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 .
This office action is in response to submission of application on 3/29/2024.
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1-13 are directed to a method (i.e., a process), and claims 14-20 are directed to a processing system (i.e., a machine/apparatus); therefore, all pending claims are directed to one of the four categories of invention.
Step 2A: Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 recites limitations of:
generating a prompt including the at least one job title – mental process (observation, evaluation, judgement, opinion) as a human mind can generate a prompt.
applying the template selection to the resume content to generate the sample resume – mental process (observation, evaluation, judgement, opinion) as a human mind can apply a template selection to resume content to generate a sample resume.
Mental processes are abstract ideas.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements of:
A method of dynamically creating a sample resume at a user workstation – components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
receiving, at a first interactive element of a user interface, at least one job title – inputting data is insignificant, extra-solution activity. See MPEP 210.05(g).
receiving, at a second interactive element of the user interface, a template selection from a list of available templates, each template in the list of available templates represents a graphical design of an output for the sample resume - inputting data is insignificant, extra-solution activity. See MPEP 210.05(g).
providing the prompt to an artificial intelligence (AI) model instructing the AI model to generate content related to the at least one job title included in the prompt - outputting data is insignificant, extra-solution activity. See MPEP 2106.05(g).
receiving resume content generated by the AI model based on the prompt -inputting data is insignificant, extra-solution activity. See MPEP 210.05(g).
displaying the sample resume in a third interactive element of the user interface - outputting data is insignificant, extra-solution activity. See MPEP 2106.05(g).
The additional elements do not integrate the abstract idea into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
A method of dynamically creating a sample resume at a user workstation – components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
receiving, at a first interactive element of a user interface, at least one job title – inputting data is insignificant, extra-solution activity. See MPEP 210.05(g). Transmitting data is well-understood, routine, and conventional. See MPEP 2106.05(d)(II)(i).
receiving, at a second interactive element of the user interface, a template selection from a list of available templates, each template in the list of available templates represents a graphical design of an output for the sample resume - inputting data is insignificant, extra-solution activity. See MPEP 210.05(g). Transmitting data is well-understood, routine, and conventional. See MPEP 2106.05(d)(II)(i).
providing the prompt to an artificial intelligence (AI) model instructing the AI model to generate content related to the at least one job title included in the prompt - outputting data is insignificant, extra-solution activity. See MPEP 2106.05(g). Transmitting data is well-understood, routine, and conventional. See MPEP 2106.05(d)(II)(i).
receiving resume content generated by the AI model based on the prompt -inputting data is insignificant, extra-solution activity. See MPEP 210.05(g). Transmitting data is well-understood, routine, and conventional. See MPEP 2106.05(d)(II)(i).
displaying the sample resume in a third interactive element of the user interface - outputting data is insignificant, extra-solution activity. See MPEP 2106.05(g). Transmitting data is well-understood, routine, and conventional. See MPEP 2106.05(d)(II)(i).
The additional elements do not integrate the abstract idea into a practical application.
Nor do the additional elements do not amount to significantly more. Therefore, claim 1 is not patent eligible.
Independent claim 7 recites the same limitations as claim 1 and a similar analysis applies. Claim 7 recites the additional limitation of “resume creation system”. Components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
Independent 14 recites the same limitations as claim 1 and a similar analysis applies. Claim 14 recites the additional elements of “A processing system configured to dynamically create a sample resume, comprising: one or more computer-readable storage devices storing computer-readable instructions; one or more processors” – computer components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2). And “provide, to an external computing system, a user interface configured to accept user inputs and transmit the user inputs to the processing system” – outputting data is insignificant, extra-solution activity. See MPEP 2106.05(g). Transmitting data is well-understood, routine, and conventional. See MPEP 2106.05(d)(II)(i).
The additional elements do not integrate the abstract idea into a practical application, nor do they amount to significantly more. Therefore, the independent claims are not patent eligible.
A similar analysis applies to the dependent claims.
Claims 2, 8, and 15 recite the additional elements of “receive, by way of the interactive element, edits to the resume content of the sample resume” – inputting data is insignificant, extra-solution activity. See MPEP 2106.05(g). Transmitting data is well-understood, routine, and conventional. See MPEP 2106.05(d)(II)(i).
Claims 3, 9, and 16 recite the additional elements of “including instructions directing the AI model to structure the content in a resume format including an applicant name, an applicant address, educational history, and work history” – mental process (observation, evaluation, judgement, opinion) as a human mind can direct an AI model to structure the content in a resume format including an applicant name, an applicant address, educational history, and work history.
Claims 4, 10, and 17 recite the additional elements of “scrub the resume content to remove personal data present in the resume content generated by the AI model prior to displaying the sample resume” – mental process (observation, evaluation, judgement, opinion) as a human mind can remove personal data found in resume content.
Claims 5, 11, 18, and 19 recite the additional elements of “replacing personal information in the resume content with generic personal information” – mental process (observation, evaluation, judgement, opinion) as a human mind can replace personal information in a resume with generic personal information.
Claim 6 recites the additional elements of “using the sample resume for at least one of search engine optimization (SEO) and search engine marketing (SEM) campaigns to drive web traffic to a resume builder website” – inputting a resume to a search engine is merely outputting data which is insignificant, extra-solution activity. See MPEP 2106.05(g). Transmitting data is well-understood, routine and conventional. See MPEP 2106.05(d)(II)(i).
Claims 12, and 20 recite the additional elements of “registering the resume content with a resume database; and assigning a document ID to the resume content” – mental process (observation, evaluation, judgement, opinion) as a human mind can register resume content with a resume database, and assign a document ID to the resume content.
Claim 13 recites the additional elements of “the at least one job title and the template selection are provided in a text file containing a plurality of job titles, each associated with a years of experience value and a resume template name, the text file being structured as a comma-separated-value (CSV) file” – selecting a particular data source or type of data to be manipulated is insignificant extra solution activity. See MPEP 2106.05(g)(3). Providing, transmitting data is well-understood, routine and conventional. See MPEP 2106.05(d)(II)(i).
The additional elements do not integrate the abstract idea into a practical application. Nor do the additional elements amount to significantly more. Therefore the claims are not patent eligible.
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.
Claims 1-3, 7-9, and 14-16 are rejected under 35 U.S.C. § 103 as being unpatentable over Sunico, et al (Resume Building Application based on LLM (Large Language Model), herein Sunico), Balaji, et al (AIResume: Automated generation of Resume Work History, herein Balaji), and Tyagi, et al (Resume Builder Application, herein Tyagi).
Regarding claim 1,
Sunico i teaches A method of dynamically creating a sample resume at a user workstation (Sunico, abstract, line 4 “To mitigate this, we introduce a novel resume building application that employs the Large Language Model (LLM) to aid students in composing their first resumes. The application comprises three modules: Resume Generation, Resume Assessment, and User I/O. The Resume Generation module utilizes prompt engineering to produce resume bullet points, while the Resume Assessment module evaluates these bullet
points for potential enhancements. The User I/O module simplifies user interaction by accepting free-style plain English as input and displaying the generated bullet points as suggestions.” In other words, resume building application is a method of dynamically creating a sample resume, and user I/O is at user workstation.) , comprising:
receiving, at a first interactive element of a user interface, [at least one job title] (Sunico, Fig. 1,
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In other words, User I/O is receiving at a first interactive element of a user interface.) ;
[receiving, at a second interactive element of the user interface, a template selection from a list of available templates, each template in the list of available templates represents a graphical design of an output for the sample resume];
generating a prompt (Sunico, Table II.
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In other words, issue a prompt is generating a prompt.) including
[the at least one job title];
providing the prompt to an artificial intelligence (AI) model instructing the AI model to generate content related to [the at least one job title] included in the prompt (Sunico, Tables I, and II.
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In other words, Issue a prompt to LLM which incorporates user’s input and the requirements shown TABLE 1. is providing the prompt to an artificial intelligence model instructing the AI model to generate content. Examiner notes the at least one job title is mapped to Balaji.);
receiving resume content generated by the AI model based on the prompt (Sunico, Fig. 1, In other words, LLM is AI model, and resume assessment module receives the resume content generated by the AI model based on the prompt.) ;
[applying the template selection to the résumé content to generate the sample resume]; and
displaying the sample resume in a third interactive element of the user interface (Sunico, Table II, In other words, present the resume bullet to the users is display the sample resume at the user interface.) .
Thus far, Sunico does not explicitly teach receiving at a user interface… at least one job title.
Balaji teaches receiving at a user interface… at least one job title (Balaji, Figure 2,
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In other words, select title is receiving at a user interface… at least one job title.)
Both Sunico and Balaji are directed to automatic resume generation, among other things. Sunico teaches a method of dynamically creating a sample resume at a user workstation, comprising receiving, at a first interactive element of a user interface,
In view of the teaching of Sunico, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Balaji into the teaching of Sunico. This would result in a method of dynamically creating a sample resume at a user workstation, comprising receiving, at a first interactive element of a user interface, at least one job title; generating a prompt including the at least one job title; providing the prompt to an artificial intelligence (AI) model instructing the AI model to generate content related to the at least one job title included in the prompt; receiving resume content generated by the AI model based on the prompt; applying the template selection to the résumé content to generate the sample resume; and displaying the sample resume in a third interactive element of the user interface.
One of ordinary skill in the art would be motivated to do this in order to help applicants summarize their work history when producing a resume to improve their success rate in getting hired. (Balaji, abstract, line 1 Automatic text generation has redefined the act of content generation in multiple fields such as article/news summarization, chatbots, and virtual assistants. For a person on the job market, a resume is an important piece of document that determines his rate of success in landing a job. In this paper, we introduce AIResume (AIR) - a
tool that utilizes a vast knowledge base comprising of resume entries and job descriptions to generate the work history portion of a person’s resume with minimal input from the user. The system starts with suggesting personalized job titles based on the employer and then goes on to mine and present the user with relevant work activities for the selected employer and job title.”)
Examiner notes that both Sunico and Balaji teach user interfaces. It would be obvious to one of ordinary skill in the art that user interfaces have multiple interactive elements. Nevertheless, for clarity, Examiner combines Tyagi to teach multiple interactive elements.
Thus far, the combination of Sunico and Balaji does not explicitly teach receiving, at a second interactive element of the user interface, a template selection from a list of available templates, each template in the list of available templates represents a graphical design of an output for the sample resume.
Tyagi teaches multiple interactive elements, and receiving, at a second interactive element of the user interface, a template selection from a list of available templates, each template in the list of available templates represents a graphical design of an output for the sample resume (Tyagi, Figs. 1-3, and page 15, subparagraph 3) “This app allows and suggests users to choose from several creative and up-to-date templates to make their resume more eye-Catching to HR.”
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In other words, Figs. 1-3 are a first, second, and third interactive element, and this app allows and suggests users to choose from several creative and up-to-date templates is receiving a template selection from a list of available templates, each template in the list of available templates represents a graphical design of an output for the sample resume.).
Both Tyagi and the combination of Sunico and Balaji are directed to automatic resume generation, among other things. The combination of Sunico and Balaji teach a method of dynamically creating a sample resume at a user workstation, comprising: receiving, at a first interactive element of a user interface, at least one job title;
In view of the teaching of the combination of Sunico and Balaji, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Tyagi into the combination of Sunico and Balaji. This would result in a method of dynamically creating a sample resume at a user workstation, comprising: receiving, at a first interactive element of a user interface, at least one job title; receiving, at a second interactive element of the user interface, a template selection from a list of available templates, each template in the list of available templates represents a graphical design of an output for the sample resume; generating a prompt including the at least one job title; providing the prompt to an artificial intelligence (AI) model instructing the AI model to generate content related to the at least one job title included in the prompt; receiving resume content generated by the AI model based on the prompt; applying the template selection to the résumé content to generate the sample resume; and displaying the sample resume in a third interactive element of the user interface.
One of ordinary skill in the art would be motivated to do this to use predefined templates in order to create better resumes to help applicants get hired. (Tyagi, abstract, line 1 “Nowadays there is a tough competition for getting a job and one of the biggest trials for many job-seekers is creating the perfect resume. The resume is the very first thing that a potential employer encounters regarding an applicant and is used to screen applicants, often followed by an interview. This research paper aims to specify a method for creating resumes in a very simple and user friendly way. We are proposing an application that will help applicants in creating resumes by simply taking their information as input.”)
Regarding claim 2,
The combination of Sunico, Balaji, and Tyagi teaches the method of Claim 1, further comprising
receiving, by way of the third interactive element, edits to the resume content of the sample resume (Sunico, Table II. In other words, if improvement is needed, signal Resume Generation module to reissue the prompt is receiving edits to the resume content of the sample resume.).
Regarding claim 3,
The combination of Sunico, Balaji, and Tyagi teaches the method of Claim 1, wherein generating the prompt further comprises
including instructions directing the AI model to structure the content in a resume format including an applicant name, an applicant address, educational history, and work history (Sunico, Tables I and II. In other words, issue a prompt to LLM which incorporates user’s input and the requirements shown in Table I is including instructions directing the AI model to structure the content in a resume format including an applicant name, an applicant address, educational history, and work history.) .
Claim 7 is a method claim that corresponds to method claim 1. Otherwise, it is not patentably distinct. Claim 7 has the additional limitation of “resume creation system”. The combination of Sunico, Balaji, and Tyagi teaches a “resume creation system” (Sunico, abstract, line 4 “To mitigate this, we introduce a novel resume building application that employs the Large Language Model(LLM) to aid students in composing their first resumes.” In other words, resume building application is resume creation system.). Therefore, claim 7 is rejected for the same reasons as claim 1.
Claims 8-9 are method claims that depend from claim 7 that correspond to claims 2-3 that depend from claim 1, respectively. Otherwise, they are not patentably distinct. Therefore, claims 8-9 are rejected for the same reasons as claims 2-3, respectively.
Claim 14 is a processing system configured to dynamically create a sample resume claim, that corresponds to method claim 1. Otherwise, they are not patentably distinct. The combination of Sunico, Balaji, and Tyagi teaches a processing system (Sunico, Fig. 1, In other words, the system architecture comprising a user I/O and a Large Language Model requires a processing system with one or more computer-readable storage devices storing instructions and one or more processors in order to execute.) Therefore, claim 14 is rejected for the same reasons as claim 1.
Claims 15-16 are processing system claims that correspond to method claims 2-3, respectively. Otherwise, they are not patentably distinct. Therefore, claims 15-16 are rejected for the same reasons as claims 2-3, respectively.
Claims 4-5, 10-11, and 17-19 are rejected under 35 U.S.C. § 103 as being unpatentable over Sunico, Balaji, and Tyagi, further in view of Aberdeen, et al (The MITRE Identification Scrubber Toolkit: Design, Training, and assessment, herein Aberdeen).
Regarding claim 4,
The combination of Sunico, Balaji, and Tyagi teaches the method of Claim 3, further comprising
Thus far, the combination of Sunico, Balaji, and Tyagi does not explicitly teach scrubbing the resume content to remove personal data present in the résumé content generated by the AI model prior to displaying the sample resume.
Aberdeen teaches scrubbing the resume content to remove personal data present in the résumé content generated by the AI model prior to displaying the sample resume (Aberdeen, abstract, line 1 “Medical records must often be stripped of patient identifiers, or de-identified, before being shared. De-identification by humans is time-consuming, and existing software is limited in its generality. The open source MITRE Identification Scrubber Toolkit (MIST) provides an environment to support rapid tailoring of automated de-identification to different document types, using automatically learned classifiers to de-identify and protect sensitive information.” And, page 853, column 1, paragraph 1, subparagraph 4, line 1 “A redaction and resynthesis module, which is applied to replace phrases containing PHI with synthetic identifiers to facilitate sharing of records in both human readable and MIST accessible forms.” In other words, PHI is personal data, and redact is scrubbing the content to remove personal data present in the content prior to displaying. Examiner notes that redacting personal information from a document of a medical record is not patentably distinct from redacting personal information from a resume.)
Both Aberdeen and the combination of Sunico, Balaji, and Tyagi are directed to document processing with personal data information, among other things. The combination of Sunico, Balaji, and Tyagi teaches the method of claim 3, but does not explicitly teach scrubbing the resume content to remove personal data present in the résumé content generated by the AI model prior to displaying the sample resume. Aberdeen teaches scrubbing the resume content to remove personal data present in the résumé content generated by the AI model prior to displaying the sample resume.
In view of the teaching of the combination of Sunico, Balaji, and Tyagi, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Aberdeen into the combination of Sunico, Balaji, and Tyagi. This would result in the method of claim 3, and scrubbing the resume content to remove personal data present in the résumé content generated by the AI model prior to displaying the sample resume.
One of ordinary skill in the art would be motivated to do this in order to protect personal information and preserve the privacy of the user. (Aberdeen, abstract, line 1 “Medical records must often be stripped of patient identifiers, or de-identified, before being shared. De-identification by humans is time-consuming, and existing software is limited in its generality. The open source MITRE Identification Scrubber Toolkit (MIST) provides an environment to support rapid tailoring of automated de-identification to different document types, using automatically learned classifiers to de-identify and protect sensitive information.”)
Regarding claim 5,
The combination of Sunico, Balaji, Tyagi and Aberdeen teaches the method of Claim 4, further comprising
replacing the personal data in the resume content with generic personal information (Aberdeen, see mapping of claim 4. In other words, replace with synthetic identifiers is replacing the personal data in the content with generic personal information.).
Claims 10-11 are method claims that correspond to method claims 4-5, respectively. Otherwise, they are not patentably distinct. Therefore, claims 10-11 are rejected for the same reasons as claims 4-5, respectively.
Claims 17-18 are processing system claims that correspond to method claims 4-5, respectively. Otherwise, they are not patentably distinct. Therefore, claims 17-18 are rejected for the same reasons as claims 4-5, respectively.
Claim 19 is a system claim that depends from claim 16 that corresponds to claim 18 that depends from claim 17. Otherwise, they are not patentably distinct. Therefore, claim 19 is rejected for the same reasons as claim 18 and 16.
Claim 6 is rejected under 35 U.S.C. § 103 as being unpatentable over Sunico, Balaji, and Tyagi, further in view of Swan, et al (The Demand for Teacher Characteristics in the Market Child Care: Evidence from a Field Experiment, herein Swan).
Regarding claim 6,
The combination of Sunico, Balaji and Tyagi teaches the method of Claim 1, further comprising
Thus far, the combination of Sunico, Balaji, and Tyagi does not explicitly teach using the sample resume for at least one of search engine optimization (SEO) and search engine marketing (SEM) campaigns to drive web traffic to a resume builder website.
Swan teaches using the sample resume for at least one of search engine optimization (SEO) and search engine marketing (SEM) campaigns to drive web traffic to a resume builder website (Swan, page 10, paragraph 5, line 3 “Once the city-specific resumes were generated using the Resume Randomizer software, they were cleaned using a macro to ensure resumes were no longer than one page, converted to PDFs, and uploaded to the cloud-based file sharing service within each Research Assistant’s unique folder. The Research Assistant-specific folders contained city-specific folders (for each of the cities they were assigned) which in turn contained monthly folders (e.g., May, June, and July).” In other words, resume…was cleaned is sample resume, and submitted to file sharing service is using the sample resume for at least one of search engine optimization and search engine marketing campaigns. Examiner notes that the limitation of “to drive web traffic to a resume builder website” is a limitation directed to a purpose/intent and does not further describe the claimed invention.)
Both Swan and the combination of Sunico, Balaji, and Tyagi are directed to resume generation, among other things. The combination of Sunico, Balaji, and Tyagi teaches the method of claim 1, but does not explicitly teach using the sample resume for at least one of search engine optimization (SEO) and search engine marketing (SEM) campaigns to drive web traffic to a resume builder website. Swan teaches using the sample resume for at least one of search engine optimization (SEO) and search engine marketing (SEM) campaigns to drive web traffic to a resume builder website.
In view of the combination of Sunico, Balaji, and Tyagi, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Swan into the combination of Sunico, Balaji, and Tyagi. This would result in the method of claim 1, and using the sample resume for at least one of search engine optimization (SEO) and search engine marketing (SEM) campaigns to drive web traffic to a resume builder website.
One of ordinary skill would be motivated to do this in order to help identify relevant positions for job seekers. (Swan, page 10, paragraph 4, line 1 “A Data Collection Protocol was developed for the Research Assistants to serve as a step-by-step guide for identifying relevant childcare teacher positions and submitting resumes in response to those positions. In total, the Data Collection Protocol summarized three main steps in the data collection phase: (i) generate a large bank of resumes whose attributes were randomly assigned, (ii) submit resumes in response to on-line childcare teacher job postings, and (iii) record whether each submitted resume was invited for an interview.)
Claims 12 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Sunico, Balaji, and Tyagi, and further in view of Swan.
Regarding claim 12,
The combination of Sunico, Balaji, and Tyagi teaches the method of Claim 7, further comprising:
Thus far, the combination of Sunico, Balaji, and Tyagi does not explicitly teach
registering the resume content with a resume database; and assigning a document ID to the resume content.
Swan teaches registering the resume content with a resume database; and assigning a document ID to the resume content (Swan, page 10, paragraph 5, line 3 “Once the city-specific resumes were generated using the Resume Randomizer software, they were cleaned using a macro to ensure resumes were no longer than one page, converted to PDFs, and uploaded to the cloud-based file sharing service within each Research Assistant’s unique folder. The Research Assistant-specific folders contained city-specific folders (for each of the cities they were assigned) which in turn contained monthly folders (e.g., May, June, and July).” And, page 11, paragraph 1, line 5 “The naming convention for the resumes contained the study city name, the batch number (which also represented the job number being applied for in that city- month), and the resume number (the specific resume in the batch of four). For example, the filename for the second resume in a batch to be sent to the tenth job located in Dallas would be “Dallas_10_2of4.pdf”. Together, this naming convention created a unique identifier, which was used along with the month-of-study variable to create a unique study identification number.” In other words, uploaded to the cloud-based file sharing service is registering resume content with a resume database, and naming convention created a unique identifier is assigning a document ID to the resume content.)
It would be obvious to combine Swan into the combination of Sunico, Balaji, and Tyagi at least for the same reasons used to combine Swan into the combination of Sunico, Balaji, and Tyagi used in the mapping of claim 6.
Claim 20 is a processing system claim that depends from claim 14 that corresponds to method claim 12 that depends from claim 7. Otherwise, they are not patentably distinct. Therefore claim 20 is rejected for the same reasons as claims 14 and 12.
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over Sunico, Balaji, and Tyagi, and further in view Kale, et al (Job Tailored Resume Content Generation, herein Kale).
The combination of Sunico, Balaji, and Tyagi teaches the method of Claim 7, wherein the at least one job title and the template selection are
Thus far, the combination of Sunico, Balaji, and Tyagi, does not explicitly teach provided in a text file containing a plurality of job titles, each associated with a years of experience value and a resume template name, the text file being structured as a comma-separated-value (CSV) file.
Kale teaches provided in a text file containing a plurality of job titles, each associated with a years of experience value and a resume template name, the text file being structured as a comma-separated-value (CSV) file (Kale, page 46, column 1, paragraph 4, line 1 “Most employers have common template for their job description, hence custom models could be trained for specific / popular employers. E.g: Training separate models for jobs at Amazon, Google, Apple, Meta etc. Newer GPT variants like GPT-3 and GPT-4 can be fine-tuned to solve the problem.” And, page 42, column 2, paragraph 3, line 1 “In a similar way to resume bullet points, job descriptions were also cleansed, by removing any garbage characters, html tags, punctuations, non ASCII characters. JD were stored into a .csv file along with their JobID. Below is an example of a sample job scraped and its cleansed version:
Title : Software Engineer Google
Scraped Job:
<div>
<h3 class="jobSectionHeader">
<b>Minimum\quad qualifications:</b></h3>
<ul>
<li>Bachelor’s\quad degree\quad or\quad
equivalent\quad practical\quad experience.
</li>
<li>5\quad years\quad of\quad experience
\quad with\quad software\quad development
\quad in\quad one\quad or\quad more\quad
programming\quad languages,\quad and\quad
with\quad data\quad structures/algorithms.
</li>
<li>3\quad years\quad of\quad experience
\quad testing, \quad maintaining,\quad or
launching\quad software\quad products,\quad
and\quad 1\quad year\quad of\quad experience
\quad with\quad software\quad design\quad
and\quad architecture.\quad
</li>
<li>3\quad years\quad of\quad experience
\quad working\quad with\quad embedded
\quad operating\quad systems.</li>....”
In other words, job title is job title, template is template, JobID is resume template name, and JD stored into a .csv file along with their JobID is the text file being structured as a comma-separated-value (CSV) file.)
Both Kale and the combination of Sunico, Balaji, and Tyagi are directed to resume content generation, among other things. The combination of Sunico, Balaji, and Tyagi teaches the method of claim 7, but does not explicitly teach provided in a text file containing a plurality of job titles, each associated with a years of experience value and a resume template name, the text file being structured as a comma-separated-value (CSV) file. Kale teaches provided in a text file containing a plurality of job titles, each associated with a years of experience value and a resume template name, the text file being structured as a comma-separated-value (CSV) file.
In view of the teaching of the combination of Sunico, Balaji, and Tyagi, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Kale into the combination of Sunico, Balaji, and Tyagi. This would result in the method of claim 7, and providing in a text file containing a plurality of job titles, each associated with a years of experience value and a resume template name, the text file being structured as a comma-separated-value (CSV) file.
One of ordinary skill in the art would be motivated to do this to help improve a candidate’s chances of getting hired by tailoring their resume to specific jobs. (Kale, abstract, line 1 “Generally candidates apply to multiple jobs with a single resume and do not tend to customize their resume to match the job description. This hampers their chances of getting a resume shortlisted for the job. The project aims to help such candidates build job tailored resumes that help them create a customized and targeted resume for a specific job. The
tool specifically targets candidates’ employment work history for resume content generation.”)
The prior art made of record and not used is considered pertinent to applicant’s disclosure:
Guo, et al “ResuMatcher: A personalized resume-jo0b matching system” discloses a resume matching system, that intelligently extracts the qualifications and experience of a job seeker directly from his/her résumé, and relevant information about the qualifications and experience requirements of job postings.
Sethre, et al, US20200394615 A1 “Artificial Intelligence Assisted Hybrid Enterprise/Candidate Employment Assistance Platform” discloses a platform that generates a resume for a candidate based upon information from a user profile. The platform can eliminate bias when managing such resume by indexing the personal attributes of all user profiles to identify hidden variables, where the hidden variables are data that has been indexed as receiving biased treatment, editing the resume to remove the hidden variables, and returning the resume to the candidate.
Skondras, et al “Generating Synthetic Resume Data with Large Language Models for Enhanced Job Description Classification” discloses using the OpenAI API to generate both structured and unstructured resumes tailored to specific criteria. These synthetically generated resumes were cleaned, preprocessed and then utilized to train two distinct models: a transformer model (BERT) and a feedforward neural network (FFNN) that incorporated Universal Sentence Encoder 4 (USE4) embeddings.
Conclusion
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/Bart I Rylander/Examiner, Art Unit 2124
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