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 .
Status of Claims
The following is a final office action.
Claims 1-20 are currently pending and have been examined.
Claim 1 is newly amended see REMARKS July 23, 2025.
Claims 7-20 are newly added see REMARKS July 23, 2025.
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: Claim 1-7 recites a method (i.e. a series of steps), claims 8-14 recite a non-transitory computer readable storage, and claims 15-20 recite a device and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claims 1, 8, and 15 recites a method comprising: obtaining one or more job requirements for an open position and a list of candidate resumes; extracting job-related skills from a specific candidate resume in the list of candidate resumes; inputting the extracted job-related skills of the specific resume, uses a graph comprising interconnected job-related skills and job-related tasks, wherein a task from the job-related tasks is a process to be executed by one or more job-related skills; identifying a first set of job-related tasks that match the job-related skills of the specific resume based on the graph: converts text representing the job-related skills into mathematical vector representations and determines similarity scores between the vector representations and a pool of tasks related sentences; inputting the one or more job requirements that represent job-related skills, uses the graph of interconnected job-related skills and job-related tasks; to identify a second set of job-related tasks that match the one or more job requirements based on the graph: executing a similarity function between the first set of job-related tasks and the second set of job-related tasks, wherein the similarity function computes a cosine similarity between vector representations of the tasks; assigning a score to the specific candidate resume to fit the open position according to an output of the similarity function; and outputting the scored candidate resume with associated task-based matching data.
The claims recite a certain method of organizing human activity. The claims recite a certain method of organizing human activity as the disclosure is directed to managing personal behavior or relationships or interactions between people. The claims recite a series of steps for obtaining job requirements for an open position and a list of candidate resumes and determining a score to a specific resume according to the output of a similarity function. The steps merely amount to determining the similarities between a candidate’s resume and job requirements for a position during the hiring process to identify potential candidates for an open position.
Alternatively, the Examiner further finds that the claims recite a mental process. The claims recite a method for obtaining job requirements for an open position, extracting job related skills from a candidate resume, identifying a first set of job-related tasks that match the job-related skills of the resume, identifying a second set of job-related tasks that match one or more job requirements, executing a similarity function between the first and second set of job-related tasks, and assigning a score to the specific candidate resume. The steps of determining skills related to a job candidate’s resume and an open position and determining their similarities to identify a candidate that is a match for an open position are capable of being performed in the human mind or by using simple tools such as pen and paper. Additionally, the courts have identified concepts such as observation, evaluation, judgement and opinion as being concepts capable of being performed in the human mind. Therefore, the claims recite an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: a computer, digital data stored in a database, and a trained machine learning model.
Claim 8: A non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps; digital data stored in a database, and a trained machine learning model.
Claim 15: A device comprising: a processor; and a non-transitory computer-readable medium storing program instructions that, when executed by the processor, cause the processor to perform the steps; digital data stored in a database, and a trained machine learning model.
However, the limitations merely amount to adding 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 additional elements are directed to merely using a computer and a computer model to perform the abstract idea of receiving and analyzing information related to a job candidate’s resume and a job opening. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional limitations amount to adding 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 additional elements are directed to merely a generic computer for performing the claim limitations of obtaining, extracting, and analyzing information from a job candidate’s resume and a job opening to determine a similarity score between them. Therefore, the additional elements are directed to merely apply it or being applied to perform the abstract idea. The additional elements do not amount to significantly more than the judicial exception.
Claims 2-7, 9-14, and 16-20 are directed to further narrowing the abstract idea of analyzing a candidate’s resume in comparison to job requirements.
Claims 2-7, 9-14, and 16-20 do not recite any additional elements that were not discussed in the above analysis.
Therefore, claims 1-20 are rejected under U.S.C. 101.
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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crow (US 2005/0080656) in view of Brown (US 2006/0106638) further in view of Hardtke (US 2014/0122355).
Claims 1, 8, and 15: Crow discloses a computerized method performed on digital data stored in a database, the method comprising: obtaining one or more job requirements from an open position and a list of candidate resumes (Paragraph [0010-0016]; [0050-0056]; [0077-0084]; [0208-0209]; [0251]; Figs. 3 and 13, various technologies described relate to conceptualization of job candidate data. Conceptualization can include a process of converting a document [a resume] into an abstract representation that desirably accurately reflects the intended meaning of the author. In the example, the job candidate data is represented in electronic form and can include an electronic representation of the candidate’s resume. A resume parser can convert the unstructured job candidate data into a structured representation of the data. A conceptualizer analyzes structured the job candidate to generate conceptualized job candidate data. The match engine can analyze the conceptualized job candidate data and the desired job candidate criteria to find the one or more job candidate matches the desired job candidate criteria. A technique involving an n-dimensional concept space can be used to match candidates to desired job criteria. The desired job candidate criteria can take the form of a point of the same n-dimensional concept space. After job candidate data is conceptualized, it can be included in a collection of other job candidate data for matching against job requisitions. A job requisition object can be the basic query specifier. The job requisition can be a data structure that carries a standardized description of a job requisition (e.g. a query with desired criteria));
Extracting job related skills from a specific candidate resume in the list of candidate resumes (Paragraph [0010-0016]; [0050-0056]; [0077-0084]; [0208-0209]; Figs. 3 and 13, various technologies described relate to conceptualization of job candidate data. Conceptualization can include a process of converting a document [a resume] into an abstract representation that desirably accurately reflects the intended meaning of the author. In the example, the job candidate data is represented in electronic form and can include an electronic representation of the candidate’s resume. A resume parser can convert the unstructured job candidate data into a structured representation of the data. A conceptualizer analyzes structured the job candidate to generate conceptualized job candidate data. The match engine can analyze the conceptualized job candidate data and the desired job candidate criteria to find the one or more job candidate matches the desired job candidate criteria. A technique involving an n-dimensional concept space can be used to match candidates to desired job criteria. The desired job candidate criteria can take the form of a point of the same n-dimensional concept space. After job candidate data is conceptualized, it can be included in a collection of other job candidate data for matching against job requisitions);
Inputting the extracted job-related skills of the specific resume into a trained machine learning model, said machine learning model comprising a graph comprising interconnected job-related skills and job-related tasks, wherein a task from the job-related tasks comprises a process to be executed by one or more job-related skills (Paragraph [0011-0013]; [0052]; [0066-0068]; [0164] Figs. 19-20, the conceptualizer can include an ontology which can represent knowledge about the field of human resources. The ontology can include one or more taxonomies which can be hierarchically arranged, specifying roles, skills, and the like. Via a concept score, job candidate data can be represented by a point in n-dimensional space, sometimes called concept space. Similarly, desired criteria can be represented in the same concept space. A conceptualizer analyzes structured job candidate data to generate conceptualized job candidate data. The conceptualized job candidate data includes one or more concepts extracted via the analysis. In an example of concept entries provide information about how to extract concepts in job applicant data and how concepts are related to each other. An ontology can be represented via a variety of data structures. For example, a database can be used to indicate relationships between entries in the ontology. Concept entries can be organized via taxonomies. A taxonomy can include a plurality of concept entries related to a particular family of concepts (e.g. job roles, job skills, and the like). A hierarchical arrangement within the taxonomy can further organize the concepts into parent-child relationships. For example, a role can be associated with one or more skills. Similarly, a skill may be associated with one or more roles or one or more other skills);
the model identifying a first set of job-related tasks that match the job-related skills of the first resume based on the graph (Paragraph [0011-0013]; [0052]; [0066-0068]; [0164] Figs. 19-20, the conceptualizer can include an ontology which can represent knowledge about the field of human resources. The ontology can include one or more taxonomies which can be hierarchically arranged, specifying roles, skills, and the like. Via a concept score, job candidate data can be represented by a point in n-dimensional space, sometimes called concept space. Similarly, desired criteria can be represented in the same concept space. A conceptualizer analyzes structured job candidate data to generate conceptualized job candidate data. The conceptualized job candidate data includes one or more concepts extracted via the analysis. In an example of concept entries provide information about how to extract concepts in job applicant data and how concepts are related to each other. An ontology can be represented via a variety of data structures. For example, a database can be used to indicate relationships between entries in the ontology. Concept entries can be organized via taxonomies. A taxonomy can include a plurality of concept entries related to a particular family of concepts (e.g. job roles, job skills, and the like). A hierarchical arrangement within the taxonomy can further organize the concepts into parent-child relationships. For example, a role can be associated with one or more skills. Similarly, a skill may be associated with one or more roles or one or more other skills);
Executing a similarity function between the first set of job-related tasks and the second set of job-related tasks in the job requirements (Paragraph [0010-0016]; [0050-0056]; [0077-0084]; [0208-0209]; Figs. 3 and 13, various technologies described relate to conceptualization of job candidate data. Conceptualization can include a process of converting a document (a resume) into an abstract representation that desirably accurately reflects the intended meaning of the author. In the example, the job candidate data is represented in electronic form and can include an electronic representation of the candidate’s resume. A resume parser can convert the unstructured job candidate data into a structured representation of the data. A conceptualizer analyzes structured the job candidate to generate conceptualized job candidate data. The match engine can analyze the conceptualized job candidate data and the desired job candidate criteria to find the one or more job candidate matches the desired job candidate criteria. A technique involving an n-dimensional concept space can be used to match candidates to desired job criteria. The desired job candidate criteria can take the form of a point of the same n-dimensional concept space. After job candidate data is conceptualized, it can be included in a collection of other job candidate data for matching against job requisitions);
Assigning a score to the specific candidate resume to fit the open position according to the output of the similarity function and outputting the scored candidate resume with associated task-based matching data (Paragraph [0010-0016]; [0050-0056]; [0077-0085]; [0208-0209]; [00264]; Figs. 3 and 13, various technologies described relate to conceptualization of job candidate data. Conceptualization can include a process of converting a document (a resume) into an abstract representation that desirably accurately reflects the intended meaning of the author. In the example, the job candidate data is represented in electronic form and can include an electronic representation of the candidate’s resume. A resume parser can convert the unstructured job candidate data into a structured representation of the data. A conceptualizer analyzes structured the job candidate to generate conceptualized job candidate data. The match engine can analyze the conceptualized job candidate data and the desired job candidate criteria to find the one or more job candidate matches the desired job candidate criteria. A technique involving an n-dimensional concept space can be used to match candidates to desired job criteria. The desired job candidate criteria can take the form of a point of the same n-dimensional concept space. After job candidate data is conceptualized, it can be included in a collection of other job candidate data for matching against job requisitions. The distance between the requisition and the candidate can be calculated using a simple geometric equation as one exemplary way of determining a match. An overall match score is a combination of the scores of the individual requirements).
However, Crow does not disclose wherein the machine learning model converts text representing the job-related skills into mathematical vector representations and determines similarity scores between the vector representations and a pool of tasks related sentences; inputting the one or more job requirements that represent job-related skills into the machine learning model, said machine learning model using the graph of interconnected job-related skills and job-related tasks; to identify a second set of job-related tasks that match the one or more job requirements based on the graph; wherein the similarity function computes a cosine similarity between vector representations of the tasks;
In the same field of endeavor of identifying job candidates for a job Hardtke teaches wherein the machine learning model converts text representing the job-related skills into mathematical vector representations and determines similarity scores between the vector representations and a pool of tasks related sentences; wherein the similarity function computes a cosine similarity between vector representations of the tasks (Paragraph [0009-0010]; [0075-0077]; [0087]; [0105] the present technology is based on an approach in which a combination of information in a candidate’s resume, a description of a job opening, and external data is utilized to inform a set of machine learning algorithms that match job openings to candidates by calculating a score, referred to as a suitability score. The suitability score is a composite quantity made up of contributions from various features that are found in description of job openings, candidate resumes, and various external data. The suitability score is computed form a mathematical function that takes the vector of values and outputs a single number. All of the features are calculated as cosine similarities or sums. When comparing a portion of the description of the job opening with a portion of a candidate’s resume, the cosine similarity is calculated as the vector cosine of the word vectors formed after stop-word removal. During parsing of a job description or resume, common words are identified and removed. The remaining words are considered further in the analysis. Another way of deriving a weighting coefficient for a feature is to analyze data from a large-scale comparison of resumes to job openings using a method selected from machine learning; neural networks; support vector machines; etc.).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of modify the system of conceptualizing job and job candidate data to help create a representation of a job candidate’s information and match them to a job role as disclosed by Crow (Crow [0010]) with the system of wherein the machine learning model converts text representing the job-related skills into mathematical vector representations and determines similarity scores between the vector representations and a pool of tasks related sentences; wherein the similarity function computes a cosine similarity between vector representations of the tasks as taught by Hardtke (Hardtke [0087]). With the motivation of helping to match potential job candidates with open positions based on a similarity between a job description and a resume (Hardtke [0004]).
In the same field of endeavor of conceptualizing job skills Brown teaches inputting the one or more job requirements that represent job-related skills into the computerized model, said computerized model uses the graph of interconnected job-related skills and job-related tasks (Paragraph [0006-0008]; [0010]; [0025-0026]; [0056]; Fig. 4, in a first embodiment, a method includes identifying a plurality of tasks associated with at least one job position. The method includes grouping the plurality of tasks into one or more groups. Each group includes at least one of the identified tasks. The method includes storing for each of the one or more groups information identifying the at least one task associated with the group and the at least one additional characteristic associated with the group. In another embodiment, a computer readable medium is encoded with a data structure. The data structure includes information identifying a group of tasks associated with a job position. For each task in the group information identifying at least one of a skill and an ability associated with the task is included);
the model identifying a second set of job-related tasks that match the one or more job requirements based on the graph (Paragraph [0006-0008]; [0010]; [0056]; Fig. 4, in a first embodiment, a method includes identifying a plurality of tasks associated with at least one job position. The method includes grouping the plurality of tasks into one or more groups. Each group includes at least one of the identified tasks. The method includes storing for each of the one or more groups information identifying the at least one task associated with the group and the at least one additional characteristic associated with the group. In another embodiment, a computer readable medium is encoded with a data structure. The data structure includes information identifying a group of tasks associated with a job position. For each task in the group information identifying at least one of a skill and an ability associated with the task is included).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of modify the system of conceptualizing job and job candidate data to help create a representation of a job candidate’s information and match them to a job role as disclosed by Crow (Crow [0010]) with the system of inputting the one or more job requirements that represent job-related skills into the model, said model uses the graph of interconnected job-related skills and job-related tasks; the model identifying a second set of job-related tasks that match the one or more job requirements based on the graph as taught by Brown (Brown [0006]). With the motivation of helping to analyze various tasks and determine what skills are best matched for performing them to help match various job candidate information to a desired job role (Brown [0003]).
Claims 2, 9, and 16: Modified Crow discloses the method as per claim 1, the non-transitory computer readable storage medium as per claim 8, and the device as per claim 15. Crow further discloses further comprises obtaining a graph stored in the database, the graph comprises weights or other values indicating a similarity between the job-related skills and the job-related tasks, such that the weights assist the similarity function to identify whether or not a candidate with the specific candidate resume has the relevant experience for a specific job-related task included in the open position (Paragraph [0010-0016]; [0050-0056]; [0077-0084]; [0208-0209]; Figs. 3 and 13, various technologies described relate to conceptualization of job candidate data. Conceptualization can include a process of converting a document (a resume) into an abstract representation that desirably accurately reflects the intended meaning of the author. In the example, the job candidate data is represented in electronic form and can include an electronic representation of the candidate’s resume. A resume parser can convert the unstructured job candidate data into a structured representation of the data. A conceptualizer analyzes structured the job candidate to generate conceptualized job candidate data. The match engine can analyze the conceptualized job candidate data and the desired job candidate criteria to find the one or more job candidate matches the desired job candidate criteria. A technique involving an n-dimensional concept space can be used to match candidates to desired job criteria. The desired job candidate criteria can take the form of a point of the same n-dimensional concept space. After job candidate data is conceptualized, it can be included in a collection of other job candidate data for matching against job requisitions).
Claims 3, 10, and 17: Modified Crow discloses the method as per claim 1, the non-transitory computer readable storage medium as per claim 8, and the device as per claim 15.Crow further discloses further comprises comparing a number of job-related skills outputted by the model in the specific candidate's resume to a list of skills connected to a specific task in the graph and determining that the specific candidate matches the specific task based on the comparison (Paragraph [0010-0016]; [0050-0056]; [0077-0084]; [0208-0209]; Figs. 3 and 13, various technologies described relate to conceptualization of job candidate data. Conceptualization can include a process of converting a document (a resume) into an abstract representation that desirably accurately reflects the intended meaning of the author. In the example, the job candidate data is represented in electronic form and can include an electronic representation of the candidate’s resume. A resume parser can convert the unstructured job candidate data into a structured representation of the data. A conceptualizer analyzes structured the job candidate to generate conceptualized job candidate data. The match engine can analyze the conceptualized job candidate data and the desired job candidate criteria to find the one or more job candidate matches the desired job candidate criteria. A technique involving an n-dimensional concept space can be used to match candidates to desired job criteria. The desired job candidate criteria can take the form of a point of the same n-dimensional concept space. After job candidate data is conceptualized, it can be included in a collection of other job candidate data for matching against job requisitions).
Claims 4 and 11: Modified Crow discloses the method as per claim 1 and the non-transitory computer readable storage medium as per claim 8. Crow further discloses wherein the graph comprises edges connecting the job-related skills and the job-related tasks, the edges have weights, and the comparison comprises accumulating the weights of the skills connected to the specific task and comparing the accumulated value to a threshold (Paragraph [0010-0016]; [0050-0056]; [0077-0084]; [0087-0088]; [0208-0209]; Figs. 3 and 13, various technologies described relate to conceptualization of job candidate data. Conceptualization can include a process of converting a document (a resume) into an abstract representation that desirably accurately reflects the intended meaning of the author. In the example, the job candidate data is represented in electronic form and can include an electronic representation of the candidate’s resume. A resume parser can convert the unstructured job candidate data into a structured representation of the data. A conceptualizer analyzes structured the job candidate to generate conceptualized job candidate data. The match engine can analyze the conceptualized job candidate data and the desired job candidate criteria to find the one or more job candidate matches the desired job candidate criteria. A technique involving an n-dimensional concept space can be used to match candidates to desired job criteria. The desired job candidate criteria can take the form of a point of the same n-dimensional concept space. After job candidate data is conceptualized, it can be included in a collection of other job candidate data for matching against job requisitions. A threshold or other requirements can be designated with the system ignoring candidates who do not at least meet the threshold).
Claims 5, 12, and 18: Modified Crow discloses the method as per claim 1, the non-transitory computer readable storage medium as per claim 8, and the device as per claim 15. Crow further discloses further comprises generating an N-dimensional vector based on text in the job-related tasks extracted from each resume of the candidates' resumes, such that there is a unique vector for each resume (Paragraph [0010-0016]; [0050-0056]; [0077-0084]; [0087-0088]; [0208-0209]; Figs. 3 and 13, various technologies described relate to conceptualization of job candidate data. Conceptualization can include a process of converting a document (a resume) into an abstract representation that desirably accurately reflects the intended meaning of the author. In the example, the job candidate data is represented in electronic form and can include an electronic representation of the candidate’s resume. A resume parser can convert the unstructured job candidate data into a structured representation of the data. A conceptualizer analyzes structured the job candidate to generate conceptualized job candidate data. The match engine can analyze the conceptualized job candidate data and the desired job candidate criteria to find the one or more job candidate matches the desired job candidate criteria. A technique involving an n-dimensional concept space can be used to match candidates to desired job criteria. The desired job candidate criteria can take the form of a point of the same n-dimensional concept space. After job candidate data is conceptualized, it can be included in a collection of other job candidate data for matching against job requisitions. A threshold or other requirements can be designated with the system ignoring candidates who do not at least meet the threshold).
Claims 6, 13, and 19: Modified Crow discloses the method as per claim 1, the non-transitory computer readable storage medium as per claim 8, and the device as per claim 15. Crow further discloses wherein the candidate is considered as matching a job in case the result of the similarity function is lower than a threshold or higher than a threshold (Paragraph [0010-0016]; [0050-0056]; [0077-0084]; [0087-0088]; [0208-0209]; Figs. 3 and 13, various technologies described relate to conceptualization of job candidate data. Conceptualization can include a process of converting a document (a resume) into an abstract representation that desirably accurately reflects the intended meaning of the author. In the example, the job candidate data is represented in electronic form and can include an electronic representation of the candidate’s resume. A resume parser can convert the unstructured job candidate data into a structured representation of the data. A conceptualizer analyzes structured the job candidate to generate conceptualized job candidate data. The match engine can analyze the conceptualized job candidate data and the desired job candidate criteria to find the one or more job candidate matches the desired job candidate criteria. A technique involving an n-dimensional concept space can be used to match candidates to desired job criteria. The desired job candidate criteria can take the form of a point of the same n-dimensional concept space. After job candidate data is conceptualized, it can be included in a collection of other job candidate data for matching against job requisitions. A threshold or other requirements can be designated with the system ignoring candidates who do not at least meet the threshold).
Claims 7, 14, and 20: Modified Crow discloses the method as per claim 1, the non-transitory computer readable storage medium as per claim 8, and the device as per claim 15. However, Crow does not disclose wherein identifying the first set of job-related tasks comprises: parsing the text representing the job-related skills; identifying suspected sentences that are likely to include tasks from the parsed text; converting the suspected sentences into mathematical representations using a sentence embedder; executing a similarity function between the suspected sentences and related sentences associated with known tasks; and adding a task to the first set of job-related tasks when the similarity between the sentences matches a predefined rule.
In the same field of endeavor of identifying job candidates for a job Hardtke teaches wherein identifying the first set of job-related tasks comprises: parsing the text representing the job-related skills; identifying suspected sentences that are likely to include tasks from the parsed text; converting the suspected sentences into mathematical representations using a sentence embedder; executing a similarity function between the suspected sentences and related sentences associated with known tasks; and adding a task to the first set of job-related tasks when the similarity between the sentences matches a predefined rule (Paragraph [0009-0010]; [0075-0077]; [0087]; [0105] the present technology is based on an approach in which a combination of information in a candidate’s resume, a description of a job opening, and external data is utilized to inform a set of machine learning algorithms that match job openings to candidates by calculating a score, referred to as a suitability score. The suitability score is a composite quantity made up of contributions from various features that are found in description of job openings, candidate resumes, and various external data. The suitability score is computed form a mathematical function that takes the vector of values and outputs a single number. All of the features are calculated as cosine similarities or sums. When comparing a portion of the description of the job opening with a portion of a candidate’s resume, the cosine similarity is calculated as the vector cosine of the word vectors formed after stop-word removal. During parsing of a job description or resume, common words are identified and removed. The remaining words are considered further in the analysis. Another way of deriving a weighting coefficient for a feature is to analyze data from a large-scale comparison of resumes to job openings using a method selected from machine learning; neural networks; support vector machines; etc.).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of modify the system of conceptualizing job and job candidate data to help create a representation of a job candidate’s information and match them to a job role as disclosed by Crow (Crow [0010]) with the system of wherein identifying the first set of job-related tasks comprises: parsing the text representing the job-related skills; identifying suspected sentences that are likely to include tasks from the parsed text; converting the suspected sentences into mathematical representations using a sentence embedder; executing a similarity function between the suspected sentences and related sentences associated with known tasks; and adding a task to the first set of job-related tasks when the similarity between the sentences matches a predefined rule as taught by Hardtke (Hardtke [0087]). With the motivation of helping to match potential job candidates with open positions based on a similarity between a job description and a resume (Hardtke [0004]).
Therefore, claims 1-20 are rejected under U.S.C. 103.
Response to Arguments
Applicant’s arguments, see REMARKS, filed July 23, 2025, with respect to the rejections of claims 1-20 under U.S.C. 101 have been fully considered but are not persuasive.
The applicant argues that the claims do not recite an abstract idea as they recite a specific machine learning architecture for matching data structures. However, the examiner respectfully disagrees as the claims recite a method for obtaining one or more job requirements for an open position and a list of candidate resumes, extracting job-related skills from a specific candidate resume in the list of candidate resumes; identifying a first set of job-related tasks that match the job-related skills of the specific resume based on a graph; using a graph of interconnected job-related skills and job-related tasks to identify a second set of job related tasks that match the one or more job requirements based on the graph; executing a similarity function between the first set of job-related tasks and the second set of job-related tasks, wherein the similarity function computes a cosine similarity between vector representations of the tasks; assigning a score to the specific candidate resume to fit the open position according to an output of the similarity function; and outputting the score candidate resume with associated task-based matching data. The examiner finds that the claims recite a mental process as a person is capable of mentally, or with simple tools such as pen and paper, obtaining information such as job requirements and candidate resumes, extracting information, using a graph to determine a fist and second set of job-related tasks based on the requirements and resumes, and determine a similarity between the first and second sets of tasks to generate a score of how well a job candidate’s skill match the job requirements. Job recruiters frequently are mentally relating written job descriptions with resume information to determine overlapping concepts when determining potential candidates for a position. Alternatively the claims recite a certain method of organizing human activity as they recite a series of steps for analyzing job candidate and job requirement information to determine how well a job candidate’s resume matches the job requirements for an open position. Therefore, the claims recite an abstract idea.
The representative further argues that the additional elements direct the claims to a practical application as they recite a technical solution to the technical problem of leveraging vector-based representations and specialized similarity calculations to accurately match job candidates to job requirements. However, the examiner respectfully disagrees as the additional elements of a computer and a trained machine learning model to perform the method of receiving and analyzing candidate information through a series of steps are directed to merely “apply it” or applying generic computer elements to perform the abstract idea. The claims do not recite a technical improvement or technical solution as they merely use a trained machine learning model to receive an input and generate an output by performing generic steps such as using a graph to identify job-related tasks based on resume and job requirement data and comparing the job-related tasks using vectorization. Therefore, the claims are not directed to a practical application and do not amount to significantly more.
Therefore, the examiner maintains the current 101 rejection.
Applicant argues that claims 2-7, 9-14, and 16-20 are allowable as being dependent on claims 1, 8, and 15 and therefore are rejected under the same rejection.
Applicant’s arguments, see REMARKS, filed July 23, 2025, with respect to the rejections of Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crow (US 2005/0080656) in view of Brown (US 2006/0106638) further in view of Hardtke (US 2014/0122355) are not persuasive as the claims were amended which required further search and consideration and new art was applied.
Claims 1, 8, and 15: Applicant argues that the current prior art does not disclose the amended claim limitations. However, upon further search and consideration the examiner finds that the Hardtke is capable of being combined with the current prior art to disclose the newly amended claim limitations. Crow discloses a system of conceptualizing job candidate information such as a resume. The method includes extracting concepts relating to job skills and titles from elements of a resume and using a concept space to identify related concepts. Crow further discloses identifying concepts related to job criteria and matching the concepts for the job criteria and job candidate data to determine a degree of similarity between the two. Brown further teaches defining an ontology of tasks related to a set of skills and mapping various tasks to a plurality of skills. Hardtke can be further used in combination as Hardtke teaches calculating the overlap between a characteristics of a resume to a job description by using a machine learning model which generates a vector representation of the data and performing a cosine similarity function.
Therefore, the examiner finds that the combination of Crow, Brown, and Hardtke as capable of teaching the currently amended claimed limitations. Claims 1, 8, and 15 are newly rejected under U.S.C. 103.
Claims 2-6, 9-14, and 16-20 were argued as being allowable only as being dependent on claims 1, 8, and 15. Therefore, they are also rejected under the same rejection as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Michaels (US 2020/0193382) Employment resource system, method, and apparatus.
Fang (US 2017/0286914) Systems and methods to develop training set of data based on resume corpus.
Beason (US 2020/0134537) System and method for generating employment candidates.
Kundapur (US 2022/0366374) System, method, and computer program for identifying implied job skills for qualified talent profiles.
Bykov (US 2021/0065126) Job skill taxonomy.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30.
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/COREY RUSS/Primary Examiner, Art Unit 3629
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