DETAILED ACTION
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 .
Election/Restriction
Election was made by Applicant for claims 1-19 in the reply filed6/29/2026. Claim 20 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species.
Claim 20 is withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a non-elected inventions, there being no allowable generic or linking claim.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply: separate classification thereof and different field of search utilizing different search queries.
Although applicant traversed on the ground that the inventions are not patentably distinct, applicant did not submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-19 rejected under 35 U.S.C. § 101 are directed to an abstract idea without significantly more.
The claims do not provide significantly more than the judicial exception under the subject matter eligibility two-part statutory analysis, as provided below.
Regarding Step 1,
Step 1 addresses whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter according to MPEP §2106.03. Claims 1-19 all fall within one of the four statutory categories.
Regarding Step 2A [prong 1],
The claimed invention recites an abstract idea according to MPEP §2106.04. Independent claim 1, underlined below recites the following claim limitations, as an abstract idea.
Claims 1: integrated application and scheduling with efficient and accurate matching, comprising:
facilitating application submission, match display, and scheduling;
store and manage access to candidate profiles, job listings, and scheduling data; and
a matching and scheduling to automate the matching process and facilitate direct interview scheduling based on real-time employer availability.
The underlined claim limitations, under its broadest reasonable interpretation, fall under “Certain Methods of Organizing Human Activities” grouping of abstract ideas, and includes at least managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP §2106.04(a)(2)(II).
But for the recitation of generic implementation of computer system components, the claimed invention merely recites a process for managing personal behavior/relationships or interactions between people because the claimed steps recite scheduling an interview. Accordingly, since the claimed invention describes a process that falls under “Certain Methods of Organizing Human Activities” grouping, the claimed invention recites an abstract idea.
Regarding Step 2A [prong 2],
The judicial exception is not integrated into a practical application according to MPEP §2106.04(d). Claims 1 and 9 include the following additional elements:
A system for application and platform: a user interface; a database to store; a (software) module;
In particular, the additional elements cited above beyond the abstract idea are recited at a high-level of generality and simply equivalent to a generic recitation and basic functionality that amount to no more than mere instructions to apply the judicial exception using generic computer technology components.
The claimed invention merely provides an abstract-idea-based-solution implemented with generic computer processes and components recited at a high-level of generality (receiving, storing, determining, and comparing data) using computer instructions to implement the abstract idea on a computer, and merely “apply it” without any meaningful technological limits or any improvement to technology, technical field or improvement to the functioning of the computer itself.
Additionally, a user interface facilitating (data) submission and display, database to store and manage access to (data)….amounts to data gathering and selecting a particular data source or type of data to be manipulated, thus does not add any meaningful limitations, and since receiving, storing and transmitting data is considered one of the most basic functions of a computer, these additional elements are deemed as insignificant extra-solution activity to the judicial exception. The legal precedent in Electric Power Group and Ultramercial cited in MPEP 2106.05(g) indicate that selecting information, based on types of information and availability of information for collection, analysis and display, and requiring a request from a user to view an advertisement and restricting public access, are all insignificant extra-solution activity.
Therefore, the additional elements fail to integrate the recited abstract idea into any practical application since they do not impose any non-generic meaningful limits on practicing the abstract idea. Thus, the claimed invention is directed to an abstract idea.
Regarding Step 2B,
The claimed invention does not include additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP §2106.05.
As discussed above, the claimed additional elements recited above amounts to no more than mere instructions to implement the abstract idea by adding the words “apply it” using generic computer components and functionality. See MPEP §2106.05(h). Mere instructions to apply the judicial exception using generic computer components are insufficient to provide an inventive concept. Furthermore, the claimed additional elements merely limit the abstract idea to be executed in a computer environment, thus do nothing more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h).
Additionally, re-evaluating the insignificant extra-solution activities listed above, it is determined that they are also well-understood, routine, and conventional, as well. See MPEP 2106.05(d). The legal precedent in Ultramercial, Versata, Symantec, TLI, and OIP Techs court decisions cited in MPEP 2106.05(d)(II) indicate that storing and retrieving information in memory, as well as receipt and transmission of information over a computer network, and updating an activity log are a well-understood, routine, and conventional functions claimed in a generic manner, as is the case here. See also Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019) (data gathering and displaying are well-understood, routine, and conventional activities) and also buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (“That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive”).
Considered as an ordered combination, the additional elements are claimed at a high-level of generality and add nothing that is not already present when the steps are considered separately. The sequence of the claimed limitations is equally generic and otherwise held to be abstract since the combination of these additional elements is no more than mere instructions to apply the judicial exception using generic computer components operating in their ordinary and generic capacities of what is typically expected of computers storing and updating data, and receiving and transmitting data between generic computer devices. The claimed invention is not patent eligible because the additional elements are merely invoked as tools to execute the abstract idea and thus are insufficient to amount to an inventive concept significantly more than the judicial exception.
As for dependent claims below, they merely further narrow and reiterate the same abstract ideas, for displaying, storing and updating data, and receiving and transmitting data using generic data storage and transmittal techniques with the same additional elements as recited above which provide nothing more than applying the abstract idea using generic computer technology components.
2. The system of claim 1, wherein the user interface provides interactive feedback to candidates about their scheduled first-round interviews, including automated confirmation notifications, adaptive reminders based on scheduled interview dates and times, and real-time updates on changes to the scheduled interviews.
3. The system of claim 1, further comprising a security module implementing security protocols to protect data integrity and confidentiality across all subsystems.
4. The system of claim 1, further comprising a feedback loop mechanism that collects and analyzes post-interview feedback from candidates and employers to continuously improve the matching and scheduling algorithms.
5. The system of claim 1, wherein the matching and scheduling module incorporates Al-driven resume parsing to automatically extract and validate information from uploaded candidate documents, enhancing the accuracy of candidate profiles and job matches ([0024] may parse resumes 116 for actual human candidates that are similar to the mock candidates 106, [0032] [0032] In some embodiments, the CSS 102 may compare each resume 116 to each of the mock candidates 106 and generate a similarity score 118, [0009] CSS 102 may generate or cause a language model (LM) 104 to generate a set of one or more mock candidates 106. These mock candidates 106 may serve as a basis for identifying real, human candidates to fill the job opening 108 as indicated by a corresponding job description 110).
6. The system of claim 1, further comprising a modular API framework allowing integration with third-party calendars and HR tools, extending the system's functionality and enhancing the user experience for both candidates and employers.
7. The system of claim 1, wherein the matching and scheduling module uses a combination of fixed and fuzzy matching techniques to match candidates with job opportunities.
8. The system of claim 7, wherein the fixed matching techniques compare exact values for criteria including location preferences, required qualifications, and salary range.
9. The system of claim 8, wherein the fuzzy matching techniques analyze text-based data including job titles, skills, and job descriptions to identify relevant matches.
10. The system of claim 1, wherein the matching and scheduling module employs vectorization techniques to convert text-based data into vectors for efficient comparison and analysis.
11. The system of claim 10, wherein the vectorization techniques include converting user text segments from candidate profiles and posting text segments from job listings into high-dimensional vectors.
12. The system of claim 11, wherein the vectorization techniques include: extracting relevant keywords and phrases from text-based data; assigning numerical values to the extracted keywords and phrases based on their frequency and importance; and generating a multi-dimensional vector representation of the text-based data using the assigned numerical values.
13. The system of claim 1, wherein the matching and scheduling module applies vector functions to compare user vectors and posting vectors to determine the similarity between candidate profiles and job postings.
14. The system of claim 1, wherein the matching and scheduling module filters job postings in multiple stages, including an initial filtering based on parameters and a subsequent filtering based on vector comparisons.
15. The system of claim 1, wherein the matching and scheduling module applies parameter functions to compare user parameters and posting parameters to determine the relevance of job postings for a user.
16. The system of claim 14, wherein the vector functions calculate similarity scores between user vectors and posting vectors, and the matching and scheduling module ranks job postings based on these similarity scores.
17. The system of claim 16, wherein the similarity scores are calculated using cosine similarity between the user vectors and posting vectors.
18. The system of claim 16, wherein the matching and scheduling module applies clustering algorithms to group similar user vectors and posting vectors, facilitating efficient matching between candidates and job postings within the same cluster.
19. The system of claim 16, wherein the matching and scheduling module assigns different weights to various components of the user vectors and posting vectors based on their relative importance in determining job suitability.
Furthermore dependent claims 2-19 comprise the following additional elements: a security module (software), AI-driven (data) parsing, modular API framework, third-party (software) tools, fuzzy (NLP) matching. These additional elements do not provide any improvement to technology, technical field or improvement to the functioning of the computer itself, and at best simply applying the abstract idea executed in a general-purpose computer environment. Therefore the dependent claims are also directed to ineligible subject matter since they do not provide significantly more than the abstract idea itself.
Thus, after considering all claim elements in Claims 1-19 both individually and as an ordered combination, it has been determined that the claimed invention as a whole, is not enough to transform the abstract idea into a patent-eligible invention since nothing in the claim limitations provide significantly more than the abstract idea under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sachin (US 20250190948).
Regarding Claim 1,
Sachin discloses:
A system for integrated application and scheduling platform with efficient and accurate matching (Abstract; System for candidate selection and interviewing, Summary, Figs. 1-3), comprising:
a user interface (Fig. 1; user interface 128) facilitating application submission, match display, and scheduling ([0031-0035]); a database ([0050] Computer system includes one or more secondary storage devices) to store and manage access to candidate profiles, job listings, and scheduling data; and a matching and scheduling module designed to automate the matching process and facilitate direct interview scheduling based on real-time employer availability ([0030-0035] automatically match qualified candidates to new job postings, in real-time and automatically consider their resume 116 for any job postings or job openings 108, for which they may be qualified and send a notification to the candidate when their resume exceeds a threshold for a new position for which their resume 116 is being considered, and/or when an interview is to be scheduled, and automatically schedule interviews between the candidates corresponding to the resumes 116 in the candidate list 122, a scheduler 124 may include access to the calendar 126 of one or more individuals who will be conducting an interview (phone, video call, or in person). Calendar 126 may include actual access to an electronic calendar, or an indication as to which dates/times the individual is available).
Regarding Claim 2. The system of claim 1, wherein the user interface provides interactive feedback to candidates about their scheduled first-round interviews, including automated confirmation notifications, adaptive reminders based on scheduled interview dates and times, and real-time updates on changes to the scheduled interviews ([0035-0036] CSS 102 may automatically schedule interviews between the candidates corresponding to the resumes 116 in the candidate list 122 and a hiring manager or human resource professional. For example, a scheduler 124 may include access to the calendar 126 of one or more individuals who will be conducting an interview (phone, video call, or in person). Calendar 126 may include actual access to an electronic calendar, or an indication as to which dates/times the individual is available. Scheduler 124, may include an artificial intelligence or other computing system, that sends an electronic message to the candidates using the contact information to request an interview).
Regarding Claim 3. The system of claim 1, further comprising a security module implementing security protocols to protect data integrity and confidentiality across all subsystems ([0009] CSS 102 may generate or cause a language model (LM) 104 to generate a set of one or more mock candidates 106. These mock candidates 106 may serve as a basis for identifying real, human candidates to fill the job opening 108 as indicated by a corresponding job description 110. CSS 102 may also help protect the privacy of candidates and provide them with up-to-date job listings).
Regarding Claim 4. The system of claim 1, further comprising a feedback loop mechanism that collects and analyzes post-interview feedback from candidates and employers to continuously improve the matching and scheduling algorithms ([0025] hiring manager or other user can approve the mock candidates 106 and then CSS 102 may more accurately identify which resumes 116 correspond to the those mock candidates 106, CSS 102 may more accurately identify which resumes 116 correspond to the those mock candidates 106. CSS 102 provides faster and more accurate results relative to doing a simple keyword search based on the job description 110).
Regarding Claim 5. The system of claim 1, wherein the matching and scheduling module incorporates Al-driven resume parsing to automatically extract and validate information from uploaded candidate documents, enhancing the accuracy of candidate profiles and job matches ([0016] LM 104 may include a pre-trained artificial intelligence system that is configured or designed to perform various tasks, [0018] LM 104 may be configured to read, process, and analyze a job description 104, and generate a set of mock candidates 106 whose mock qualifications would satisfy in the qualifications 112 in the job description 104, [0025] CSS 102 may more accurately identify which resumes 116 correspond to the those mock candidates 106. CSS 102 provides faster and more accurate results relative to doing a simple keyword search based on the job description 110).
Regarding Claim 6. The system of claim 1, further comprising a modular API framework allowing integration with third-party calendars and HR tools, extending the system's functionality and enhancing the user experience for both candidates and employers ([0035-0036] a scheduler 124 may include access to the calendar 126 of one or more individuals who will be conducting an interview (phone, video call, or in person). Calendar 126 may include actual access to an electronic calendar, or an indication as to which dates/times the individual is available. CSS 102 may extract contact information for the actual human candidates from their resumes 116 (e.g., which may include any other application forms or questionnaires or submitted materials). The contact information may include email address, phone number, social media handles, etc. Scheduler 124, may include an artificial intelligence or other computing system, that sends an electronic message to the candidates using the contact information to request an interview).
Regarding Claim 7. The system of claim 1, wherein the matching and scheduling module uses a combination of fixed and fuzzy matching techniques to match candidates with job opportunities ([0016] LM 104 may include a pre-trained artificial intelligence system that is configured or designed to perform various tasks, [0030] CSS 102 may automatically match qualified candidates to new job postings, in real-time).
Regarding Claim 8. The system of claim 7, wherein the fixed matching techniques compare exact values for criteria including location preferences, required qualifications, and salary range ([0019-0020] CSS 102 may be configured to provide additional detail, instructions, or diversity criteria 114 to LM 104 which may create mock candidates 106 accordingly, [0020] The diversity criteria 114 may include any information that may aid in generating the mock candidates 106. In some embodiments, diversity criteria 114 may specify diversity preferences, based on gender, years of experience, amount of schooling, which schools attended, certifications, type of experience, or any other legal diversity criteria. In some embodiments, the similarity score 118 of a first resume 116 satisfying the diversity criteria 114 is weighted more heavily than a second resume 116 that does not satisfy the diversity criteria 114. In some embodiments, the diversity criteria 114 includes multiple different elements, for example schooling and experience, each of which may be weighted differently. For example, experience may weighted more heavily than schooling. Then, for example, a resume 116 satisfying the experience criteria may be ranked higher than a resume satisfying the schooling criteria).
Regarding Claim 9. The system of claim 8, wherein the fuzzy matching techniques analyze text-based data including job titles, skills, and job descriptions to identify relevant matches ([0031] automatically consider their resume 116 for any job postings or job openings 108, for which they may be qualified. Then for example, CSS 102 may send a notification to the candidate when their resume exceeds a threshold for a new position for which their resume 116 is being considered, or when an interview is to be scheduled).
Regarding Claim 10. The system of claim 1, wherein the matching and scheduling module employs vectorization techniques to convert text-based data into vectors for efficient comparison and analysis ([0022-0032] he CSS 102 may compare each resume 116 to each of the mock candidates 106 and generate a similarity score 118. For example, if there are two mock candidates 106, in some embodiments, CSS 102 may generate for each resume 116 three similarity scores 118. The first similarity score 118 may be a similarity of the resume to the first mock candidate, the second similarity score 118 may be a similarity of the resume to the second mock candidate, and the third similarity score 118 may be an aggregated similarity to all the mock candidates 106 and the job description 110. In some embodiments, the similarity score 118 may be a vector similarity score or measure).
Regarding Claim 11. The system of claim 10, wherein the vectorization techniques include converting user text segments from candidate profiles and posting text segments from job listings into high-dimensional vectors ([0022-0032] he CSS 102 may compare each resume 116 to each of the mock candidates 106 and generate a similarity score 118. For example, if there are two mock candidates 106, in some embodiments, CSS 102 may generate for each resume 116 three similarity scores 118. The first similarity score 118 may be a similarity of the resume to the first mock candidate, the second similarity score 118 may be a similarity of the resume to the second mock candidate, and the third similarity score 118 may be an aggregated similarity to all the mock candidates 106 and the job description 110. In some embodiments, the similarity score 118 may be a vector similarity score or measure).
Regarding Claim 12. The system of claim 11, wherein the vectorization techniques include: extracting relevant keywords and phrases from text-based data; assigning numerical values to the extracted keywords and phrases based on their frequency and importance; and generating a multi-dimensional vector representation of the text-based data using the assigned numerical values ([0022-0032] CSS 102 may parse resumes 116 for actual human candidates that are similar to the mock candidates 106. The first similarity score 118 may be a similarity of the resume to the first mock candidate, the second similarity score 118 may be a similarity of the resume to the second mock candidate, and the third similarity score 118 may be an aggregated similarity to all the mock candidates 106 and the job description 110. In some embodiments, the similarity score 118 may be a vector similarity score or measure).
Regarding Claim 13. The system of claim 1, wherein the matching and scheduling module applies vector functions to compare user vectors and posting vectors to determine the similarity between candidate profiles and job postings (([0022-0032] CSS 102 may parse resumes 116 for actual human candidates that are similar to the mock candidates 106. The first similarity score 118 may be a similarity of the resume to the first mock candidate, the second similarity score 118 may be a similarity of the resume to the second mock candidate, and the third similarity score 118 may be an aggregated similarity to all the mock candidates 106 and the job description 110. In some embodiments, the similarity score 118 may be a vector similarity score or measure).
Regarding Claim 14. The system of claim 1, wherein the matching and scheduling module filters job postings in multiple stages, including an initial filtering based on parameters and a subsequent filtering based on vector comparisons ([0033-0034] [0033] CSS 102 may then generate one or more rankings 120. In some embodiments, ranking 120 may include a single ranking of all the resumes 116 in accordance of the aggregated or final similarity score 118. In other embodiments, the resumes 116 may be organized into two rankings, one for the first mock candidate 106 based on the first similarity score 118, and a second for the second mock candidate 106 based on the second similarity score 118. The ranking 120 may optionally include all three ranked lists. In some embodiments, CSS 102 may generate a candidate list 122. Candidate list 122 may include the top five candidates for the job description 110 based on the similarity score 118, or any other number of top candidates. Or, for example, candidate list 122 may include any number of candidates who resumes exceed a threshold similarity score 118. In some embodiments, candidate list 122 may include multiple candidate lists 122 corresponding to each mock candidate 106 and/or the aggregated score).
Regarding Claim 15. The system of claim 1, wherein the matching and scheduling module applies parameter functions to compare user parameters and posting parameters to determine the relevance of job postings for a user ([0032] CSS 102 may compare each user resume 116 to each of the mock ideal candidates 106 and generate a similarity score 118, [0024] may parse resumes 116 for actual human candidates that are similar to the mock candidates 106).
Regarding Claim 16. The system of claim 14, wherein the vector functions calculate similarity scores between user vectors and posting vectors, and the matching and scheduling module ranks job postings based on these similarity scores ([0032-0034] similarity score or measure for determining candidate can be a vector and the first similarity score 118 may be a similarity of the resume to the first mock candidate, the second similarity score 118 may be a similarity of the resume to the second mock candidate, and the third similarity score 118 may be an aggregated similarity to all the mock candidates 106 and the job description 110. In some embodiments, the similarity score 118 may be a vector similarity score or measure, CSS 102 may then generate one or more rankings 120 and Candidate list 122 may include the top five candidates for the job description 110 based on the similarity score 118).
Regarding Claim 17. The system of claim 16, wherein the similarity scores are calculated using cosine similarity between the user vectors and posting vectors ([0032] similarity score or measure for determining candidate can be a vector, CSS 102 may determine that the similarity score 118 for the candidate and the mock candidates ideal for the posting 106 for the second posting exceeds a threshold value).
Regarding Claim 18. The system of claim 16, wherein the matching and scheduling module applies clustering algorithms to group similar user vectors and posting vectors, facilitating efficient matching between candidates and job postings within the same cluster ([0032] similarity score or measure for determining candidate can be a vector, [0029] CSS 102 may determine that the similarity score 118 for the candidate and the mock ideal candidates 106 for the second posting exceeds a threshold value).
Regarding Claim 19. The system of claim 16, wherein the matching and scheduling module assigns different weights to various components of the user vectors and posting vectors based on their relative importance in determining job suitability ([0020] similarity score 118 of a first resume 116 satisfying the diversity criteria 114 is weighted more heavily than a second resume 116 that does not satisfy the diversity criteria 114, or other components like schooling and experience, each of which may be weighted differently. For example, experience may weighted more heavily than schooling, [0032] similarity score or measure for determining candidate can be a vector).
Conclusion
The relevant prior art made of record not relied upon but considered pertinent to applicant's disclosure can be found in the current and/or previous PTO-892 Notice of References Cited. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to LAURA YESILDAG whose direct telephone number is (571) 270-5066 and work schedule is generally Monday-Friday, from 9:00 AM - 5:00 PM ET.
In order to receive any email communication from the Examiner, filing for official authorization for Internet Communication is required. The authorization form can be accessed at https://www.uspto.gov/sites/default/files/documents/sb0439.pdf.
Examiner interviews can be requested by telephone or are available using the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the Examiner are unsuccessful, the Examiner’s Supervisor, LYNDA JASMIN, can be reached at (571) 272-6782 for any urgent matter that needs immediate attention. Additional information regarding the status of an application may be obtained from the USPTO Patent Center. For more information about the USPTO Patent Center, please access https://patentcenter.uspto.gov/ The Patent Center is available to all users for electronic filing and management of patent applications and can be contacted for questions at 1-866-217-9197 or 571-272-4100.
/LAURA YESILDAG/Primary Examiner, Art Unit 3629
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