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 Non-Final office action in response to RCE filed on 04/21/2026. Claims 1, 2, 3, 8, 9, 10, 15, 16 and 17 have been amended. Claims 4, 11 and 18 have been canceled. Claim 21 is newly added. Therefore, claims 1-3, 5-10, 12-17 and 19-21 are currently pending and have been addressed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/21/2026 has been entered.
Response to Amendment
Applicant has amended claims 1, 8 and 15 by removing the language “vector embeddings” to overcome the previous 112(a) rejections. Therefore, the previous 112(a) rejections for “vector embeddings” is withdrawn. However, newly amended claims have a new 112(a) rejection. See below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 5-10, 12-17 and 19-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Newly amended Independent claims 1, 8 and 15 recite: “a vector representation of each resume of the set of resumes toa vector representation of the one or more mock candidates, thereby generating a vector similarity score corresponding to each resume of the set of resumes”. Applicant’s specification does not include “vector representation”, let alone “vector representation of each resume” and “vector representation of the one or more mock candidates”. Examiner notes, while Applicant’s specification, para 0032 includes “vector similarity score”, this is not the same as “vector representation”.
Examiner note: A ‘vector representation’ is a long list of numbers that translates raw data—like a word, sentence, or image—into coordinates within a multi-dimensional space. A ‘vector similarity score’ is a mathematical comparison between two objects. These are not the same. Further, the term ‘vector’ is merely mentioned once in Applicant’s specification with no details or specifics. The ‘written description’ requirement implements the principle that a patent must describe the technology that is sought to be patented; the requirement serves both to satisfy the inventor’s obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee was in possession of the invention that is claimed." Capon v. Eshhar, 418 F.3d 1349, 1357, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). Further, the written description requirement promotes the progress of the useful arts by ensuring that patentees adequately describe their inventions in their patent specifications in exchange for the right to exclude others from practicing the invention for the duration of the patent’s term.
To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See, e.g., Moba, B.V. v. Diamond Automation, Inc., 325 F.3d 1306, 1319, 66 USPQ2d 1429, 1438 (Fed. Cir. 2003); Vas-Cath, Inc. v. Mahurkar, 935 F.2d at 1563, 19 USPQ2d at 1116. However, a showing of possession alone does not cure the lack of a written description. Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 969-70, 63 USPQ2d 1609, 1617 (Fed. Cir. 2002). An applicant shows possession of the claimed invention by describing the claimed invention with all of its limitations using such descriptive means as words, structures, figures, diagrams, and formulas that fully set forth the claimed invention. Lockwood v. Amer. Airlines, Inc., 107 F.3d 1565, 1572, 41 USPQ2d 1961, 1966 (Fed. Cir. 1997). The claimed invention as a whole may not be adequately described if the claims require an essential or critical feature which is not adequately described in the specification and which is not conventional in the art or known to one of ordinary skill in the art (MPEP 2163 | (A)). Dependent claims inherit the deficiencies of the parent claims and thus dependent claims are rejected on the same basis as indicated above for the respective parent claims.
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 1-3, 5-10, 12-17 and 19-21 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 1-3, 5-7 are directed to a method (i.e. a process). Claims 8-10 and 12-14 are directed to a system (i.e. a machine). Claims 15-17 and 19-21 are directed to a non-transitory computer-readable medium (i.e. an article of manufacture). Thus, each of these claims fall 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 1-3, 5-10, 12-17 and 19-21 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1, analogous to independent claims 8 and 15 recite: A method comprising: identifying a job description comprising qualifications for an individual who would be suitable for filling a job position corresponding to the job description; generating a formatted prompt by formatting data of a job description and data of a set of diversity criteria using a prompt template, wherein the prompt template is optimized as a result of a plurality of trials adjusting the prompt template;generating one or more mock candidates that satisfy at least a subset of the qualifications, wherein the mock candidates are generated based on the formatted prompt and is a publicly available; identifying a set of resumes corresponding to candidates for the job position; comparing vector representation of each resume of the set of resumes toa vector representation of the one or more mock candidates, thereby generating a vector similarity score corresponding to each resume of the set of resumes; ranking the set of resumes based on their correspondingvector similarity score; selecting a subset of the resumes with a highest similarity score, corresponding to candidates to interview for the job position, based on the ranking of the set of resumes. The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under at least the abstract groupings of:
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 independent claims discuss identifying a job description comprising qualifications for an individual who would be suitable for filling a job; comparing resume and candidate, ranking resumes and selecting a subset of the resumes with a highest score corresponding to candidates to interview for the job position, which is a clear business relations and one of certain methods of organizing human activity.
Mental Processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion (independent claim 1, analogous to independent claims 8 and 15, recites for example, “identifying a job description comprising qualifications for an individual who would be suitable for filling a job”; “generating a formatted prompt by formatting data of a job description and data of a set of diversity criteria”; “generating one or more mock candidates that satisfy at least a subset of the qualifications”; “identifying a set of resumes corresponding to candidates for the job position”; “comparing vector representation of each resume of the set of resumes to a vector representation of the one or more mock candidates”; “ranking the set of resumes based on their corresponding vector similarity score”; “selecting a subset of the resumes with a highest similarity score”; “candidates to interview for the job position, based on the ranking of the set of resumes.”) Concepts performed in the human mind as mental processes because the steps of obtaining, identifying, generating, comparing, ranking, selecting, transmitting 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)).
Mathematical concepts (mathematical relationships, mathematical formulas or equations and mathematical calculations (as independent claim 1, analogous to independent claims 8 and 15 recites: : “comparing vector representation of each resume of the set of resumes tovector representation of the one or more mock candidates, thereby generating a vector similarity score corresponding to each resume of the set of resumes; ranking the set of resumes based on their correspondingvector similarity score; selecting a subset of the resumes with a highest similarity score.” Dependent claims add additional limitations, for example: (claims 2, 9 and 16) wherein the generating the one or more mock candidates comprises: identifying a set of diversity criteria for the one more candidates; (claims 3, 10 and 17) comparing the vector representation of each resume comprises: identifying a set of diversity criteria for the one more candidates, wherein the vector similarity score of a first resume satisfying the diversity criteria is weighted more heavily than a second resume that does not satisfy the diversity criteria; (claims 6, 13 and 20) identifying availability for a hiring manager based on a calendar of the hiring manager; identifying contact information for the candidates corresponding to the subset of resumes with the highest vector similarity score; and sending electronic communications scheduling interviews between the candidates corresponding to the subset of resumes with the highest vector similarity score and the hiring manager (claims 7, 14 and 21) wherein the identifying the job description comprises: identifying a new job opening posted; and retrieving the job description, 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 certain methods of organizing human activity, mathematical concepts and mental processes, but for the recitation of generic computing 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) computer(s); language model (LM); pre-trained large language model (LLM), processor(s) (claims 5, 12 and 19) artificial intelligence or machine learning (claims 7 and 14) crawling a website (claim 8) memory, processor(s) (claim 15) a non-transitory computer-readable medium. 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 artificial intelligence 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, claims 1-3, 5-10, 12-17 and 19-21 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations 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 non-obviousness.
Claims 1-3, 5-10, 12-17 and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over O’Malley (US 2018/0157995A1), hereinafter “O’Malley”, over Ghosh et al. (US 2021/0150485 A1), hereinafter “Ghosh”, over Michaels (US 2020/0193382 A1), hereinafter “Michaels”.
Regarding Claim 1, O’Malley teaches A method comprising: identifying a job description comprising qualifications for an individual who would be suitable for filling a job position corresponding to the job description; (Job Descriptions and job positions are taught throughout O’Malley, See at least para 0265, teaching job descriptions and para 0051 and Figure 17, teaches an example of potential Job Positions within a particular Job Category); ... data of a job description and data of a set of diversity criteria ... (Job Descriptions and job positions are taught throughout O’Malley, See at least para 0265, teaching job descriptions; O’Malley, para 0053, FIG. 20 depicts an example of Education levels completed that could represent the entire set of criteria for a particular job position (Examiner notes, this is identical to Applicant’s own specification, para 0020, recites: “The diversity criteria may include any information that may aid in generating the mock candidates. In some embodiments, diversity criteria may specify… amount of schooling”);
… that satisfy at least a subset of the qualifications, (O’Malley, Figure 4, teaches a subset of qualifications including software skills, experience, education);
identifying a set of resumes corresponding to candidates for the job position; (O’Malley, para 0008, teaches candidates submitting a resume(s));
comparing, by one or more processors of a first computer, running locally, ... of each resume of the set of resumes to …thereby generating a ... similarity score corresponding to each resume of the set of resumes; (See at least O’Malley, para 0030, teaching computing devices; para 0026, teaches the computing environment including processors; para 0281, teaches scoring system applied to each Applicant and resumes are taught throughout O’Malley, Candidate; Comparison is taught throughout O’Malley, see at least para 0478,The “Compared to Others” system's checkbox option allows to determine what elements and/or factors would like the System to track and compare); ranking, by the one or more processors, the set of resumes based on their corresponding ... similarity score; and (O’Malley, para 0077, FIG. 44 is an example screenshot of list of Applicants that have been ranked based on scores);
selecting, by the one or more processors, a subset of the resumes with a highest ... similarity score, corresponding to candidates to interview for the job position, based on the ranking of the set of resumes (O’Malley, para 0010, rank the Applicants according to pre-determined criteria (Examiner notes score), and automation tools to contact the Applicants and schedule appointments as necessary). Yet, O’Malley does not appear to explicitly teach yet in the same field of endeavor Ghosh teaches generating a formatted prompt by formatting ... using a prompt template, wherein the prompt template is optimized for a language model (LM) as a result of a plurality of trials adjusting the prompt template (See at least Ghosh, para 0016, the natural language input can automatically invoke one or procedures that search networked systems and extract from one or more of the networked systems data that provides a basis for generating the set of job requirements…the set of job requirements can be dynamically refined with additional natural language; para 0017-0019, using machine learning, a model can be trained to invoke a procedure in response to a single statement, as for example one identifying an individual (e.g., current or former employee) such as “we need a candidate like Ms. Smith, our company's current chief engineer.” The statement invokes procedures to gather data such as Ms. Smith's education, credentials, skills, work history, and any other relevant data ... using the data as a standard, a set of job requirements can be constructed such that a preferred candidate (Examiner is interpreting preferred candidate as mock candidate) would have the same or comparable education (Examiner notes diversity criteria), credentials, skills, and work history; See at least Ghosh, para 0029, ML model can be iteratively adjusted through repeated application to the training set until the model is able to classify a test set of example user inputs with an acceptable level of accuracy);
... generating, via the LM running on a second computer, one or more mock candidates … wherein the mock candidates are generated by the LM based on the formatted prompt and the LM is a publicly available pre-trained large language model (LLM); … of the one or more mock candidates (See at least Ghosh, Figure 3 and para 0023, teaches multiple computers in a communications network; See at least Ghosh, para 0034, teaches various publicly accessible networked systems; See at least Ghosh, para 0029, teaching training machine learning model; See at least Ghosh, para 0016, the natural language input can automatically invoke one or procedures that search networked systems and extract from one or more of the networked systems data that provides a basis for generating the set of job requirements…the set of job requirements can be dynamically refined with additional natural language; para 0017-0019, using machine learning, a model can be trained to invoke a procedure in response to a single statement, as for example one identifying an individual (e.g., current or former employee) such as “we need a candidate like Ms. Smith, our company's current chief engineer.” The statement invokes procedures to gather data such as Ms. Smith's education, credentials, skills, work history, and any other relevant data ..using the data as a standard, a set of job requirements can be constructed such that a preferred candidate (Examiner is interpreting preferred candidate as mock candidate) would have the same or comparable education, credentials, skills, and work history; Further, Ghosh, para 0034, teaches define a preferred candidate (Examiner is interpreting a preferred candidate as a mock candidate) for constructing a set of job requirements).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine O’Malley with generating a formatted prompt by formatting ... using a prompt template, wherein the prompt template is optimized for a language model (LM) as a result of a plurality of trials adjusting the prompt template... generating, via the LM running on a second computer, one or more mock candidates … wherein the mock candidates are generated by the LM based on the formatted prompt and the LM is a publicly available pre-trained large language model (LLM); … of the one or more mock candidates as taught by Ghosh with the motivation for determining job requirements and assessing a job candidate can include generating a set of job requirements and ranking a job candidate (Ghosh, Abstract).
Yet, O’Malley does not appear to explicitly teach yet in the same field of endeavor Michaels teaches a vector representation ... vector (Michaels teaches vector embeddings throughout, see at least para 0048, vector embeddings... allows the system to increase its accuracy in determining the meaning and context of the resumes and job descriptions; Further, Michaels, para 0018, the system may utilize word embedding (WE) models and/or phrase embedding (PE) models to train its algorithm(s) and semantically evaluate the resumes and/or job descriptions for matches; Michaels, para 0018, teaches vector representations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine O’Malley with a vector representation ... vector as taught by Michaels with the motivation for semantically evaluating and matching resumes with job descriptions (Michaels, Abstract). The O’Malley invention now incorporating the Ghosh and Michaels invention, has all the limitations of claim 1.
Regarding Claim 2, O’Malley, now incorporating Ghosh and Michaels, teaches The method of claim 1, … identifying a set of diversity criteria for the one more candidates (O’Malley, para 0053, FIG. 20 depicts an example of Education levels completed that could represent the entire set of criteria for a particular job position (Examiner notes, this is identical to Applicant’s own specification, para 0020, recites: “The diversity criteria may include any information that may aid in generating the mock candidates. In some embodiments, diversity criteria may specify… amount of schooling”).
Yet, O’Malley does not appear to explicitly teach yet in the same field of endeavor Ghosh teaches wherein the generating the one or more mock candidates comprises (See at least Ghosh, para 0016, the natural language input can automatically invoke one or procedures that search networked systems and extract from one or more of the networked systems data that provides a basis for generating the set of job requirements…the set of job requirements can be dynamically refined with additional natural language; para 0017-0019, using machine learning, a model can be trained to invoke a procedure in response to a single statement, as for example one identifying an individual (e.g., current or former employee) such as “we need a candidate like Ms. Smith, our company's current chief engineer.” The statement invokes procedures to gather data such as Ms. Smith's education, credentials, skills, work history, and any other relevant data ... using the data as a standard, a set of job requirements can be constructed such that a preferred candidate (Examiner is interpreting preferred candidate as mock candidate) would have the same or comparable education (Examiner notes diversity criteria), credentials, skills, and work history.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine O’Malley and Michaels with wherein the generating the one or more mock candidates comprises as taught by Ghosh with the motivation for determining job requirements and assessing a job candidate can include generating a set of job requirements and ranking a job candidate (Ghosh, Abstract).
Regarding Claim 3, O’Malley, now incorporating Ghosh and Michaels, teaches The method of claim 1, wherein the comparing the ... of each resume comprises: identifying a set of diversity criteria for the one more candidates, wherein the ... similarity score of a first resume satisfying the diversity criteria is weighted more heavily than a second resume that does not satisfy the diversity criteria (O’Malley, para 0053, FIG. 20 depicts an example of Education levels completed that could represent the entire set of criteria for a particular job position (Examiner notes, this is identical to Applicant’s own specification, para 0020, recites: “The diversity criteria may include any information that may aid in generating the mock candidates. In some embodiments, diversity criteria may specify… amount of schooling”; O’Malley, para 0285, a “Criteria Ranking” function for ranking the criteria requirement for the job posting or select a “Criteria Weighting” for applying weighting to the criteria. Examiner notes for example Figure 44, the education score is weighted more heavily if selected; Further, O’Malley, para 0644, teaches including anti-discrimination ).
Yet, O’Malley does not appear to explicitly teach yet in the same field of endeavor Michaels teaches vector representation ... vector (Michaels teaches vector embeddings throughout, see at least para 0048, vector embeddings... allows the system to increase its accuracy in determining the meaning and context of the resumes and job descriptions; Further, Michaels, para 0018, teaches vector representation... the system may utilize word embedding (WE) models and/or phrase embedding (PE) models to train its algorithm(s) and semantically evaluate the resumes and/or job descriptions for matches).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine O’Malley and Ghosh with vector representation ... vector as taught by Michaels with the motivation for semantically evaluating and matching resumes with job descriptions (Michaels, Abstract).
Regarding Claim 5, O’Malley, now incorporating Ghosh and Michaels, teaches The method of claim 1.
Yet, O’Malley does not appear to explicitly teach yet in the same field of endeavor Ghosh teaches wherein the LM incorporates one of artificial intelligence or machine learning technologies in generating the one or more mock candidates (See at least Ghosh, para 0017-0019, using machine learning, a model can be trained to invoke a procedure in response to a single statement, as for example one identifying an individual (e.g., current or former employee) such as “we need a candidate like Ms. Smith, our company's current chief engineer.” The statement invokes procedures to gather data such as Ms. Smith's education, credentials, skills, work history, and any other relevant data .using the data as a standard, a set of job requirements can be constructed such that a preferred candidate (Examiner is interpreting preferred candidate as mock candidate) would have the same or comparable education, credentials, skills, and work history).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine O’Malley with wherein the LM incorporates one of artificial intelligence or machine learning technologies in generating the one or more mock candidates as taught by Ghosh with the motivation for determining job requirements and assessing a job candidate can include generating a set of job requirements and ranking a job candidate (Ghosh, Abstract).
Regarding Claim 6, O’Malley, now incorporating Ghosh and Michaels, teaches The method of claim 1, further comprising: identifying, by the one or more processors, availability for a hiring manager based on a calendar of the hiring manager; (See at least O’Malley, para 1006, The TIMES is a module that can automatically schedules events or appointments for such as interviews, which can take place through a variety of methods and venues, such as on the phone or a face-to-face interview…, all based on conditions preset, such as dates and times a NEED-MGR and/or an Interviewer selects as “Open” times on his/her calendar for the interview);
identifying, by the one or more processors, contact information for the candidates corresponding to the subset of resumes with the highest ... similarity score; and (See at least O’Malley, Figure 44, shows candidates with highest scores, for example, Joe Smith has highest score of 2502; See at least O’Malley, para 0198, teaches contact information);
sending, by the one or more processors, electronic communications scheduling interviews between the candidates corresponding to the subset of resumes with the highest similarity score and the hiring manager (See at least O’Malley, Figure 44, shows candidates with highest scores, for example, Joe Smith has highest score of 2502; O’Malley, para 0594, Email to invite potential applicants to participate in an Interview).
Yet, O’Malley does not appear to explicitly teach yet in the same field of endeavor Michaels teaches vector (Michaels teaches vector embeddings throughout, see at least para 0048, vector embeddings... allows the system to increase its accuracy in determining the meaning and context of the resumes and job descriptions; Further, Michaels, para 0018, the system may utilize word embedding (WE) models and/or phrase embedding (PE) models to train its algorithm(s) and semantically evaluate the resumes and/or job descriptions for matches. As is known in the art, a word embedding may be a learned representation of text where words that may have the same or similar meaning may have a similar representation. Each word may be represented by a real-valued vector and the word embedding methods may use a corpus of text to learn the real-valued vector representations of a set of vocabulary; para 0019, The system may utilize pre-trained word embeddings and/or pre-trained phrase embeddings, may learn new word embeddings and/or new phrase embeddings for one or more vocabulary(s) of interest, or any combination thereof.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine O’Malley and Ghosh with vector as taught by Michaels with the motivation for semantically evaluating and matching resumes with job descriptions (Michaels, Abstract).
Regarding Claim 7, O’Malley, now incorporating Ghosh and Michaels, teaches The method of claim 1, wherein the identifying the job description comprises: crawling a website; identifying a new job opening posted to the website based on the crawling; and retrieving the job description from the website (O’Malley, Abstract teaches information regarding a at least one job opening; para 0158, teaches social networks, such as a LinkedIn®, can connect and interact with the HIRES system to exchange data; Further, See at least Ghosh, Abstract, job requirements can be generated by searching networked systems and extracting data).
Regarding claims 8 and 15 the claims are an obvious variant to claim 1 above, and are therefore rejected on the same premise. O’Malley further teaches a system comprising a memory and a non-transitory computer-readable medium having instructions stored thereon that when executed by at least one computing device, cause the at least one computing device to perform operations. See at least O’Malley, para 0087, teaching the computing environment including computers, processing system, memory; Further Ghosh, para 0101, teaches a computer readable storage medium, as used herein, is not to be construed as being transitory signals per se.
Regarding Claims 9 and 16, the claims recite analogous limitations to claim 2 above, and are therefore rejected on the same premise.
Regarding Claims 10 and 17, the claims recite analogous limitations to claim 3 above, and are therefore rejected on the same premise.
Regarding Claims 12 and 19, the claims recite analogous limitations to claim 5 above, and are therefore rejected on the same premise.
Regarding Claims 13 and 20, the claims recite analogous limitations to claim 6 above, and are therefore rejected on the same premise.
Regarding Claims 14 and 21, the claim recites analogous limitations to claim 7 above, and is therefore rejected on the same premise.
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Carpenter US 2018/0336528 A1 - A server is used to screen a job candidate. An indication is received that an interview interface has been activated by a client device. A first question of a plurality of questions stored in a memory of the server is sent to the client device for access by the job candidate via the interview interface. A first response to the first question is received from the client device, and the first response is evaluated in real-time using the server before sending a second question of the plurality of questions to the client device for access by the job candidate via the interview interface.
Champaneria US 2019/0019159 A1 - A method and system for automating some aspects of a recruiting process, which may implement rules permitting the processes of sourcing candidates, setting up job interviews, and responding to candidate questions to all be automated with a computer. Such a system may match résumé s and job descriptions with a scoring system, and may initiate communications between one or more candidates and a recruiter once an appropriate number of matches have been found. The system may then be configured to field responses to commonly asked questions from a question database, and notify the recruiter if a question is asked that it cannot answer, and may further be configured to proactively ask questions to the candidate if desired. This may allow recruiters to focus on the highest level of vetting, and on aspects of the recruitment process such as promoting the hiring company and salary negotiations.
Applicants are advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Response to Arguments
Applicants arguments filed on 04/21/2026 have been fully considered but they are not persuasive.
Regarding 35 U.5.C. § 101 rejections: Examiner has updated the 101 rejection in light of the most recent claim amendments and maintains the 101 rejection. Applicant’s arguments have been fully considered but are found unpersuasive.
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 all three abstract groupings as explained in the above 101 analysis. Applicant appears to be confusing additional elements (e.g. processor, computers, large language model (LLM)) under Step 2A: Prong One: Abstract Ideas. Examiner respectfully notes the additional elements are analyzed in Step 2A: Prong Two and Step 2B of the 101 analysis. With respect to Applicants remarks on 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. See above 101 analysis.
With respect to Applicant’s remarks (remarks pages 12-14), “These additional elements improve the functioning of a computer at least in part by using less compute resources (e.g. compute time) through generating a vector similarity by comparing vector representations of candidate resumes and mock candidates. These mock candidates are generated by a public pre-trained LLM based on an optimized formatted prompt...”
Examiner respectfully disagrees.
As an initial matter, “vector representations” are not in Applicant’s specification, and ‘vector’ is merely mentioned once in Applicants specification, see above 112 rejection. Further, Applicant is merely reciting the known benefits of computing systems and machine learning. Using an already existing, publicly available language model, is not a technical solution to a technical problem. Rather, the computing systems and known machine learning models are being used for the very purpose that such computing elements are known to be used for, e.g. more efficient, faster, and etc. 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.
Therefore, Examiner maintains the 101 rejection with respect to these and all depending claims unless otherwise indicated.
Regarding 35 U.S.C. § 103 rejections. With respect to the prior art rejections, Applicants arguments have been considered but are moot in light of the most recent claim amendments as the Examiner has updated the rejections with the Ghosh and Michaels references.
With respect to Applicants remarks (pages 15-16), “Applicant has amended independent claim 1, 8 and 15, to recite "generating a formatted prompt by formatting data of a job description and data of a set of diversity criteria using a prompt template, wherein the prompt template is optimized for a language model (LM) as a result of a plurality of trials adjusting the prompt template." O'Malley, Ghosh, and Michaels, alone or in combination, do not teach or suggest at least this feature.” Examiner respectfully disagrees.
These newly added limitations are taught by a combination of the references. The primary reference O’Malley teaches it is know to have candidates, resumes and job descriptions. See at least Ghosh, para 0016, teaching the natural language input can automatically invoke one or procedures that search networked systems and extract from one or more of the networked systems data that provides a basis for generating the set of job requirements…the set of job requirements can be dynamically refined with additional natural language; para 0017-0019, using machine learning, a model can be trained to invoke a procedure in response to a single statement, as for example one identifying an individual (e.g., current or former employee) such as “we need a candidate like Ms. Smith, our company's current chief engineer.” The statement invokes procedures to gather data such as Ms. Smith's education, credentials, skills, work history, and any other relevant data ... using the data as a standard, a set of job requirements can be constructed such that a preferred candidate (Examiner is interpreting preferred candidate as mock candidate) would have the same or comparable education (Examiner notes diversity criteria), credentials, skills, and work history; See at least Ghosh, para 0029, ML model can be iteratively adjusted through repeated application to the training set until the model is able to classify a test set of example user inputs with an acceptable level of accuracy. Therefore Applicants remarks are found unpersuasive and Examiner maintains the 103 rejection.
Conclusion
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/R.R.N./Examiner, Art Unit 3629
/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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