posting fields associated with job posting data of the job posting corpus (learns weights for different features, ¶¶[0022], [0043]; and features include job-side features, ¶¶[0024]-[0025]; see also ¶[0059] discussing weighting skills in the job posting),
obtaining, by the web platform and from a user device connected to 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]; see also ¶[0018] discussing various user devices);
determining, by the web platform, an initial score for the job posting by the first machine learning model (generates scores for skills using machine learning model, ¶¶[0023], [0042]; see also ¶¶[0021]-[0022] discussing machine learning)
evaluating a combination of a specific job type and a specific job location of the first information against content the first information representing values of the criteria (identifies attributes for the specific job, ¶[0043], including the job’s location, ¶[0061]),
wherein evaluating the combination of the specific job type and the specific job location against the content representing the values of the criteria includes the first machine learning model applying first training weights that are associated with the combination of the specific job type and the specific job location (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]; and features include specific job, ¶[0043] and location, ¶[0061]; see also ¶¶[0022], [0043] discussing learning weights for features);
determining, by the web platform using the first 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);
outputting, by the web platform and to the user device, first rendering instructions for causing the user device to display a graphical user interface rendered for the job posting (presents subset of scores skills on a screen of a computing device, ¶[0045]; see also Fig. 2 summarizing process),
wherein the graphical user interface according to the first rendering instructions visually identifies the initial score (presents subset of scores skills on a screen of a computing device, ¶[0045]; see also Fig. 2 summarizing process),
and interactive visual elements each corresponding to a different category of recommendation of the one or more recommendations the one or more recommendations and connected to a different web form of one or more web forms accessible for obtaining further information associated with the corresponding different category of recommendation (presents job skills for selection to job poster, ¶[0048] and Fig. 3; see also e.g., ¶¶[0018], [0045] discussing web applications and user devices);
obtaining, by the web platform, second information associated with the job posting from one or more web forms accessed based on user interactions with corresponding ones of the interactive visual elements within of the graphical user interface at the user device (receives input selecting a subset of the presented skills, ¶[0047]; see also e.g., ¶¶[0018], [0045] discussing web applications and user devices).
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 does not teach but Westerheide does teach:
the first machine learning model applying first training weights that are associated with the combination of the specific job type and the specific job location and based on first output of a second machine learning model trained for natural language processing of job postings (determines weights based on performing natural language processing of job requisition data, ¶[0098]; see also e.g., ¶¶[0055], [0057]-[0058] discussing applying weights to features including occupation roles and location);
evaluating the combination of the specific job type and the specific job location of the first information against content of the second information representing updated values of the criteria, wherein evaluating the combination of the specific job type and the specific job location against the content representing the updated values of the criteria includes the first machine learning model applying second training weights that are associated with the combination of the specific job type and the specific job location and based on second output of the second machine learning model (determines weights based on performing natural language processing of job requisition data, ¶[0098]; see also ¶[0089] discussing other machine learning models; and ¶[0104] discussing using machine learning to make predictions about job postings; and e.g., ¶¶[0055], [0057]-[0058] discussing applying weights to features including occupation roles and location);
and publishing, by the web platform after the outputting of the second rendering instructions to the user device, the job posting to the web platform for viewing by users of the web platform (job is posted by hiring entity, ¶[0049]).
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 natural language processing of Westerheide 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 understood existing job postings likely involve unstructured text and this understructure text would need to be analyzed with natural language processing to understand it before further analyses could be performed.
However the combination of Shi and Westerheide does not teach but teach Mathiesen does teach:
wherein the criteria for a given job posting of the untagged job posting data corresponds to a job posting detail level addressing a degree to which the given job posting is filled out and a quality of details of the given job posting (issues analyzed includes too short of a description and missing salary information, ¶¶[0050]-[0051] and Fig. 2),
and job posting compensation information addressing a competitiveness of compensation offered within the given job posting (issues analyzed includes missing salary information, ¶¶[0050]-[0051] and Fig. 2);
determining, by the web platform in real-time in response to obtaining the second information, an updated score for the job posting by the first machine learning model evaluating the combination of the specific job type and the specific job location of the first information against content of the second information representing updated values of the criteria (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, Westerheide and Mathiesen teaches determining the difference between job postings with and without various information (i.e., determining the significance of additional 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 Westerheide 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.
However the combination of Shi, Westerheide and Mathiesen does not teach:
wherein the graphical user interface according to the first rendering instructions includes a color wheel visualization
Nevertheless, Examiner finds claim 1 obvious because Examiner takes official notice that displaying score using a colors wheel is known and obvious and modifying the combination of Shi and Westerheide to display the score as a color wheel would have been obvious to make the user interface more visually appealing.
However the combination of Shi, Westerheide, Mathiesen, Official Notice does not teach but Lear does teach:
an inferred job posting market competitiveness addressing a supply of job postings in a market of the given job posting relative to a demand for job seekers to fulfill the job postings in the market (determines compensation for individual based on marketplace, ¶[0032]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring and suggesting corrective actions of Shi, Westerheide, Mathiesen, Official Notice with the compensation determination of Lear 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 Mathiesen would likely also be interested in suggestions for salaries for the job postings and accordingly would have modified Shi, Westerheide, Mathiesen, Official Notice to analyze salary information as taught by Lear.
However the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear does not explicitly teach:
and outputting, by the web platform and to the user device prior to the publication of the job posting, second rendering instructions for causing the user device to display the graphical user interface, wherein the graphical user interface according to the second rendering instructions includes an update to the color wheel visualization to visually identify the updated score
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] and Mathiesen teaches determining a second score, ¶¶[0073]-[0074]. Examiner finds no evidence displaying the second, updated score would produce new or unexpected results.
Regarding claim 2, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 1 and Shi further teaches:
wherein, based on the patterns, the 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 the combination of Shi, Westerheide, Mathiesen and Official Notice does not teach but Lear does teach
wherein the one or more models include an unsupervised machine learning model (unsupervised machine learning, ¶[0028])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring of Shi, Westerheide, Mathiesen and Official Notice with the unsupervised machine learning of Lear 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 the analysis may be improved by using unsupervised machine learning (i.e., in situations where unsupervised learning is preferrable).
Regarding claim 3, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear 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 the combination of the specific job type and job location (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, Westerheide, Mathiesen, Official Notice and Lear 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 combination of specific job type and job location (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 5, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 2 and Mathiesen further teaches:
the first information includes a job posting detail level for the job posting and job posting compensation information for the job posting (issues analyzes includes too short of a description and missing salary information, ¶¶[0050]-[0051] and Fig. 2).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring of Shi and Westerheide 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 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 6, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 5 and Shi further teaches:
a first weight of the first training weights, a second weight of first training weights, and a third weight of the first training weights (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]).
However the combination of Shi and Westerheide does not teach but Mathiesen does teach:
wherein the initial score is determined based on a first sub-score determined based on the job posting detail level for the job posting and, a second sub-score determined based on the inferred job posting market competitiveness for the job posting, and a third sub-score determined based on the job posting compensation information for the job posting (performance metrics include too short of a description, missing benefits and/or salary, and too many low quality application, ¶¶[0050]-[0052] and Fig. 2),
wherein the one or more recommendations indicate to change details for the job posting when the first sub-score is below a first threshold, wherein the one or more recommendations indicate to increase a priority level of the job posting when the second sub-score is below a second threshold, and wherein the one or more recommendations indicate to change compensation information for the job posting when the third sub-score is below a third threshold (provides resolutions for the various issues, ¶¶[0050]-[0052] and Fig. 2; see also ¶[0013] discussing performance being below threshold).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring of Shi and Westerheide 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 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 and Westerheide to include the various other suggestions taught by Mathiesen.
Regarding claim 7, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 5 and Shi further teaches:
wherein output of the second machine learning mode corresponds quality of details included in job postings of a given job type and job location combination within the job posting corpus (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]).
Regarding claim 8, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 5 and Shi further teaches:
wherein a weight assigned to the inferred job posting market competitiveness for a given job type and job location combination is based on a volume of job postings of the given job type and job location combination within the job posting corpus (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]).
Regarding claim 9, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 5 and Shi further teaches:
a weight assigned to the job posting compensation information for a given job type and job location combination (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]).
However the combination of Shi, Westerheide, Mathiesen, and Official Notice does not teach but Lear does teach:
is based on an average compensation value computed for job postings of the given job type and job location combination within the job posting corpus (determines compensation based on market value mean, ¶¶[0230]-[0234]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring and suggesting corrective actions of Shi, Westerheide, Mathiesen, and Official Notice with the compensation determination of Lear 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, Westerheide, Mathiesen, and Official Notice would likely also be interested in suggestions for salaries for the job postings and accordingly would have modified Shi and Mathiesen to analyze salary information as taught by Lear.
Regarding claim 10, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear 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 each feature of the machine learning model, ¶¶[0043], [0055], and scores are adjusted based on feedback, ¶¶[0032], [0064]).
Regarding claim 11, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 1 and Shi further teaches:
wherein rendering instructions include instructions for updating the color wheel visualization to reflect the updated score in real-time in response to the updated score (generates scores in real-time, ¶[0043]).
Regarding claim 12, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 1 and Mathiesen further teaches:
wherein the second information represents changes to the first information based on the one or more recommendations (tracks edits of job postings, ¶[0037]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring of Shi and Westerheide 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 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 13 Shi teaches:
A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising (memory and instructions, ¶¶[0070]-[0072]):
producing, by a web platform associated with a job posting corpus, a first machine learning model trained (trains machine-learning model, ¶¶[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)
to recognize patterns in 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),
according to training weights determined based on features extracted from content associated with one or more job posting fields associated with job posting data of the job posting corpus (learns weights for different features, ¶¶[0022], [0043]; and features include job-side features, ¶¶[0024]-[0025]; see also ¶[0059] discussing weighting skills in the job posting),
determining, for a job posting for publication via the web platform, an initial score (generates scores for skills using machine learning model, ¶¶[0023], [0042])
and one or more recommendations for increasing the initial score (ranks skills, ¶[0045]; see also ¶[0016] noting purpose of system is to suggest skills to improve user engagement);
by the first machine learning model evaluating a combination of a specific job type and a specific job location of first information associated with the job posting against content the first information representing values of the criteria (identifies attributes for the specific job, ¶[0043], including the job’s location, ¶[0061]; see also ¶¶[0021]-[0022] discussing machine learning)),
wherein evaluating the combination of the specific job type and the specific job location against the content representing the values of the criteria includes the first machine learning model
applying first training weights that are associated with the combination of the specific job type and the specific job location (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]; and features include specific job, ¶[0043] and location, ¶[0061]; see also ¶¶[0022], [0043] discussing learning weights for features);
outputting first rendering instructions for causing a display of a graphical user interface for the job posting, wherein the graphical user interface according to the first rendering instructions visually identifies 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; see also e.g., ¶¶[0018], [0045] discussing web applications and user devices);
in real-time in response to second information obtained by the web platform from one or more web forms accessed based on user interactions with corresponding ones of the interactive prompts (receives input selecting a subset of the presented skills, ¶[0047]; see also e.g., ¶¶[0018], [0045] discussing web applications and user devices).
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 does not teach but Westerheide does teach:
the first machine learning model applying first training weights that are associated with the combination of the specific job type and the specific job location and based on first output of a second machine learning model trained for natural language processing of job postings (determines weights based on performing natural language processing of job requisition data, ¶[0098]; see also e.g., ¶¶[0055], [0057]-[0058] discussing applying weights to features including occupation roles and location);
evaluating the combination of the specific job type and the specific job location of the first information against content of the second information content within the second combination of fields representing updated values of the criteria, wherein evaluating the combination of the specific job type and the specific job location against the content representing the updated values of the criteria includes the first machine learning model applying second training weights that are associated with the second combination of fields the specific job type and the specific job location and based on second output of the second machine learning model (determines weights based on performing natural language processing of job requisition data, ¶[0098]; see also e.g., ¶¶[0055], [0057]-[0058] discussing applying weights to features including occupation roles and location);
and publishing the job posting to the web platform for viewing by users of the web platform (job is posted by hiring entity, ¶[0049]).
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 natural language processing of Westerheide 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 understood existing job postings likely involve unstructured text and this understructure text would need to be analyzed with natural language processing to understand it before further analyses could be performed.
However the combination of Shi and Westerheide does not teach but teach Mathiesen does teach:
wherein the criteria for a given job posting of the untagged job posting data corresponds to a job posting detail level addressing a degree to which the given job posting is filled out and a quality of details of the given job posting (issues analyzed includes too short of a description and missing salary information, ¶¶[0050]-[0051] and Fig. 2),,
and job posting compensation information addressing a competitiveness of compensation offered within the given job posting (issues analyzed includes missing salary information, ¶¶[0050]-[0051] and Fig. 2);
determining, in real-time in response to second information obtained by the web platform from one or more web forms accessed based on user interactions with corresponding ones of the interactive prompts, an updated score for the job posting by the first machine learning model (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, Westerheide and Mathiesen teaches determining the difference between job postings with and without various information (i.e., determining the significance of additional 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 Westerheide 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.
However the combination of Shi, Westerheide and Mathiesen does not teach:
wherein the graphical user interface according to the first rendering instructions includes a color wheel visualization
Nevertheless, Examiner finds claim 13 obvious because Examiner takes official notice that displaying score using a colors wheel is known and obvious and modifying the combination of Shi and Westerheide to display the score as a color wheel would have been obvious to make the user interface more visually appealing.
However the combination of Shi, Westerheide, Mathiesen, Official Notice does not teach but Lear does teach:
an inferred job posting market competitiveness addressing a supply of job postings in a market of the given job posting relative to a demand for job seekers to fulfill the job postings in the market (determines compensation for individual based on marketplace, ¶[0032]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring and suggesting corrective actions of Shi, Westerheide, Mathiesen, Official Notice with the compensation determination of Lear 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 Mathiesen would likely also be interested in suggestions for salaries for the job postings and accordingly would have modified Shi, Westerheide, Mathiesen, Official Notice to analyze salary information as taught by Lear.
However the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear does not explicitly teach:
outputting, prior to the publication of the job posting, second rendering instructions for causing a display of the graphical user interface, wherein the graphical user interface according to the second rendering instructions includes an update to the color wheel visualization to visually identify the updated score.
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] and Mathiesen teaches determining a second score, ¶¶[0073]-[0074]. Examiner finds no evidence displaying the second, updated score would produce new or unexpected results.
Regarding claim 14, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 13 and Shi further teaches:
wherein, based on the patterns, the 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]).
Regarding claim 15, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 14 and Shi further teaches:
wherein the first training weights include a first weight corresponding to job posting detail level of the job posting, a second weight corresponding to an inferred job posting market competitiveness of the job posting, and a third weight corresponding to job posting compensation information for the job posting (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]);
and inferencing, using the first machine learning model, the first information according to the first weight, the second weight, and the third weight (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]).
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, according to training weights determined based on features extracted from content associated with one or more job posting fields associated with job posting data of a job posting corpus (learns weights for different features, ¶¶[0022], [0043]; and features include job-side features, ¶¶[0024]-[0025]; see also ¶[0059] discussing weighting skills in the job posting),
a first machine learning model trained (trains machine-learning model, e.g., ¶¶[0056]-[0057]);
to recognize patterns in 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),
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 first machine learning model (ranks skills, ¶[0045]; see also ¶[0016] noting purpose of system is to suggest skills to improve user engagement);
evaluating a combination of a specific job type and a specific job location of first information associated with the job posting against content of the first information representing values of the criteria (identifies attributes for the specific job, ¶[0043], including the job’s location, ¶[0061]),
wherein evaluating the combination of the specific job type and the specific job location against the content representing the values of the criteria includes the first machine learning model applying first training weights that are associated with the combination of the specific job type and the specific job location (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]; and features include specific job, ¶[0043] and location, ¶[0061]).
output rendering instructions to cause a display of a graphical user interface for the job posting, wherein the graphical user interface according to the rendering instructions visually identifies 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 Westerheide does teach:
first training weights that are associated with the combination of the specific job type and the specific job location and based on output of a second machine learning model trained for natural language processing of job postings (determines weights based on performing natural language processing of job requisition data, ¶[0098]; see also e.g., ¶¶[0055], [0057]-[0058] discussing applying weights to features including occupation roles and location)
and publish the job posting to a web platform (job is posted by hiring entity, ¶[0049]).
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 natural language processing of Westerheide 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 understood existing job postings likely involve unstructured text and this understructure text would need to be analyzed with natural language processing to understand it before further analyses could be performed.
However the combination of Shi and Westerheide does not teach but teach Mathiesen does teach:
wherein the criteria for a given job posting of the untagged job posting data corresponds to a job posting detail level addressing a degree to which the given job posting is filled out and a quality of details of the given job posting (issues analyzed includes too short of a description and missing salary information, ¶¶[0050]-[0051] and Fig. 2);
and job posting compensation information addressing a competitiveness of compensation offered within the given job posting (issues analyzed includes missing salary information, ¶¶[0050]-[0051] and Fig. 2).
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, Westerheide and Mathiesen teaches determining the difference between job postings with and without various information (i.e., determining the significance of additional 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 Westerheide 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.
However the combination of Shi, Westerheide and Mathiesen does not teach:
wherein the graphical user interface according to the rendering instructions includes a color wheel visualization that visual identifies the initial score
Nevertheless, Examiner finds claim 16 obvious because Examiner takes official notice that displaying score using a colors wheel is known and obvious and modifying the combination of Shi and Westerheide to display the score as a color wheel would have been obvious to make the user interface more visually appealing.
However the combination of Shi, Westerheide, Mathiesen, Official Notice does not teach but Lear does teach:
an inferred job posting market competitiveness addressing a supply of job postings in a market of the given job posting relative to a demand for job seekers to fulfill the job postings in the market (determines compensation for individual based on marketplace, ¶[0032]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring and suggesting corrective actions of Shi, Westerheide, Mathiesen, Official Notice with the compensation determination of Lear 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 Mathiesen would likely also be interested in suggestions for salaries for the job postings and accordingly would have modified Shi, Westerheide, Mathiesen, Official Notice to analyze salary information as taught by Lear.
Regarding claim 17, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear and Westerheide further teaches:
by the first machine learning model evaluating the combination of the specific job type and the specific job location of the first information against content of the second information representing updated values of the criteria (determines weights based on performing natural language processing of job requisition data, ¶[0098]; see also e.g., ¶¶[0055], [0057]-[0058] discussing applying weights to features including occupation roles and location),
wherein evaluating the combination of the specific job type and the specific job location against the content representing the updated values of the criteria includes the first machine learning model applying, second training weights that are associated with the combination of the specific job type and the specific job location and based on second output of the second machine learning model (determines weights based on performing natural language processing of job requisition data, ¶[0098]; see also e.g., ¶¶[0055], [0057]-[0058] discussing applying weights to features including occupation roles and location);
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 natural language processing of Westerheide 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 understood existing job postings likely involve unstructured text and this understructure text would need to be analyzed with natural language processing to understand it before further analyses could be performed.
However the combination of Shi and Westerheide does not teach but teach Mathiesen does teach:
determine, in real-time in response to second information obtained from one or more web forms accessed based on user interactions with the interactive prompts, an updated score for the job posting (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, Westerheide and Mathiesen teaches determining the difference between job postings with and without various information (i.e., determining the significance of additional 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 Westerheide 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.
However the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear does not explicitly teach:
and output, prior to the publication of the job posting, second rendering instructions to cause a display of the graphical user interface, wherein the graphical user interface according to the second rendering instructions includes an update to the color wheel visualization to visually identify the updated score.
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] and Mathiesen teaches determining a second score, ¶¶[0073]-[0074]. Examiner finds no evidence displaying the second, updated score would produce new or unexpected results.
Regarding claim 18, the combination Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 17 and Shi further teaches:
wherein the initial score is presented within the graphical user interface via a visualization (presents subset of scores skills on a screen of a computing device, ¶[0045]; see also Fig. 2 summarizing process);
and wherein, to output the second rendering instructions, the processor is configured to execute the instructions to: update the color wheel visualization to in real-time in response to the updated score (generates scores in real-time, ¶[0043]).
Regarding claim 19, the combination of Shi, Westerheide, Mathiesen, Official Notice and Lear teaches all the limitations of claim 16 and Shi further teaches:
wherein the trained machine learning model assigns different weights to one or more criteria for different job type and job location combinations derived from multiple job types and multiple job location (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]).
Regarding claim 20, the combination of Shi, Westerheide, Mathiesen, Official Notice, and Lear teaches all the limitations of claim 19 and Shi further teaches:
wherein, to determine the initial score and the one or more recommendations, the processor is configured to execute the instructions to: determine from amongst the different weights, to use the first training weights for the job posting (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055]);
and inference, using the trained machine learning model, the first information associated with the job posting according to the first weight, the second weight, and the third weight (assigns weights to each feature of the machine learning model, ¶¶[0043], [0055])..
However Shi and Westerheide does not teach but Mathiesen does teach:
A job posting detail level of the job posting, job posting compensation information for the job posting (issues analyzes includes too short of a description and missing salary information, ¶¶[0050]-[0051] and Fig. 2).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring of Shi and Westerheide 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 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.
However the combination of Shi, Westerheide, Mathiesen, and Official Notice does not teach but Lear does teach:
an inferred job posting market competitiveness of the job posting (determines compensation for individual based on marketplace, ¶[0032]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job posting scoring and suggesting corrective actions of Shi and Mathiesen with the compensation determination of Lear 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, Westerheide, Mathiesen, and Official Notice would likely also be interested in suggestions for salaries for the job postings and accordingly would have modified Shi and Mathiesen to analyze salary information as taught by Lear.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 10-6.
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/BRENDAN S O'SHEA/
Examiner, Art Unit 3626
Prosecution Insights