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 AIA .
Status of Claims
This communication is a Final Office action in response to communications received on 01/15/2026. Claims 1, 5, 8, 11, 15, 18 and 20 have been amended. Claims 3 and 13 have been previously canceled. Therefore, claims 1-2, 4-12 and 14-22 are currently pending and have been addressed below.
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 1-2, 4-12 and 14-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories When considering subject matter eligibility under 35 U.S.C. § 101, it must 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 (i.e., Step 1). In the instant case, claims 1-2, 4-10 and 21-22 are directed to a method (i.e., a process). Claims 11-12 and 14-19 are directed to an information handling device (i.e. a machine). Claim 20 is directed to a product comprising a computer-readable storage device (i.e. an article of manufacture). Examiner notes, Applicant’s specification, para 0053, recites: “a storage device is not a signal and is not to be construed as being transitory signals per se”. Thus, each of these claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1-2, 4-12 and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Representative independent claim 1, analogous to independent claims 11 and 20 recites:
A method, the method comprising: displaying a job search for receiving input from a user and displays visual output to the user; receiving a pool of jobs, wherein the receiving of the pool of jobs comprises accessing job listings and determining job requirements for the job listings by identifying context contained within the job listings by analyzing the job listings, wherein the job search application recognizes entities within the job listings using the language analysis techniques and assigning a label to the entities; filtering jobs within the pool of jobs to remove jobs unavailable for formally incarcerated individuals, wherein the filtering comprises comparing the context within the job listings to rules identifying context related to unavailable jobs, wherein the jobs unavailable for formally incarcerated individuals are removed from the pool of jobs to create a filtered pool of jobs; categorizing each of the jobs within the filtered pool of jobs into a job category based upon the requirements of each of the jobs, wherein each of the job categories has defined requirements based upon the jobs contained within a job category, wherein the requirements are defined utilizing at least a secondary information source and wherein the requirements for a given of the job categories applies to at least a majority of the jobs within the given of the job categories, wherein at least one of the requirements identifies types of convictions not allowed for a given of the job categories, wherein the categorizing comprises matching the requirements of a given of the jobs to the requirements of a job category; receiving a plurality of job-related input from a user, wherein the plurality of job-related input comprises input from the user related to job requirements; filtering the filtered pool of jobs into a final pool of jobs based upon the job-related input by correlating the job-related input to requirements of the job categories and removing jobs contained within a job category that do not satisfy the job-related input; and displaying the final pool of jobs.
The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under the abstract groupings of:
Mental Processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion (claim 1 recites for example, “receiving a pool of jobs, wherein the receiving of the pool of jobs comprises job listings”, “determining job requirements for the job listings”, “analyzing the job listings”, “filtering jobs within the pool of jobs to remove jobs unavailable for formally incarcerated individuals”, “comparing the context within the job listings to rules identifying context related to unavailable jobs”; “categorizing each of the jobs within the filtered pool of jobs”, “identifies types of convictions not allowed for a given of the job categories”; “receiving a plurality of job-related input from a user”.) Concepts performed in the human mind as mental processes because the steps of receiving, identifying, determining, comparing, categorizing and analyzing data mimic human thought processes of observation, evaluation, judgement and opinion, perhaps with paper and pencil, where data interpretation is perceptible in the human mind. See In re TLI Commc’ns LLCPatentLitig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)).
Certain methods of organizing human activity (commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations; (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)). As the claims discuss providing a job search, receiving input from a user, receiving, at the job search application a pool of jobs; filtering, jobs within the pool of jobs to remove jobs unavailable for formally incarcerated individuals; categorizing each of the jobs into a job category, wherein each of the job categories has defined requirements based upon the jobs contained within a job category, receiving a plurality of job-related input from a user, wherein the plurality of job-related input comprises input from the user related to job requirements; filtering the pool of jobs based upon the job-related input by correlating the job-related input to requirements of the job categories and removing jobs contained within a job category that do not satisfy the job-related input and displaying the filtered pool of jobs, which is a clear business relation, and one of certain methods of organizing human activity.
Dependent claims add additional limitations, for example: (claims 2 and 12) wherein the job search application comprises a job search application directed to identifying jobs for formerly incarcerated individuals, (claims 5 and 15) wherein the filtering of the filtered pool of jobs occurs iteratively after each of the plurality of job-related input is provided by the user, (claims 6 and 16) wherein the receiving job-related input is responsive to displaying at least one request for information (claims 7 and 17) wherein the job-related input comprises a result of at least one test taken by the user, displaying, the at least one test, receiving input from the user within the at least one test, and receiving the result (claims 8 and 18) wherein the displaying is responsive to receiving user input indicating the final pool of jobs is to be displayed; (claims 9 and 19) analyzing the job-related input by correlating the job-related input to at least one job type and utilizing the job type as a job requirement; (claim 10) wherein the job-related input comprises job requirements (claim 21) identifying additional job requirements, entities from a job listing, assigning a label to the entities, and identifying, based upon the label, an entity of the entities as a job requirement; (claim 22) upon receipt of a selection of a job within the filtered pool of jobs, populating entries within a job application corresponding to the job selected based upon the job-related input, but these only serve to further limit the abstract idea.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mental processes and certain methods of organizing human activity, but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – (claim 1) job search software application, graphical user interface fields, language analysis techniques, scraping websites, parsing (independent claims 4 and 14) machine-learning model trained (claim 11) an information handling device, a processor; a memory device, a job search software application, a graphical user interface, language analysis techniques, scraping websites (claim 20) a computer-readable storage device, a processor, a job search software application, a graphical user interface, language analysis techniques, scraping websites (claim 21) mining, language analysis technique. These 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. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing or machine learning, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, the above-mentioned additional elements do not amount to significantly more than the abstract idea, and fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5-12 and 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over Chevalier (US 2015/0127565), hereinafter “Chevalier”, over Okezie et al. (US 2006/0195326 A1), hereinafter “Okezie”.
Regarding Claim 1, Chevalier teaches A method, the method comprising: displaying a graphical user interface of a job search software application, wherein the graphical user interface provides fields for receiving input from a user and displays visual output to the user; (See at least Chevalier, para 00117, Figure 102a-102b, disclosing job listing search results; claim 34, a user interface display data structure including the initial search results);
receiving, at the job search software application, a pool of jobs, wherein the receiving of the pool of jobs comprises accessing, using the job search software application, websites including job listings, scraping the job listings, and determining job requirements for the job listings by identifying context contained within the job listings by analyzing, using language analysis techniques, the job listings, wherein the job search application parses and recognizes entities within the job listings using the language analysis techniques and assigning a label to the entities; (See at least Chevalier, Figure 102a, receive input; Chevalier, Figure 5A; See at least Chevalier Abstract, discussing various jobs; para 0144, a user searches for jobs; Chevalier, para 0408, FIG. 62A is an example of a software engineering job opportunity hosted by an employment opportunity host web site (e.g., Monster.com, Examiner notes). Examiner notes, Monster.com is an internet source which includes a plurality or pool of job listings. Chevalier, para 0409, teaches the job listing is parsed to extract the job listing terms. In the parsing step, a generator module categorizes and tags the information extracted from the job listing. For example, the listing may be parsed to extract one or more of the following: the job title, company (Examiner notes entity, as per Applicant’s own specification, para 0034, recites “company or entity”), job location, job category, experience required, level of education, salary; the data can be extracted from the job listings using natural language processing software, to identify and categorize the relevant information.);
filtering, using the job search software application, jobs within the pool of jobs to remove jobs unavailable … wherein the filtering comprises comparing the context within the job listings to rules identifying context; ... are removed from the pool of jobs to create a filtered pool of jobs; (See at least Chevalier, para 0146, filter these employer jobs to those in which a user may be interested. That is, if a user is a computer programmer and a contact is employed by Microsoft, the user would see all jobs posted by Microsoft. In a scenario in which these jobs are filtered, entry level positions at Microsoft may not be displayed to a senior programmer; Further, Chevalier, para 0687, compares the extracted job listing data to the criteria from the applicant's profile. For example, in the case of an online job listing, contents of the web page (e.g., HTML page) are parsed and the parsed contents of the job listing are compared to criteria from the applicant's profile and/or resume. Examiner notes “filtering” is the process of sorting, refining or restricting data to remove unnecessary or unwanted information. Therefore, jobs that are unwanted or unnecessary are removed when jobs are filtered.);
categorizing, using the job search software application, each of the jobs within the filtered pool of jobs into a job category based upon the requirements of each of the jobs, wherein each of the job categories has defined requirements based upon the jobs contained within a job category, wherein the requirements are defined utilizing at least a secondary information source and wherein the requirements for a given of the job categories applies to at least a majority of the jobs within the given of the job categories, wherein at least one of the requirements identifies types of ... for a given of the job categories, wherein the categorizing comprises matching the requirements of a given of the jobs to the requirements of a job category; (Chevalier, para 0408, FIG. 62A is an example of a software engineering job opportunity hosted by an employment opportunity host web site (e.g., Monster.com). Examiner notes, Monster.com is a secondary information source, as Applicant’s specification, para 0047, recites “secondary information source (e.g., Internet sources, job sources, etc.)”. Chevalier, para 0409, the job listing is parsed to extract the job listing terms …categorizes and tags the information extracted from the job listing. For example, the listing may be parsed to extract one or more of the following: the job title, company, job location, job category, experience required, level of education (Examiner notes location, experience, education are job requirements), … the data can be extracted from the job listings using natural language processing software, to identify and categorize the relevant information; Further, Chevalier, para 0356, job requirements including geographic region and/or location requirements, education requirements); receiving, at the graphical user interface of the job search software application and subsequent to the filtering and categorizing, a plurality of job-related input from a user, wherein the plurality of job-related input comprises input from the user related to job requirements; (See at least Chevalier, Figure 26A, career data input; para 0356, teaches geographic region and/or location requirements, education requirements);
filtering, using the job search software application, the filtered pool of jobs into a final pool of jobs based upon the job-related input by correlating the job-related input to requirements of the job categories and removing jobs contained within a job category that do not satisfy the job-related input; and (See at least Chevalier, para 0146, filter jobs to those in which a user may be interested… In a scenario in which these jobs are filtered, entry level positions at Microsoft may not be displayed to a senior programmer.);
displaying, within the graphical user interface of the job search software application, the final pool of jobs (Chevalier, para 0221, a website user interface or other graphical user interface; Chevalier, para 0146, filter jobs to those in which a user may be interested; para 0148, display jobs a user might be interested in).
Chevalier does not appear to explicitly teach and in the same field of endeavor Okezie teaches for formally incarcerated individuals … related to unavailable jobs... wherein the jobs unavailable for formally incarcerated individuals … convictions not allowed (See at least Okezie, Abstract; private agencies, with employers searching for suitable workers, using a computer implemented system; para 0006, employers would submit the job descriptions, prerequisites, necessary qualifications, and other relevant information to be placed on software or database made available for ex-offenders; para 0040, ex-offender application and information including record of conviction, prison conduct record; para 0019, the ex-offender could “shop” a particular type of employer and employment by searching the information contained in an employment database. The employer would include the necessary qualifications, requirements, skills, salary, and other related information. Examiner notes, it would be obvious to one of ordinary skill in the art that jobs that are unavailable are not shown as available). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chevalier with for formally incarcerated individuals … related to unavailable jobs... wherein the jobs unavailable for formally incarcerated individuals … convictions not allowed as taught by Okezie with the motivation for a system and apparatus for allowing ex-offenders searching for employment to be matched, by prison/government officials and private agencies, with employers searching for suitable workers, using a computer implemented system, to assist ex-offenders by easing their transition back into the community (Okezie, para 0004 and 0015). The Chevalier invention now incorporating the Okezie invention, has all the limitations of claim 1.
Regarding Claim 2, Chevalier, now incorporating Okezie, teaches the method of claim 1.
Chevalier does not appear to explicitly teach and in the same field of endeavor Okezie teaches wherein the job search application comprises a job search application directed to identifying jobs for formerly incarcerated individuals (Okezie, Abstract; A database/software program containing information on ex-offenders, employers willing and able to hire them... Employers use the information contained in the software to decide which individuals best suit their employment situation. Ex-offenders use the software to search for jobs they qualify for.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chevalier with wherein the job search application comprises a job search application directed to identifying jobs for formerly incarcerated individuals as taught by Okezie with the motivation for a system and apparatus for allowing ex-offenders searching for employment to be matched, to assist ex-offenders by easing their transition back into the community (Okezie, para 0004 and 0015).
Regarding Claim 5, Chevalier, now incorporating Okezie, teaches the method of claim 1, wherein the filtering of the filtered pool of jobs occurs iteratively after each of the plurality of job-related input is provided by the user (Chevalier, para 0332 and 0334, teaches filter to extract desired and/or relevant matches; the single-resume job sequence matches to the JS existing chain from, but not including the target state from. The CSE may then search the state model to obtain “goal results” comprising couplets of the last state in the JS existing chain with the target state. A filter process similar to that shown at 3761-3765 may then be applied to the sequence; Further, Chevalier para 0672, teaches a further analysis and/or refinement of the search results, and from the processing, relevant results (i.e., a plurality of job listings corresponding to the input) are identified; Chevalier, para 0673, the method may be iteratively refined.)
Regarding Claim 6, Chevalier, now incorporating Okezie, teaches the method of claim 1, wherein the receiving job-related input is responsive to displaying at least one request for information on the graphical user interface of the job search software application (Chevalier, para 0148, display jobs a user might be interested in; para 0117-0118 and figures 102a-b, discloses a flow diagram for a job listing search; Further, See at least Figure 62A).
Regarding Claim 7, Chevalier, now incorporating Okezie, teaches the method of claim 1, wherein the job-related input comprises a result of at least one test taken by the user, wherein the at least one test is facilitated by the job search software application via the job search software application displaying, within the graphical user interface, the at least one test, receiving input from the user within the at least one test, and receiving the result at the job search software application (Chevalier, Figures 9-11, teaches tests taken by user; Chevalier, para 0221, teaches a website user interface or other graphical user interface; para 0222, test database includes tests of various types which may be administered, along with information indicating how to analyze results of responses.)
Regarding Claim 8, Chevalier, now incorporating Okezie, teaches the method of claim 1, wherein the displaying is responsive to receiving user input indicating the final pool of jobs is to be displayed (Chevalier, para 0146, filter jobs to those in which a user may be interested; Figure 62A, discloses search jobs with display).
Regarding Claim 9, Chevalier, now incorporating Okezie, teaches the method of claim 1, comprising analyzing the job-related input by correlating the job-related input to at least one job type and utilizing the job type as a job requirement (See at least Chevalier, Figure 26A, career data input; Figures 102a-102b and 103A-B, teaches correlation of job data; para 0356, teaches geographic region and/or location requirements, education requirements).
Regarding Claim 10, Chevalier, now incorporating Okezie, teaches the method of claim 1, wherein the job-related input comprises job requirements (See at least Chevalier, para 0356, teaches geographic region and/or location requirements, education requirements; Further, see Figure 75B).
Regarding claims 11 and 20, the claims are obvious variants to claim 1 above, and are therefore rejected on the same premise. Chevalier further teaches the handling device comprising: a processor; a memory device, and a computer-readable storage device that stores executable code (See para 0225-0226, 0605, 0608, describes the computer environment including processor(s), memory, stored instruction codes, e.g., programs; storage into which data may be saved; and processors by which information may be processed; readable storage mediums).
Regarding claim 12, the claim recites analogous limitations to claim 2 above, and is therefore rejected on the same premise.
Regarding claim 15, the claim recites analogous limitations to claim 5 above, and is therefore rejected on the same premise.
Regarding claim 16, the claim recites analogous limitations to claim 6 above, and is therefore rejected on the same premise.
Regarding claim 17, the claim recites analogous limitations to claim 7 above, and is therefore rejected on the same premise.
Regarding claim 18, the claim recites analogous limitations to claim 8 above, and is therefore rejected on the same premise.
Regarding claim 19, the claim recites analogous limitations to claim 9 above, and is therefore rejected on the same premise.
Regarding Claim 21, Chevalier, now incorporating Okezie, teaches the method of claim 1, comprising identifying additional job requirements by mining, using at least one language analysis technique, entities from a job listing, assigning a label to the entities, and identifying, based upon the label, an entity of the entities as a job requirement (Chevalier, para 0409-0410, teaches the data can be extracted from the job listings using natural language processing software to identify and categorize the relevant information… The key terms represent the extracted and categorized portions of the job listings. For example, key terms might include job title, company, location, full-time/part-time, industry, category, experience, career level, education required, salary, a descriptive tag, and a full description.)
Regarding Claim 22, Chevalier, now incorporating Okezie, teaches the method of claim 1, comprising, upon receipt of a selection of a job within the filtered pool of jobs, populating, by the job search software application, entries within a job application corresponding to the job selected based upon the job-related input (Chevalier, para 0667, The profile data, resume and/or cover letter can then be made available to aid in filing job applications for the applicant… a copy of the applicant's resume and cover letter or a symbolic link to its location, and/or a set of profile data that could be used to populate a resume and/or job application.)
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chevalier and Okezie, and over Yerastov et al. (US 2023/0004941 A1), hereinafter “Yerastov”.
Regarding Claim 4, Chevalier, now incorporating Okezie, teaches the method of claim 1.
Yet, Chevalier and Okezie do not appear to explicitly teach and in the same field of endeavor Yerastov teaches wherein the categorizing comprises utilizing a machine-learning model trained using a training dataset comprising annotated job listings (Yerastov, Abstract, Training data is used to define job categories. Further, see at least Yerastov, para 0041-0042, job categories may be as specific as a job title or as general as a professional field; machine learning techniques, including some statistical techniques, are used to automatically identify one or more pertinent job categories for each job description.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chevalier and Okezie with wherein the categorizing comprises utilizing a machine-learning model trained using a training dataset comprising annotated job listings as taught by Yerastov with the benefit to use machine learning and statistical analysis for job-related data (Yerastov, para 0003). The Chevalier and Okezie invention now incorporating the Yerastov invention, has all the limitations of claim 4.
Regarding claim 14, the claim recites analogous limitations to claim 4 above, and is therefore rejected on the same premise.
Response to Arguments
Applicants arguments filed on 01/15/2026 have been fully considered but they are not persuasive.
Regarding 35 U.5.C. § 101 rejections: Examiner has updated the 101 rejections in light of the most recent claim amendments. Applicant’s arguments have been fully considered but are found unpersuasive and Examiner maintains the 101 rejection.
With respect to Applicant’s remarks that the claims do not recite an abstract idea, Examiner respectfully disagrees. With respect to the abstract idea, the claimed invention falls within at least the abstract groupings of certain methods of organizing human activity and mental processes as explained in the above 101 analysis.
With respect to Applicants arguments on the abstract idea and "managing personal behavior or interactions between people”, Examiner respectfully notes the Office Action explains the claimed invention recites business relations, and is therefore one of certain methods of organizing human activity. For example, claim 1 discusses a job search including each of the job categories has defined requirements based upon the jobs contained within a job category, receiving a plurality of job-related input from a user, wherein the plurality of job-related input comprises input from the user related to job requirements; filtering the pool of jobs based upon the job-related input by correlating the job-related input to requirements of the job categories and removing jobs contained within a job category that do not satisfy the job-related input and displaying the filtered pool of jobs. With respect to Applicants arguments on the abstract idea and "mental processes”, Examiner respectfully notes the Office Action explains the claim limitations mimic human thought processes of observation, evaluation, judgement and opinion. For example, claim 1 limitations recite: “receiving a pool of jobs, wherein the receiving of the pool of jobs comprises job listings”, “determining job requirements for the job listings”, “analyzing the job listings”, “filtering jobs within the pool of jobs to remove jobs unavailable for formally incarcerated individuals”, “comparing the context within the job listings to rules identifying context related to unavailable jobs”; “categorizing each of the jobs within the filtered pool of jobs”, “identifies types of convictions not allowed for a given of the job categories”; “receiving a plurality of job-related input from a user”.) Concepts performed in the human mind as mental processes because the steps of receiving, identifying, determining, comparing, categorizing and analyzing data mimic human thought processes of observation, evaluation, judgement and opinion, perhaps with paper and pencil, where data interpretation is perceptible in the human mind. See In re TLI Commc’ns LLCPatentLitig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)).
With respect to integration of the abstract idea into a practical application, the additional elements – (claim 1) job search software application, graphical user interface, language analysis techniques, scraping websites, parsing (independent claims 4 and 14) machine-learning model trained (claim 11) information handling device, processor; memory device, job search software application, graphical user interface, language analysis techniques, scraping websites (claim 20) computer-readable storage device, a processor, job search software application, graphical user interface, language analysis techniques, scraping websites (claim 21) mining, language analysis technique - are additional elements to perform the steps and amount to no more than mere instructions to apply the exception using generic computing components, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f)).
With respect to Applicants remarks (remarks page 15): “Applicant respectfully submits that, based upon the guidance of Updated MPEP, the Office is not to take into account what is well- understood, routine, conventional activity in making an "improvement" determination under Step 2A. Applicant respectfully submits that the Office incorrectly performs this analysis by taking into account what is well-understood, routine, conventional activity.” Examiner asserts the claims have been properly considered under 101. Please see above for complete 101 analysis.
With respect to Applicants remarks (remarks page 16): “Applicant respectfully submits that the claims represent an improvement to the technology. Specifically, the categorization of a pool of jobs before provision to a user within a graphical user interface and utilizing the categorizing to identify requirements of a job category to filter the jobs for a user is an improvement to the technology. Accordingly, Applicant respectfully submits that the claims integrate the judicial exception into a practical application by improving the relevant existing technology”, Examiner respectfully disagrees. Examiner respectfully does not find this assertion persuasive because categorizing jobs to identify requirements of a job category is not an improvement to technology. Further, there is no indication that the claimed invention includes an improvement to the functioning of the computer itself. See MPEP 2106(a) for examples that the courts have indicated may show an improvement in computer-functionality and for examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality. Further, with respect to integration of the abstract idea into a practical application, the computing elements are additional elements to perform the steps and amount to no more than mere instructions to apply the exception using generic computer components. Examiner has reviewed Applicants claims and specification and has found only generic computing elements. Further, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Applicant further argues that the claimed invention contains significantly more (Step 2B). With respect to Applicant remarks (remarks page 19): “since the system is specifically designed for the provision of jobs to formally incarcerated individuals, the system filters jobs that are not available to the individuals. This reduces the amount of computing resources and network communication resources that are required for performance of the claimed limitations. Since the jobs are filtered to those jobs that are available to the individuals, the system reduces the number of communications that occur over the network, thereby reducing network congestion and requiring less network bandwidth. Additionally, due to the decrease in communication, the amount of computing resources needed is also reduced.” Examiner notes these arguments are not persuasive as the general purpose device is being used for the very purpose that such devices are known to be used for, e.g. more efficient, faster, etc. Merely filtering data by using computers is considered to be mere instructions to carry out the abstract idea and mere field of use and technical environment as outlined in MPEP 2106.05. Further, Examiner notes “reducing network congestion and requiring less network bandwidth” is not claimed nor in Applicant’s specification.
Therefore, Examiner maintains the 101 rejection with respect to these and all depending claims unless otherwise indicated.
Regarding 35 U.S.C. § 103 rejections. Examiner has updated the prior art rejections and maintains the prior art rejections. With respect to Applicant’s arguments, Applicants arguments have been considered but are moot as the Examiner has updated the rejections using the Chevalier and Okezie references.
With respect to Applicants remarks (remarks, pages 20-21): “Applicant respectfully submits that the Office has not articulated a reason why a person skilled in the art would combine the prior art references, does not have adequate evidentiary basis for that finding, and has not provided a satisfactory explanation for the motivation finding that includes an express and rational connection with the evidence presented. See In re NuVasive, Inc., 842 F.3d 1376, 1382 (Fed. Cir. 2016). Rather, the Office has merely supplied a conclusory statement claiming that the combination of references would be obvious to one of ordinary skill in the art. Applicant therefore respectfully submits that the Office has not met their burden in rejecting the claims under § 103 and requests that the § 103 be withdrawn.”
Examiner respectfully disagrees. Examiner asserts the claims have been properly considered under 103. The Examiner has provided the Chevalier reference, which establishes throughout a software matching system for job applicants, including filtering for different job categories (See at least Chevalier, Figure 3H, teaches a candidate, a recruiter, a job search; Further See Chevalier, Figures 5A, teaches determining candidates job skills, request for job info, and determining a relevant job to provide to the candidate. Further, see FIG. 62A is an example of a software engineering job opportunity hosted by an employment opportunity host web site (e.g., Monster.com)). The Examiner has also provided the Okezie reference, which establishes an employment matching software program for ex-offenders. (See at least Okezie, Abstract and para 0019). As both references are in the same field of endeavor of matching jobs and candidates, it would be obvious to one of ordinary skill in the art to combine references. Combined, the references establish it is known to use an employment search system for specific job inquiries via filtering, including jobs available for ex-offenders or formerly incarcerated individuals. Therefore, Applicant’s remarks are found unpersuasive.
Applicant further argues (remarks pages 21-23): “Additionally, Applicant respectfully submits that "[t]he rationale to support a conclusion that the claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. "MPEP § 2143 (emphasis added). Applicant respectfully submits that neither the references nor the rejections support such a conclusion. Applicant respectfully submits that the combination of references fails to teach, or even suggest, at least "categorizing, using the job search software application, each of the jobs within the pool of jobs that was filtered into a job category, wherein each of the job categories has defined requirements based upon the jobs contained within a job category, wherein the requirements are defined utilizing at least a secondary information source and wherein the requirements for a given of the job categories applies to at least a majority of the jobs within the given of the job categories, wherein at least one of the requirements identifies types of convictions not allowed for a given of the job categories." Claim 1 (as previously presented).
Applicant's previously presented remarks regarding the references are incorporated by reference herein, as they remain applicable. The Office relies on Cheveliar for teaching a portion of the above-mentioned limitation, specifically "categorizing, using the job search software application, each of the jobs within the pool of jobs that was filtered into a job category, wherein each of the job categories has defined requirements based upon the jobs contained within a job category, wherein the requirements are defined utilizing at least a secondary information source and wherein the requirements for a given of the job categories applies to at least a majority of the jobs within the given of the job categories, wherein at least one of the requirements identifies types ... for a given of the job categories. "Claim 1 (as previously presented). Applicant respectfully disagrees that Cheveliar teaches this portion of this limitation. Cheveliar teaches a system that "facilitates matching of people, companies, organizations, and/or the like that may benefit from being connected using information." Cheveliar at [0140]. Cheveliar teaches the extraction of information from a job listing. See Cheveliar at [0409]. However, this is not the same as the "job categories ha[ving] defined requirements based upon the jobs contained within a job category, wherein the requirements are defined utilizing at least a secondary information source and wherein the requirements for a given of the job categories applies to at least a majority of the jobs within the given of the job categories." Claim 1 (as previously presented).
In other words, Cheveliar teaches that information is extracted from a job listing and this information is categorized, for example, "the job title, company, job location, job category, experience required, level of education, salary, company logo, graphic, etc." Cheveliar at [0409]. This information can then be utilized to generate an ad for the job. See Cheveliar at [0411] - [0412]. Thus, unlike the claimed limitations in which the jobs are categorized based upon requirements for the jobs within the job category, Cheveliar teaches that each job listing is parsed to identify key terms of the job. There is no discussion in Cheveliar of grouping jobs into job categories based upon requirements for the jobs. Thus, Cheveliar does not teach the identified claim limitation.”
Examiner respectfully disagrees.
Again, Examiner notes the reference is Chevalier, not Cheveliar.
With respect to Applicant’s remarks: “Thus, unlike the claimed limitations in which the jobs are categorized based upon requirements for the jobs within the job category, Cheveliar teaches that each job listing is parsed to identify key terms of the job. There is no discussion in Cheveliar of grouping jobs into job categories based upon requirements for the jobs.” Examiner respectfully disagrees and notes Chevalier throughout teaches a software platform for matching candidates and jobs. See at least Chevalier, Figure 5A, teaches determining candidates job skills, request for job info, and determining a relevant job to provide to the candidate. Further, see FIG. 62A is an example of a software engineering job opportunity hosted by an employment opportunity host web site (e.g., Monster.com)). Examiner notes, Monster.com is an internet source which includes a plurality of job listings. Chevalier, para 0409, teaches the job listing is parsed to extract the job listing terms. In the parsing step, a generator module categorizes and tags the information extracted from the job listing. For example, the listing may be parsed to extract one or more of the following: the job title, company, job location, job category, experience required, level of education, salary (Examiner notes requirements for jobs); the data can be extracted from the job listings using natural language processing software, to identify and categorize the relevant information. Therefore Applicants remarks are found unpersuasive.
Applicant further argues (remarks page 23): “Additionally, Chevelair fails to teach, or even suggest, "receiving, at the job search software application, a pool of jobs," Claim 1 (as previously presented), particularly in view of "wherein the receiving of the pool of jobs comprises scraping, using the job search software application, websites including job listings and determining job requirements for the job listings by analyzing, using language analysis techniques, the job listings to identify context contained within the job listings." Claim 1 (as currently amended). Chevelair teaches that a user searches for jobs, for example, on a website, and the website returns a group of jobs, which the Office is utilizing as teaching this previously presented limitation. See Office Action at p. 8. Thus, Cheveliar teaches that jobs are returned in response to a user input and user search criteria. Cheveliar does not teach that "receiving, at the job search software application, a pool of jobs, wherein the receiving of the pool of jobs comprises scraping, using the job search software application, websites including job listings and determining job requirements for the job listings by analyzing, using language analysis techniques, the job listings to identify context contained within the job listings." Claim 1 (as previously presented).”
Examiner respectfully disagrees.
Again, Chevalier throughout teaches a software platform for matching candidates and jobs. See at least Chevalier, Figure 5A, teaches determining candidates job skills, request for job info, and determining a relevant job to provide to the candidate. Further, see FIG. 62A is an example of a software engineering job opportunity hosted by an employment opportunity host web site (e.g., Monster.com)). Examiner notes, Monster.com is an internet source which includes a plurality of job listings. Chevalier, para 0409, teaches the job listing is parsed to extract the job listing terms. In the parsing step, a generator module categorizes and tags the information extracted from the job listing. For example, the listing may be parsed to extract one or more of the following: the job title, company, job location, job category, experience required, level of education, salary (Examiner notes requirements for jobs); the data can be extracted from the job listings using natural language processing software, to identify and categorize the relevant information. Therefore Applicants remarks are found unpersuasive.
Further, Applicant solely argues the Chevalier reference. This is not a 102 rejection and the applied reference Chevalier is not used alone. Examiner has provided the Okezie reference, which also establishes an employment matching software program for candidates and jobs. Okezie, specifically teaches an employment matching software program for ex-offenders (incarcerated individuals). (See at least Okezie, Abstract and para 0019).
Therefore, Applicants remarks are found unpersuasive and Examiner has updated and maintains the 103 rejections for all claims.
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.
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/R.R.N./ Examiner, Art Unit 3629
/NATHAN C UBER/ Supervisory Patent Examiner, Art Unit 3626